├── IQR_screened_redox_homolumo.jsn ├── Molecular_data_pubchem.jsn ├── Obtain_smiles ├── Considered_smiles.smi ├── Considered_smiles_lumo.smi ├── Considered_smiles_set2.smi ├── Generated_unique_smiles.smi ├── get_final_structures.py └── validate_structures.py ├── Obtain_unique ├── Generated_unique_smiles.smi └── postprocess_smiles.py ├── README.md ├── Samsung Publication License.docx ├── anova.py ├── bayes_pubchem.py ├── deep_bayesian_inference.py ├── deep_learning.py ├── deep_neural_network.py ├── design_of_experiments.py ├── dnn_pubchem.py ├── dnn_redox_add_layer_500.py ├── dnn_trained.pkl ├── dnn_trained_redox.pkl ├── drawing_tools.py ├── process_smiles.py ├── pubchem_data.py ├── rbm_trained.pkl ├── train_dnn.py ├── train_rbm.py └── visualize_mol.py /Obtain_smiles/Considered_smiles.smi: -------------------------------------------------------------------------------- 1 | CC1=CC(=C(C=C1)C)S(=O)(=O)Cl 2 | C1=CC=C2C(C=C3(=N3)=C3C=CC=CC3=C(C=C3(=N3)C2=C1 3 | C1=CC(=NC(=C1)C(=O)Cl)C(=O)Cl 4 | CCN(CC)CC.C1=CC=C(C(=C1)[O-])[O-].C1=CC=C(C(=C1)[O-])[O-].C1=CC=C(C(=C1)[O-])[O-].[GeH4] 5 | CC(=CCC1=CC2=C(C=C1)NC=C2C3=C(C(=O)C(=C(C3=O)O)C4=CNC5=C4C=C(C=C5)CC=C(C)C)O)C 6 | C1=CC=C2C=C3C(=O(C()=C4C=C5C=C6C=CC=CC6=CC5=C(=O(C()C4=CC3=CC2=C1 7 | C1=CC(=N)C(=)=C2C=CC=C(=N)C(=)C2=C1 8 | C1=CC=C2C=CC=CC2=C1PC 9 | C1=CC=C2C(O=C(5)=CC=CC2=C(O=C(5)1 10 | C1=CC=C2C(C=NC2C4)=C3C=CC=CC3=C(C=NC2C4)C2=C1 11 | OON.[Cl-].[Cl-].[Pd] 12 | C1=CC(3C=C1=C0)=C2C=C3C=CC=C(3C=C1=C0)C3=CC2=C1 13 | C1=CC=C2C(=OC(=O3C)=C3C=C4C=C5C=CC=CC5=C(=OC(=O3C)C4=CC3=CC2=C1 14 | C1=CC=C2C(C=C2(=O3)=C3C=CC=CC3=C(C=C2(=O3)C2=C1 15 | CC1=COC2=C1C(=O)C3=C(C2=O)OC(=C(C)C)C3 16 | CN(C)CCN1C(=O)C2=C3C(=CC=C4C3=C(C=C2)C(=O)N(C4=O)CCN(C)C)C1=O 17 | CC(=O)C(=C(C(=O)O)Cl)ClF 18 | C1=CC(=C0=O)(CO)=C2C=C3C(N(CC1)=CC=C(=C0=O)(CO)C3=CC2=C(N(CC1)1 19 | COC1=CC(=O)C(=CC1=O)CO 20 | C1=CC(=C1NS(NC)=C2C(N)C(=C)=C3C=C4C=C5C=CC=C(=C1NS(NC)C5=C(N)C(=C)C4=CC3=CC2=C1 21 | C1=CC=C2C=C3C(SF(C(C))=C4C=C5C=CC=CC5=CC4=C(SF(C(C))C3=CC2=C1 22 | CCOC1=CC=C2C(=C1)N=CC(=N2)C(=O)O-C= 23 | CC(=C(Cl)Cl)(C(=O)Cl)ClC 24 | C1=CC=C2C(C5)C(O(CF)=C3C(F)C7)C(F)=CC=CC3=C(C5)C(O(CF)C2=C(F)C7)C(F)1 25 | C1=CC(2C=C1=C()=C2C=C3C=CC=C(2C=C1=C()C3=CC2=C1 26 | C1=CC(=C(C(=C(O)=C2C(S(=C)(O)=C3C=CC=C(=C(C(=C(O)C3=C(S(=C)(O)C2=C1 27 | C1=CC=C2C(=C1)C(=O)C3=C(C=C(C(=C3C2=O)N)C(=O)OCCCCCCO)O 28 | CCNCC1=CC=C(C=C1)C2=CC(=S)C3=CC=CC=C3O2=(FC 29 | CC1=CC(=O)C=C1(=O 30 | C1=CC=C2C(=C1)C3=C4C(=CC(=C3)NC5=CC=CC6=C5C(=O)C7=CC=CC=C7C6=O)C=CC=C4C2=O 31 | C1=CC=C2C(=C1)C=CC=C2S(=O)(=O)NC3=CC4=C(C5=CC=CC=C53)OC(=O)S4 32 | CCCCC=CC#CC#CC=CC=CC=CC=C1C=CC(=O)O1NCP 33 | C1=CC(=C(C(=C)N)=C2C(S(=C)(O)=C3C=CC=C(=C(C(=C)N)C3=C(S(=C)(O)C2=C1 34 | C1=CC=C2C=C3C(C4(C2C))=C4C=C5C=CC=CC5=CC4=C(C4(C2C))C3=CC2=C1 35 | C1=CC=C2C(C=C2(=N1)=C3C=CC=CC3=C(C=C2(=N1)C2=C1 36 | C1=CC=C2C(=OC(=N2C)=C3C=C4C=C5C=CC=CC5=C(=OC(=N2C)C4=CC3=CC2=C1 37 | C1=CC=C2C=C3C(S7CC3C4)=C4C(=C(=CC=OC)=C5C=C6C=CC=CC6=CC5=C(S7CC3C4)C4=C(=C(=CC=OC)C3=CC2=C1 38 | CC1=CC=C2C(=C1)C(=O)C(=C(C2=O)Cl)NCCOF 39 | C1=CC=C2C(=C1)C(=O)C(=O)N2CO 40 | C1=CC(=C2O1(NC)=C2C(N(2(=C)=C3C=C4C=C5C=CC=C(=C2O1(NC)C5=C(N(2(=C)C4=CC3=CC2=C1 41 | C1=CC(5C=O)=C2C=C3C=C4C=CC=C(5C=O)C4=CC3=CC2=C1 42 | CC1C=CCC2C1C(=O)C=CC2=OC 43 | CC1=CC=C2C=C3C=C4C=C5C=CC=CC5=CC4=CC3=CC2=C1S 44 | C1C[CH-][CH-]CC[CH-][CH-]1.[CH-]1[CH-][CH-][C-]([CH-]1)I.[Co] 45 | [O-]P(=O)([O-])OOP(=O)([O-])[O-] 46 | C1=CC=C2C=C3C=C4OCC5C=C6C=CC=CC6=CC5=CC4=CC3=CC2=C1 47 | COC1=CC(=O)C(=C(C1=O)CCCCCCCC=CCC=CCC=C)O 48 | C1C=CCC2=C1C(=O)C2=O 49 | C1=CC=C2C(N=C05)=CC=CC2=C(N=C05)1 50 | C1=CC(=N=NN=NC5)=C2C=C3C=CC=C(=N=NN=NC5)C3=CC2=C1 51 | CCCC1=CC=C2C=C3C=CC=CC3=CC2=C1OCC 52 | C1=CC=C2C(C3C1C(O(C)=C3C=C4C=C5C(=C3S)5)=C6C=CC=CC6=C(C3C1C(O(C)C5=CC4=CC3=C(=C3S)5)C2=C1 53 | C1=CC=C2C(C1(C)=C3C(=N34)=CC=CC3=C(C1(C)C2=C(=N34)1 54 | C1=CC(2C)C(=C1)=C2C=C3C=CC=C(2C)C(=C1)C3=CC2=C1 55 | C1=CC=C2C(O=CC5)=CC=CC2=C(O=CC5)1 56 | COC1=CC(=O)C(=CC1=O)C(C=C)C2=CC=CC=C2 57 | C1=CC(=S)(C5)=C2C=C3C=C4C=C5C=C6C=CC=C(=S)(C5)C6=CC5=CC4=CC3=CC2=C1 58 | CCC1=CC(=O)C=CC1=NC2=CC=C(C=C2)N(CCCl)CCClCC 59 | C1=CC(3C)C(=C1)=C2C=C3C=CC=C(3C)C(=C1)C3=CC2=C1 60 | C1=CC(=C(C(=O)O)=C2C(C(=C)(O)=C3C=CC=C(=C(C(=O)O)C3=C(C(=C)(O)C2=C1 61 | CC1=C(OC(=C1)N(O)O)C2=CSC(=N2)N=NC=OF 62 | C1=CC(2S=O)=C2C=C3C=C4C=CC=C(2S=O)C4=CC3=CC2=C1 63 | CCNOC1=CC=C2C(=C1)C(=C(C(=O)C2=O)Cl)O-=CC 64 | C1=CC=C2C=C3C(S4(S(C))=C4C=C5C=CC=CC5=CC4=C(S4(S(C))C3=CC2=C1 65 | CCC1=CC=C2C(=C1)C(=CC3=C2C4=CC=CC=C4C3=O)OCC 66 | C1=CC=C2C=C3C=C4C=C5C=C6C(5)OCC(O1)=CC=CC6=CC5=CC4=CC3=CC2=C(5)OCC(O1)1 67 | C1=CC=C2C(C=CC(0)C)=C3C=CC=CC3=C(C=CC(0)C)C2=C1 68 | C1=CC=C2C(=C1)C(=O)C(=C(C2=O)Cl)N=NC3=NC=C(C=C3)N(O)O 69 | C1=CC=C2C(C=(C3)=C3C=C4C=C5C=CC=CC5=C(C=(C3)C4=CC3=CC2=C1 70 | C1=CC=C2C(=C1)C(=O)C3=C(C2=O)CNC(C=C3)Cl 71 | [Pr] $ 72 | C1=CC=C2C(C(=C15)=CC=CC2=C(C(=C15)1 73 | C=CN=C1C=C(C2=C(C1=O)C=NC=C2)NCCOCC 74 | C1=CC(=C1(C(4)=C2C=C3C(N3(B)=CC=C(=C1(C(4)C3=CC2=C(N3(B)1 75 | C1=CC(=C(C(=O)N)=C2C(C(=C)(O)=C3C=CC=C(=C(C(=O)N)C3=C(C(=C)(O)C2=C1 76 | CC1=CC=C2C=C3C=C4C=C5C=CC=CC5=CC4=CC3=CC2=C1N 77 | COF(=O(C=OC1C2C1)N)C(=O)C3=C(C=CC(=C3C2=O)OC)N 78 | C1=CC(O0=C(=)=C2C=C3C=CC=C(O0=C(=)C3=CC2=C1 79 | C1=CC(=N=NO=NC5)=C2C=C3C=CC=C(=N=NO=NC5)C3=CC2=C1 80 | OCN.[Cl-].[Cl-].[Pd] 81 | CC1=CC2=C(C(=O)NC2=O)N=C1S 82 | C1=CC(=C1(C(3)=C2C=C3C(N3(2)=CC=C(=C1(C(3)C3=CC2=C(N3(2)1 83 | C1=CC=C2C=C3C=CC=CC3=CC2=C1 84 | C1=CC=C2C((=/C(=C0)=CC=CC2=C((=/C(=C0)1 85 | C1=CC=C2C(=C1)N=C3C(=N2)C(=O)OC3=O 86 | CC1=CC2=C(C(=C1)O)NC3=CC=CC(=O)C3=N2O 87 | C1=CC=C2C=C3C(=O)C()=C4C=C5C=C6C=CC=CC6=CC5=C(=O)C()C4=CC3=CC2=C1 88 | C1=CC=C2C(N=CC(=C3)=C3C=C4C=C5C=CC=CC5=C(N=CC(=C3)C4=CC3=CC2=C1 89 | C1=CC=C2C=C3C(5(5)O)=C4C=CC=CC4=CC3=C(5(5)O)C2=C1 90 | C1=C(NC(=O)C(=N)C1=O)ONCCO 91 | C1=CC=C2C(=OC(=O2C)=C3C=C4C=C5C=CC=CC5=C(=OC(=O2C)C4=CC3=CC2=C1 92 | C1=CC=C2C(C=C1(=O1)=C3C=CC=CC3=C(C=C1(=O1)C2=C1 93 | C1=CC=C2C=CC=CC2=C1.FO 94 | CC =CC(=O)C=CC=ON1CP 95 | C1=CC(=C2(C(4)=C2C=C3C(N3(B)=CC=C(=C2(C(4)C3=CC2=C(N3(B)1 96 | C1=CC=C2C(N=CC=NO)=CC=CC2=C(N=CC=NO)1 97 | C1=CC=C2C=C3C=C4C=CC=CC4=CC3=CC2=C1=FC 98 | C=C1=CC=C2C(=C1)N=CN=N2CF 99 | C1=CC=C2C=CC=CC2=C1C= 100 | C1=CC=C2C=C3C=C4C((C=N3)O)=C5C=CC=CC5=CC4=CC3=C((C=N3)O)C2=C1 101 | CC1=CC=C2C(=C1)C(=CC(=O)C2=O)NP 102 | C1=CC(=C1(C(1)=C2C=C3C(N3(2)=CC=C(=C1(C(1)C3=CC2=C(N3(2)1 103 | C1=CC=C2C(C=OC(=O3)=C3C=CC=CC3=C(C=OC(=O3)C2=C1 104 | C1=CC=C2C=C3C=C4C=C5C(O==C3=C(F)=CC=CC5=CC4=CC3=CC2=C(O==C3=C(F)1 105 | C1=CC(N1=C(=)=C2C=C3C=CC=C(N1=C(=)C3=CC2=C1 106 | C1=CC=C2C(1=CC(3O)=CC=CC2=C(1=CC(3O)1 107 | CC1=C(C(=N)C(=O)C=C1)N(O)O 108 | CC1=CC(=C2C(=C1)C(=O)C34C5C6C(C3C(=O)C78C6(C(C5OC)C(C)(C47C2=O)C(=O)C9=CC(=CC(=C9C8=O)O)C)OC)O 109 | C1=CC(=O)C(=)=C2C=CC=C(=O)C(=)C2=C1 110 | C1=CC(=C0=O)(CO)=C2C=C3C(O(CC1)=CC=C(=C0=O)(CO)C3=CC2=C(O(CC1)1 111 | C1=CC=C2C(=C1)C3=C4C(=CC=C5C4=C(C=C3)C(=O)C6=CC=CC=C65)C2=O 112 | C1=CC=C2C=C3C=C4C=C5C=C6C=CC=CC6=CC5=CC4=CC3=CC2=C1P 113 | C1=CC=C2C=C3C=C4C=C5C=C6C(5)OCp(N5)=CC=CC6=CC5=CC4=CC3=CC2=C(5)OCp(N5)1 114 | C1(=C(C(=O)C(=C(C1NC)Cl)O)Cl)O 115 | C1=CC(=O=NN=NC5)=C2C=C3C=CC=C(=O=NN=NC5)C3=CC2=C1 116 | CC1=CC(=C2C(=C1)C(=O)C34C5C6C(C3C(=O)C78C6(C(C5OC)C(S)(C47C2=O)C(=O)C9=CC(=CC(=C9C8=O)O)C)OC)O 117 | C1CCN(CC1)C2=CC=CC3=C2C(=O)C4=CC(=C3C(C4=S 118 | C1=CC(=C(C(=C(N)=C2C(S(=C)(O)=C3C=CC=C(=C(C(=C(N)C3=C(S(=C)(O)C2=C1 119 | CCCC1=CC=C2C=C3C=C4C=CC=CC4=CC3=CC2=C1=CC 120 | CCCCCC=CCCCCCCCCCC1=C(C(=O)C=C(C1=O)OC)O= 121 | CC1=CC=C(C(=C1)N=S=O)ClN 122 | C1=CC=C2C=CC=CC2=C1CC 123 | C1=CC=C2C=C3C=C4C=CC=CC4=CC3=CC2=C1ON 124 | CC1=C(C(=O)C2=C(C1=O)N3CC4C(C3(C2COC(=O)N)O)N4C)NC 125 | C1=CC=C2C=C3C(8CC(=O)C)=C4C=C5C=C6C=CC=CC6=CC5=C(8CC(=O)C)C4=CC3=CC2=C1 126 | C1=CC=C2C=C3C(C6)S3C()=C4C=C5C=CC=CC5=CC4=C(C6)S3C()C3=CC2=C1 127 | CCC1=C(C=C(C=C1C(=O)Cl)C(=O)Cl)C(=O)ClCN 128 | C1=CC=C2C(C3C5C(O(C)=C3C=C4C=C5C(=C3S)5)=C6C=CC=CC6=C(C3C5C(O(C)C5=CC4=CC3=C(=C3S)5)C2=C1 129 | C1=CSC(=C1)C=C2C(=O)NC(=O)NC2=O 130 | C1=CC=C2C=CC=CC2=C1CPC 131 | CC1C(C(CC(O1)OC2CC(CC3OC(C4=C(C(=C23)O)C(=O)C5=C(C4=O)C=CC=C5OC)O)(C(=O)C)O)NC(=O)C(CC(C)C)N)O 132 | C1=CC=C2C(3=CC(3O)=CC=CC2=C(3=CC(3O)1 133 | C1=CC(2C=C1=C0)=C2C=C3C=CC=C(2C=C1=C0)C3=CC2=C1 134 | C1=CC=C2C(C=C3(=O1)=C3C=CC=CC3=C(C=C3(=O1)C2=C1 135 | CC1=CC=C2C=C3C=CC=CC3=CC2=C1P 136 | C1=CC(C(=C)=C2C=C3C=C4C=C5C(N4C(N5)=CC=C(C(=C)C5=CC4=CC3=CC2=C(N4C(N5)1 137 | C1=CC(=CC=C1N=NC2=CC=C(C=C2)O)N=NC3=C(C=CC(=C3)S(=O)(=O)O)Cl 138 | C1=CC=C2C=C3C(5CC(=O)C)=C4C=C5C=C6C=CC=CC6=CC5=C(5CC(=O)C)C4=CC3=CC2=C1 139 | C(=C(C(=O)C=O)C(=O)O 140 | CC1=CC2=C(C=CC3=C2C(=C1)C(=O)OC3=O)Cl= 141 | C1=CC(2C=O)=C2C=C3C=C4C=CC=C(2C=O)C4=CC3=CC2=C1 142 | CC1=CC=C(C=C1)N=C(N=C2C=C(C=CC2=O)N(O)O)SC 143 | C1=CC(=O)O(NN)=C2C=C3C=CC=C(=O)O(NN)C3=CC2=C1 144 | C1=CC(3C=C3=C1)=C2C=C3C=CC=C(3C=C3=C1)C3=CC2=C1 145 | CC1=CC=C2C(=C1)C(=O)N(C2=O)C(=O)CCl= 146 | C1=CC=C2C=C3C(9CC(=O)C)=C4C=C5C=C6C=CC=CC6=CC5=C(9CC(=O)C)C4=CC3=CC2=C1 147 | C1=CC=C2C=CC=CC2=C1OFO 148 | C1=CC=C2C=C3C(=O(C(5)C)=C4C=C5C=CC=CC5=CC4=C(=O(C(5)C)C3=CC2=C1 149 | C1=CC=C2C=C3C(=N(C(5)C)=C4C=C5C=CC=CC5=CC4=C(=N(C(5)C)C3=CC2=C1 150 | C1=CC=C2C=C3C=C4C((C=O3)O)=C5C=CC=CC5=CC4=CC3=C((C=O3)O)C2=C1 151 | C1=CC(=C2O1(NC)=C2C(N(S(=C)=C3C=C4C=C5C=CC=C(=C2O1(NC)C5=C(N(S(=C)C4=CC3=CC2=C1 152 | CCN(C1=CC=C(C=C1)C=CC=C2C(=O)NC(=O)NC2=O)CCF 153 | C1=CC(C(=C)=C2C=C3C=C4C=C5C(N=C(N5)=CC=C(C(=C)C5=CC4=CC3=CC2=C(N=C(N5)1 154 | C1=CC=C2C=C3C(=C2O)=C4C(=C(C4=CC)=CC=CC4=CC3=C(=C2O)C2=C(=C(C4=CC)1 155 | CC(COC1=C(C=CC(=C1)N=NC2=CC=CC=C2)N=O=CO 156 | CCNC1=CC(=C(C2=C1C=CC3=C2C(=O)C(=O)C=C3)N)S(=O)(=O)O 157 | CC1C(=O)CC(=O)C1=O 158 | C1=CC=C2=21ON 3=CS(=S2()N=O 159 | C1=CC=C2C(C=N(O3)=C3C=CC=CC3=C(C=N(O3)C2=C1 160 | C1=CC(O2=C(=)=C2C=C3C=CC=C(O2=C(=)C3=CC2=C1 161 | C=C(C1=CC=C2C(=C1)C(=O)C(=CS2(=O)=O)CClp)CC 162 | C1=CC=C(C=C1)N=NC2=C3C=CC(=CC3=CC(=C2O)S(=O)(=O)O)S(=O)(=O)O 163 | C1=CC(=C(SI5)=C2C(=OC(2=C(C)=C3C=CC=C(=C(SI5)C3=C(=OC(2=C(C)C2=C1 164 | C1=CC=C2C=C3C=C4C=C5C=C6C=CC=CC6=CC5=CC4=CC3=CC2=C1O 165 | C1=CC(3C=C1=C()=C2C=C3C=CC=C(3C=C1=C()C3=CC2=C1 166 | C1=CC=C2C(C=(C1)=C3C=C4C=C5C=CC=CC5=C(C=(C1)C4=CC3=CC2=C1 167 | C1=CC(=C(S(=O)O)=C2C(C(=C)(O)=C3C=CC=C(=C(S(=O)O)C3=C(C(=C)(O)C2=C1 168 | C1=CC(=C=N2=N1)=C2C=C3C=CC=C(=C=N2=N1)C3=CC2=C1 169 | C1=CC=C2C=CC=CC2=C1= 170 | C1=CC=C2C(C=C1(=N1)=C3C=CC=CC3=C(C=C1(=N1)C2=C1 171 | CC1=CC=C2C=C3C=C4C=C5C=C6C=CC=CC6=CC5=CC4=CC3=CC2=C1C 172 | C1=C2C(=CC(=C1Cl)Cl)C(=O)OC2=O 173 | C1=CC=C2C(C=OC(=N1)=C3C=CC=CC3=C(C=OC(=N1)C2=C1 174 | C1=CC=C2C=C3C=CC=CC3=CC2=C1CP 175 | C1=CC=C2C=C3C(C1(S(C))=C4C=C5C=CC=CC5=CC4=C(C1(S(C))C3=CC2=C1 176 | C1=CC(2C=C3=C1)=C2C=C3C=CC=C(2C=C3=C1)C3=CC2=C1 177 | C1C2C1C(=O)C(=O)C2=O 178 | C1=CC=C2C=C3C(NC(S(C))=C4C=C5C=CC=CC5=CC4=C(NC(S(C))C3=CC2=C1 179 | C1=CC=C2C=C3C(C5(C(C))=C4C=C5C=CC=CC5=CC4=C(C5(C(C))C3=CC2=C1 180 | C1=CC=C2C(C1(C)=C3C(=O24)=CC=CC3=C(C1(C)C2=C(=O24)1 181 | C1=CC=C2C=C3C=C4C=C5C=C6C=CC=CC6=CC5=CC4=CC3=CC2=C1C 182 | C1=CC(=C1=O)(CN)=C2C=C3C(N(CC1)=CC=C(=C1=O)(CN)C3=CC2=C(N(CC1)1 183 | CC1=CC=C2C=C3C=C4C=C5C=C6C=CC=CC6=CC5=CC4=CC3=CC2=C1N 184 | C1=CC=C2C(7=(C5)=C3C=C4C=C5C=CC=CC5=C(7=(C5)C4=CC3=CC2=C1 185 | C1=CC=C2C(C=NC(=N1)=C3C=CC=CC3=C(C=NC(=N1)C2=C1 186 | C1=CC(=C=N2=O)=C2C=C3C=CC=C(=C=N2=O)C3=CC2=C1 187 | C1=CC(2C=N)=C2C=C3C=C4C=CC=C(2C=N)C4=CC3=CC2=C1 188 | C1=CC=C2C(C5(C)=C3C(=N34)=CC=CC3=C(C5(C)C2=C(=N34)1 189 | C1=CC=C2C(C5(C)=C3C(=O24)=CC=CC3=C(C5(C)C2=C(=O24)1 190 | C1=CC=C2C(C=C2(=O1)=C3C=CC=CC3=C(C=C2(=O1)C2=C1 191 | C1=CC(=C(SI=)=C2C(=OC(N=C(C)=C3C=CC=C(=C(SI=)C3=C(=OC(N=C(C)C2=C1 192 | C1=CC=C2C(C=CC(p)C)=C3C=CC=CC3=C(C=CC(p)C)C2=C1 193 | C1=CC=C2C(C1(S)=C3C(=O24)=CC=CC3=C(C1(S)C2=C(=O24)1 194 | C1=CC(OS=C(=)=C2C=C3C=CC=C(OS=C(=)C3=CC2=C1 195 | C1=CC=C2C=C3C(C4(S1C))=C4C=C5C=CC=CC5=CC4=C(C4(S1C))C3=CC2=C1 196 | C1=CC(=C)(C=)=C2C=C3C=C4C=C5C=C6C=CC=C(=C)(C=)C6=CC5=CC4=CC3=CC2=C1 197 | C(=O)O.C(=O)O.C(=O)O.C(=O)O.[Mo]#[Mo] 198 | C1=CC=C2C=C3C(C4(C2C()=C4C=C5C=CC=CC5=CC4=C(C4(C2C()C3=CC2=C1 199 | C1=CC=C2C((=.C)=C1)=CC=CC2=C((=.C)=C1)1 200 | C1=CC(2C=C1=C1)=C2C=C3C=CC=C(2C=C1=C1)C3=CC2=C1 201 | CC1=C2C(=CC3=C1C(=O)C4=CC=CC=C4C3=O)C5=CC=CC=C5N2C 202 | C1=CC=C2C=C3C(S7CC3C4)=C4C(=C(=CC=NC)=C5C=C6C=CC=CC6=CC5=C(S7CC3C4)C4=C(=C(=CC=NC)C3=CC2=C1 203 | C(=C(C(=O)C=F)C(=O)O 204 | C1=CC=C2C=C3C(5(4)N)=C4C=CC=CC4=CC3=C(5(4)N)C2=C1 205 | C1=CC=C(C=C1)NS(=O)(=O)C2=CC=C(C=C2)[As](=O)C*=O 206 | C1=C(C=C(C=C1C(=O)Cl)C(=O)(N)C(=O)Cl 207 | CC1=C(C(=O)C2=C(C1=O)N3CC4C(C3(C2COC(=O)N)OC)N4C(=O)C5=CC=CC=C5Cl)N 208 | C1=CC(=C1(C(1)=C2C=C3C(N3(B)=CC=C(=C1(C(1)C3=CC2=C(N3(B)1 209 | CCCCCCCCCCCCCCC#CC#CC#CC#CC(=O)O/OC 210 | C1=CC=C2C=C3C(C4(S(C))=C4C=C5C=CC=CC5=CC4=C(C4(S(C))C3=CC2=C1 211 | CCCN(CC)C1=CC=C(C=C1)C(=C(C#N)C#N)C#NC 212 | CCCOC(=O)C1=CC=C(C=C1)N=CC2=C(C(=CC=C2)O)O 213 | C1=CC(=C2OS(NC)=C2C(N(S(=C)=C3C=C4C=C5C=CC=C(=C2OS(NC)C5=C(N(S(=C)C4=CC3=CC2=C1 214 | C1=CC(=C2=C1C1C(C=C(C2=O)S(=O)(=O)O)N([O-])[O-])C(=O)C=C1 215 | C1=CC=C2C=C3C=C4C=C5C(N5=C3=C(F)=CC=CC5=CC4=CC3=CC2=C(N5=C3=C(F)1 216 | C1=CSC2=C1C(=O)C3=C(C2=O)SC(=(3)C=O 217 | C1=CC=C2C=C3C(5(4)F)=C4C=CC=CC4=CC3=C(5(4)F)C2=C1 218 | C1=CC=C2C(=OC(=N3C)=C3C=C4C=C5C=CC=CC5=C(=OC(=N3C)C4=CC3=CC2=C1 219 | C1=CCC(=C1)C=C2C(=O)NC(=O)NC2=O 220 | C1=CC=C2C(=O1((=O3C)=C3C=C4C=CC=CC4=C(=O1((=O3C)C3=CC2=C1 221 | C1CCN=C1C=C(C2=C(C1=O)C=NC=C2)NCCO1=C 222 | C1=CC=C2C(C=N1)=C3C=CC=CC3=C(C=N1)C2=C1 223 | CCCCCCC1=CC2=C(C=C1)C(=O)C3=CC=CC=C3C2=OS 224 | C1=CC(=C)(C5)=C2C=C3C=C4C=C5C=C6C=CC=C(=C)(C5)C6=CC5=CC4=CC3=CC2=C1 225 | COC1=C(C(=CC(=C1)C(C2=C(C3=CC=CC=C3C(=O)C2=O)O)C4=C(C5=CC=CC=C5C(=O)C4=O)O)Br)O 226 | C1=CC(2C=N2=C()=C2C=C3C=CC=C(2C=N2=C()C3=CC2=C1 227 | C1=CC=C(C=C1)NS(=O)(=O)C2=CC=C(C=C2)[As](=O)C(=O 228 | C1=CC=C2C(=OC(=C3C)=C3C=C4C=C5C=CC=CC5=C(=OC(=C3C)C4=CC3=CC2=C1 229 | CNN(CC)CC.C1=CC=C(C(=C1)[O-])[O-].C1=CC=C(C(=C1)[O-])[O-].C1=CC=C(C(=C1)[O-])[O-].[GeH4] 230 | C1=CC(sC=N)=C2C=C3C=C4C=CC=C(sC=N)C4=CC3=CC2=C1 231 | CC1=CC(=C(C=C1N2C(=O)C=CC2=O)O)OO 232 | C1=CC=C2C=C3C(C6(C2C))=C4C=C5C=CC=CC5=CC4=C(C6(C2C))C3=CC2=C1 233 | CC1=C(C(=C(N=C1C(=O)N)C2=NC3=C(C=C2)C(=O)C(=C(C3=O)N)OC)N)C4=C(C(=C(C=C4)OC)OC)O 234 | C1=CC(=C1)C(4)=C2C=C3C(N3(B)=CC=C(=C1)C(4)C3=CC2=C(N3(B)1 235 | C1=CC=C2C((=)C(=C1)=CC=CC2=C((=)C(=C1)1 236 | CC1=CC=C2C(=C1)C(=CC=N2)C=OC 237 | CC1=CC=C2C=C3C=C4C=C5C=CC=CC5=CC4=CC3=CC2=C1C 238 | C1=CC(=C=O)=C2C=C3C=C4C=CC=C(=C=O)C4=CC3=CC2=C1 239 | C1=CC=C2C(C=CC(=O1)=C3C=CC=CC3=C(C=CC(=O1)C2=C1 240 | C(=C(C(=O(C=O)C(=O)O 241 | CCOCC1CCC(C2=C1C(=O)C(=O)C(=C2)C)C(C)CCOO 242 | [Pr] & 243 | C1=CC=C2C=C3C(C3)S3C()=C4C=C5C=CC=CC5=CC4=C(C3)S3C()C3=CC2=C1 244 | C1=CC=C2C=C3C=C4C=C5C=C6C=CC=CC6=CC5=CC4=CC3=CC2=C1CP= 245 | C1=CC=C2C=C3C=C4C=C5C=CC=CC5=CC4=CC3=CC2=C1=CC 246 | C1=CC(=C=OC1Cn)#C#N 247 | C1=CC(=CC=C1N=NC2=C(C3=C(C=C(C=C3C=C2S(=O)(=O)O)S(=O)(=O)O)O)O)S(=O)(=O)O 248 | C1(=O)C(C(=O)NC(=O)N1)(Br)Br 249 | CC1=CC(=C(C=C1Cl)C=C(C#N)C#N)OC 250 | C1=CC=C2C=C3C=C4C=C5C=CC=CC5=CC4=CC3=CC2=C1 251 | C1=CC=C2C(7=(C3)=C3C=C4C=C5C=CC=CC5=C(7=(C3)C4=CC3=CC2=C1 252 | C1=CC(1C)C(=C1)=C2C=C3C=CC=C(1C)C(=C1)C3=CC2=C1 253 | C1=CC=C2C=C3C=C4C=C5C=C6C(5)OCC(N5)=CC=CC6=CC5=CC4=CC3=CC2=C(5)OCC(N5)1 254 | C1=CC2=C(C(=O)C(=C(C2=O)N)Cl)N=C1 255 | C1=CC2=C(C(=C1)Cl)C3=C(C=C2)C(=C(S3)C4=C(C5=C(C4(C=CC(=C5)Br)OS(=O)(=O)O)OS(=O)(=O)O 256 | C1=CC(O1=C(=)=C2C=C3C=CC=C(O1=C(=)C3=CC2=C1 257 | C1=CC=C2C=C3C(C4(S2C9)=C4C=C5C=CC=CC5=CC4=C(C4(S2C9)C3=CC2=C1 258 | C1=CC2=C3C(=NC(=NN3)NN=CC4=CC=NC=C4)N=C2C=C441 259 | C1=CC=C2C=C(C=CC2=C1)S(=O)(=O)NN3C=NC4=C(C3=O)C=C(C=C4)S(=O)(=O)NC5=C(C=CC=C5Cl)Cl 260 | C1=CC=C2C((=.C(=C1)=CC=CC2=C((=.C(=C1)1 261 | CC1=C(C(=O)C2=C(C1=O)C=CN=C2CNC(=O)C(=O)C)OC 262 | C1=CC(=C(C(=O(O)=C2C(C(=C)(O)=C3C=CC=C(=C(C(=O(O)C3=C(C(=C)(O)C2=C1 263 | C1=CC(=C(C)=C2C(C(1O)O)=C3C(C(=C)NS)=C4C=C5C=CC=C(=C(C)C5=C(C(1O)O)C4=C(C(=C)NS)C3=CC2=C1 264 | C1=CC(=C2C(=C1Cl)C(=O)C3=C(O)(C(=C3C2=O)Br)Br)Cl 265 | C1=CC(=C((C5)=C2C=C3C=C4C=C5C=C6C=CC=C(=C((C5)C6=CC5=CC4=CC3=CC2=C1 266 | C(C(C1C(=O)C(=O)C(=O)O1)O)O 267 | C1=CC=C2C((=-C(=C0)=CC=CC2=C((=-C(=C0)1 268 | C1=CC(2S=N)=C2C=C3C=C4C=CC=C(2S=N)C4=CC3=CC2=C1 269 | C1=CC=C2C(N=C25)=CC=CC2=C(N=C25)1 270 | C1=CC=C2C(N=C15)=CC=CC2=C(N=C15)1 271 | 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C1=CC(=C(=N1)=C2C(=C2=C4)=C3C(C=C6)F)=C4C=CC=C(=C(=N1)C4=C(=C2=C4)C3=C(C=C6)F)C2=C1 76 | C1=CC=C(C=C1=N2C(=O)C3=C(C2=O)SC4=NN=CC(=O)N34 77 | CC1=CC(=O)C=CC1=NC2=CC(=C(C(=C2)Cl)O)ClC 78 | C1=CC=C2C=C3C=CC=CC3=CC2=C1C 79 | CCOC1=CCC(CC(=C1)/C=C\2/C(=O)NC(=S)N(C2=O)C3=CC=CC=C3)O[C@H](C)C(=O)O 80 | C1=CC2=C(C(=O)C(=C(C2=O)Cl)Cl)N=C1 81 | C1=CC(=C(C)=C2C(N(3O)OC4)=C3C=C4C=CC=C(=C(C)C4=C(N(3O)OC4)C3=CC2=C1 82 | CCCOC1=C(C=CC(=C1)N=NC2=CC(=CC=C2)S(=O)(=O)O)N=NC3=CN(N=C3C(=O)O)C4=CC(=CC=C4)NC= 83 | C1=CC=C2C(C1C(=CC)=C3C=C4C=CC=CC4=C(C1C(=CC)C3=CC2=C1 84 | CCSOCCN(CC)C1=CC2=C(C=C1)C(=C3C=CC(=[N+](CC)CC)C=C3O2)C4=C(C=C(C=C4)S(=O)(=O)Cl)S(=O)(=O)[O-]C(=F 85 | C1=CC=C2C=C3C=C4C=C5C=C6C(C=N2C)=CC=CC6=CC5=CC4=CC3=CC2=C(C=N2C)1 86 | CC(C1=CC=C2C(=C1)C(=O)C=C(C2=O)NC(CO)(CO)CO(CO 87 | CNN(CCC(=O)OC1=C(C=C(C=C1)C=C[N+](=O)[O-])OC(=NP 88 | CC(=O)C(=O)C(=O)N(C1=CC=CC=C1)N(C)N=O.O 89 | CSC1=CC=C(C=C1)C(=NC2=CC=C(C=C2)[N+](=O)[O-])NCC 90 | CCN(C1=CC2=[N+](C=CC(=C2C=C1C(=O)O)[N+](=O)[O-])[O-]C)CC 91 | CCC(=O)NC1=C(C(=O)C2=CC=CC=C2C1=O)ClF 92 | C1=CC=C2C(C=C4O3)=C3C=C4C=CC=CC4=C(C=C4O3)C3=CC2=C1 93 | C1=CC=C(C=C1)SC2=CC(=O)C3=C(N=C=C3C2=O 94 | CC1=C2C=C(C=C(C2=C(C=C1S(=O)(=O)O)O)S(=O)(=O)O)S(=O)(=O)OC 95 | CC(=C)S(=O)(=N=C1)C(=CC=C1 96 | CC1C2=C(C=CC(=C2)[N+](=O)[O-])C3=C1C=C(C=C3)F= 97 | C1=CCN=F1NC 2/C3C@@NHF(C(CC=C3)[N+](=O)[O-] 98 | C1=CC=C2C(N=N1)=CC=CC2=C(N=N1)1 99 | C1=CC(=C2C(=C1)OC3=CC(=O)C(=C(C3=N2)C(=O)O)N)CO 100 | C1=CC(C(1)N-)=C2C=C3C(=(C4)OC)=CC=C(C(1)N-)C3=CC2=C(=(C4)OC)1 101 | CC(=CC(=O)O)C(=O)NC(=O)NC 102 | C1=CC=C2C(C=\0)3C(F)=CC=CC2=C(C=\0)3C(F)1 103 | C1=CC(=C(OO)=C2C=C3C=C4C(CCS0=CC)=CC=C(=C(OO)C4=CC3=CC2=C(CCS0=CC)1 104 | CFCC(C)CC(=O)OCC1(C(=O)OC2(N1C(=O)C3=C(C2)C=C4C=CC5=C(C4=C3)C(=O)C6=C(C5=O)OC7=CC(=C(C=C7C6=O)O)OC)C 105 | C1=CC=C2C(N=C(=O3=C)=C3C=C4C(=O)=C4)=CC=CC4=C(N=C(=O3=C)C3=CC2=C(=O)=C4)1 106 | CN(C1=CC=C2C(=C1)C(=CC=C3C4=C(C=C(C=C4)Cl)C(=O)C5=CC=CC=C53)C6=C(C2=O)C=C(C=C6)Cl)ON 107 | C=C1=CC(=CC=C1C(=O)OO)[N+](=O)[O-]CC 108 | CCC1=CC=C(C=C1)N=C(N=C2C=C(C=CC2=O)N(O)O)SCC 109 | C1=CC=C2C(C=N1)=C3C(=C(=C.N)=CC=CC3=C(C=N1)C2=C(=C(=C.N)1 110 | CC1=CC=C(C=C1)C2=CC3=C(C=C(C=C3)N=NC4=C(C=CC5=CC=CC=C54)N)OC2=OF 111 | C1=CC=C2C(C=C(C)=C3C(N=2C((O)=CC=CC3=C(C=C(C)C2=C(N=2C((O)1 112 | C1=CC(C2=CC)=C2C=C3C=C4C=C5C=C6C=CC=C(C2=CC)C6=CC5=CC4=CC3=CC2=C1 113 | CCCC1(C(N2C(S1)C(C2=O)NC(=O)C3=NC4=CC=CC=C4N=C3C(=O)O)C(=O)O)CFP 114 | C1=CC=C2C=C3C(=CC(3C)=C4C(=O)(C)C6C)=C5C=CC=CC5=CC4=C(=CC(3C)C3=C(=O)(C)C6C)C2=C1 115 | CCC1=C[N+](=O)C2=CC=CC=C2N1[O-]N 116 | C1=CC=C2C(N=C(=O3=C)=C3C=C4C(=O(=C4)=CC=CC4=C(N=C(=O3=C)C3=CC2=C(=O(=C4)1 117 | C1=CC=C2C(=C1)C(=O)C3=C(C2=O)C(=C(C=C3NC4=CC(=C(C=C4)Cl)S(=C)(=O)C(S(=O)(=O)O)N 118 | CSC1=CC(=CC(=C1)[N+](=O)[O-])C2=CC(=CC=C2)[N+](=O)[O-]CC 119 | C=C1=C(C=C(C(=C1[N+](=O)[O-])O)[N+](=O)[O-])ClCC 120 | C1=CC(=CC=C1N=NC2=CC=C(C=C2)S(=O)(=O)O)N=NC3=C(C=C4C=C(C=CC4=C3O)N)S(=O)(=O)O.N.N 121 | C1=CC(=C(OO)=C2C=C3C=C4C(CCC0=CC)=CC=C(=C(OO)C4=CC3=CC2=C(CCC0=CC)1 122 | C1=CC=C2C(=C1)C(=O)C3=C(C2=O)C(=C(C=C3NC4=CC(=C(C=C4)S(=OC(=O)O)N)S(=O)(=O)O)N 123 | C1=CC=C2C(=OC()=C3C=C4C=CC=CC4=C(=OC()C3=CC2=C1 124 | CCC(C1CN(CCN1)CC2=CC=C(C=C2)[N+](=O)[O-])=CC 125 | C1=CC(=C(C)=C2C(N(1O)OC4)=C3C=C4C=CC=C(=C(C)C4=C(N(1O)OC4)C3=CC2=C1 126 | CCC(=O)OC1=CC=CC=C1[N+](=O)[O-]P 127 | C1=CC(=C1(O=OC)=C2C(=NC(9C)=C3C=C4C(2=OS)C4)=CC=C(=C1(O=OC)C4=C(=NC(9C)C3=CC2=C(2=OS)C4)1 128 | C1=CC=C2C=C3C=C4C=C5C(N=C(N=O3)=CC=CC5=CC4=CC3=CC2=C(N=C(N=O3)1 129 | CCOC1=C(C=CC(=C1)C=C[N+](=O)[O-])OCC2=CC=CC=C2S 130 | CC1=CC=C(2=NC(C3C1=C=C(=NC=C(C=C3)C)O)C(=O)C(=O)N2C4=NN=CS4 131 | C1=CC(=CC=C1C(=O)Cl)S(=O)(=O)F 132 | CCC1=CC(=CC=C1[N+](=O)[O-])SC2=NC(=NC(=N2)C(Cl)(Cl)Cl)C(Cl)(Cl)ClCF 133 | C1=CC=C2C=C3C=C4C=CC=CC4=CC3=CC2=C1#OC 134 | CC1=NSC2=C1C(=O)C(=O)C3=C2C=CC4=C3CCCC4(C)O 135 | C1=CC=C2C=C3C(C3CN=C1)=C4C=C5C(=O2=C2)=C6C=CC=CC6=CC5=C(C3CN=C1)C4=CC3=C(=O2=C2)C2=C1 136 | C1=CC=C2C=C3C=C4C(3C(C46C2)=C5C=CC=CC5=CC4=CC3=C(3C(C46C2)C2=C1 137 | C1=CC(=CC=C1N=O)N=O 138 | C1=CC(2=C(C=C2N)=C2C=C3C(O)OO)=CC=C(2=C(C=C2N)C3=CC2=C(O)OO)1 139 | COC1=CC(=O)C(=CC1=O)N(CCCl)CCCl 140 | CC1=CC(=C2C(=C1)C(=O)C3=CC(=C(C(=C3C2=O)O)O)OC)O 141 | C1=CC=C2C=C3C(C3CF=C4)=C4C=C5C(=C2=C2)=C6C=CC=CC6=CC5=C(C3CF=C4)C4=CC3=C(=C2=C2)C2=C1 142 | C1=CC=C2C(C$2C5)=C3C=C4C=C5C=C6C=CC=CC6=C(C$2C5)C5=CC4=CC3=CC2=C1 143 | C1=CC=C2C=C3C=C4C=C5C=C6C=CC=CC6=CC5=CC4=CC3=CC2=C1C 144 | CCC1CC(=O)N(C1=O)OC(=O)C2=CC(=CC=C2)N3C(=O)C=CC3=OOS 145 | C1=CC=C2C(=C1)C(=O)C3=C(C2=O)C(=C(C=C3NC4=CC(=C(C=C4)Cl)S(=O)(=O)C(S(=O)(=O)O)N 146 | C1=CC=C2C=C3C(=NN3)=CC=CC3=CC2=C(=NN3)1 147 | C1=CC(=N1C((O)=C2C=C3C=C4C(C3=C)C==C)=CC=C(=N1C((O)C4=CC3=CC2=C(C3=C)C==C)1 148 | C1=CC=C2C(C=C(2)=C3C(N=2C((O)=CC=CC3=C(C=C(2)C2=C(N=2C((O)1 149 | CCCOC1=CC(=C(C=C1)SSC2=C(C=C(C=C2)OC)[N+](=O)[O-])[N+](=O)[O-]NC 150 | CCCCCOC1=C(C(=O)C2=C(C1=O)C(C3(N2CC4C3N4)OC)COC(=O)N)C 151 | CC1=CC(=C(C=C1)N=NC2=C3C=CC(=CC3=CC(=C2O)S(=O)(=O)O)S(=O)(=O)O)C 152 | C1=CC=C(C=C1)N2C(=O)C3=C(C2=O)SC4=NN=CC(=O)N34 153 | C1=CC(=CC=C1N2C(=O)C(=C(N2)C(=O)O)C=CC=CC=C3C(=NN(C3=O)C4=CC=C(C=C4)S(=O)(=O)O)C(=O)O)S(=O)(=O)O 154 | C1=CC=C2C(=C1)C(=O)N(C2=O)C3=NNN=N3 155 | C1=CC=C2C=C3C(C(=O)C)=C4C=C5C=CC=CC5=CC4=C(C(=O)C)C3=CC2=C1 156 | C1=CC(5-=NC)=C2C=CC=C(5-=NC)C2=C1 157 | C1=CC=C2C(=C1)C=C(C(=O)O2)C(=O)NC3=CC4=NN(N=C4C=C3)C5=CC=C(C=C5)S 158 | CNCC1=C(C(=O)C2=CC=CC=C2C1=O)CC=C(C)CCCC(C)CCCC(C)CCCC(C)C 159 | C1=CC(=C(C3F5)=C2C(=CC3)=C3C=C4C(C)C3=CC)=C5C=C6C=CC=C(=C(C3F5)C6=C(=CC3)C5=CC4=C(C)C3=CC)C3=CC2=C1 160 | C1=CC=C2C(C=N(=C(C3)=CC=CC2=C(C=N(=C(C3)1 161 | C1=CC=C2C(C=N1)=C3C(=C(=C4O)=CC=CC3=C(C=N1)C2=C(=C(=C4O)1 162 | C1=CC=C2C=CC=CC2=C1PC 163 | C1=C(C=C2C(=C1Br)N=C(N2)C F)(F)F)[N+](=O)[O-] 164 | CCC1=CC=C2C(=C1)C3=C4C(=C(C=C3)SCC(=O)O)C=CC=C4C2=OON 165 | CCN=CC(CNC1=C2C(=C(C=C1)O)C(=O)C3=CC=CC=C3C2=O)C#N=NC= 166 | CCN-CC(C)(C)C1=CC2=C(C=C1)C(=O)C3=CC=CC=C3C2=O (FN 167 | CCNC1=CC=C2C(=C1)C3=C4C(=C5C=CC=C6C5=C3C7=CC=CC=C7C6=O)C=CC=C4C2=O(SF 168 | CC(C1CCC(CC1)CCCCCCCCC2=C(C3=CC=CC=C3C(=O)C2=O)O(CS 169 | 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CCCNCNC1=CC=CC2=C1C(=O)C3=C(C2=O)C(=CC=C3)NCN=CP 539 | C=C(C1CN1C2=CC(=O)C(=O)C3=CC=CC=C32)CCC 540 | CCNCC1=CC=C(C=C1)S(=O)(=O)N(C)CC2=CC=C(C=C2)NC3=CC(=C(C4=C3C(=O)C5=CC=CC=C5C4=O)N)S(=O)(=O)O(CC 541 | C1=CC=C2C=C3C(C3CN=C3)=C4C=C5C(=N3=C2)=C6C=CC=CC6=CC5=C(C3CN=C3)C4=CC3=C(=N3=C2)C2=C1 542 | C1=CC(2=C(C=C3N)=C2C=C3C(N)OO)=CC=C(2=C(C=C3N)C3=CC2=C(N)OO)1 543 | C1=CC=C2C=CC=CC2=C1NP 544 | C1=CC=C2C(=C1)C(=C(C(=O)C2=O)C=C(Cl)Cl)O 545 | CCN(CC1=CC=CC=C1Cl)C2=CC(=C(C=C2)N=NC3=NC4=C(S3)C=C(C=C4)S(=O)(=O)O)C 546 | CCCC(C)N(C1C(C(C(C(O1)CO)OC2C(C(C(C(O2)CO)O)O)O)O)O)C(=O)N(CCCl)N=OC 547 | C1=CC(=C(=)C(C1)=C2C=C3C=C4C=C5C=C6C=CC=C(=C(=)C(C1)C6=CC5=CC4=CC3=CC2=C1 548 | CC1=C(C=CC(=C1)C2=CC(=C(C=C2)N=NC3=CC(=C(C=C3)O)C(=O)O)N[O-](4C(=NN(C4=O)C5=CC=C(C=C5)S(=O)(=O)O)C 549 | C1=CC=C2C((=C(=N)3)=CC=CC2=C((=C(=N)3)1 550 | CC1=CC=C(C=C1)N2C(=O)C3=C(C2=O)SC4=NC(=O)C(=NN34)C5=CC=CC=C5 551 | C1=CC=C2C(N3C4C3N(C)=C3C(CO)C)=C4C(=C()SC)=C5C=CC=CC5=C(N3C4C3N(C)C4=C(CO)C)C3=C(=C()SC)C2=C1 552 | 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C1=CC(=C(C)=C2C(F(3O)OC4)=C3C=C4C=CC=C(=C(C)C4=C(F(3O)OC4)C3=CC2=C1 568 | C1=CC=C2C(C=N()=CC=CC2=C(C=N()1 569 | CCN1C2=CC=CC=C2C=C3C1=NC(=O)N(C3=O)CN 570 | CCCCCNC(=O)OCCN(CC)C1=CC=C(C=C1)N=NC2=C3C=C(C=CC3=NS2)[N+](=O)[O-]F 571 | CCC1=CC(=CC=C1C2=CC=C(C=C2)C(=O)Cl)C(=O)ClNS 572 | CSCC12CCC3C(C1CCC2=O)CCC4=C3C=CC(=C4[N+](=O)[O-])OCNO 573 | CC((C1=CC=C2C=C3C=C4C=C5C=C6C=CC=CC6=CC5=CC4=CC3=CC2=C1=(NC 574 | CCC1C(C(CC(O1)OC2CC(CC3=C(C4=C(C(=C23)O)C(=O)C5=C(C4=O)C=CC=C5OC)O)(C(C)O)O)N6CCOCC6)OC 575 | CCCNCNC1=CC=CC2=C1C(=O)C3=C(C2=O)C(=CC=C3)NCl=CP 576 | CCONCC1=C(C=CC(=C1)OP(=S)(OC)OC)N=OC(CO 577 | COC1=CC(=O)C2=C(C1=O)C(=C(C(=C2O)OC)OC)O 578 | C1=CC2=C(C(=C1)CNC3=CC=C(C=C3)S(=O)(=O)C4)S(=C(C2=O)C=CC(=C4)Cl 579 | C1=CC=C2C=C3C=C4C=C5C=C6C(OO4=C4)=CC=CC6=CC5=CC4=CC3=CC2=C(OO4=C4)1 580 | C1=CC=C2C(=C1)C=CC(=C2N=NC3=CC=C(C=C3)N=NC4=CC=C(N=C4=S(=O)(=O)O)O 581 | CCCOC1=CC=C(C=C1)N=NC2=CC(=C(C=C2)SC3=C(C=C(C=C3)N=NC4=CC=C(C=C4)OCC)S(=O)(=O)O)S(=O)(=O)OP 582 | C1=CC=C2C=C3C=C4C=C5C=CC=CC5=CC4=CC3=CC2=C1=FC 583 | C1=CC(C2=C)=C2C(5)C(C=C(F)=C3C(=C2=CC)=C4C=CC=C(C2=C)C4=C(5)C(C=C(F)C3=C(=C2=CC)C2=C1 584 | C1=CC=C2C=C3C=C4C=C5C=CC=CC5=CC4=CC3=CC2=C1S= 585 | C1=CC=C2C(3=C(=O)3)=CC=CC2=C(3=C(=O)3)1 586 | C1=CC=C2C(C=N1)=C3C(=C(=C/N)=CC=CC3=C(C=N1)C2=C(=C(=C/N)1 587 | CCCC12CCC3C(C1CC(=O)C2=O)CCC4=C3C=CC(=C4)O=C 588 | C1=CC(=C(=O)=C2C=C3C=C4C=C5C((C(N)CC)=CC=C(=C(=O)C5=CC4=CC3=CC2=C((C(N)CC)1 589 | C1=CC(C(=O)=C2C=CC=C(C(=O)C2=C1 590 | C1=CC=C2C(N=CC(2=C)=C3C=C4C(=Cl=C4OC4)=C5C=C6C=CC=CC6=C(N=CC(2=C)C5=CC4=C(=Cl=C4OC4)C3=CC2=C1 591 | C1=CC=C2C=C3C((=O)C3=C)=C4C=C5C=CC=CC5=CC4=C((=O)C3=C)C3=CC2=C1 592 | C1=CC2=C(C=C1OC(F)(F)F)C(=O)C3=NC4=C(C=NC=C4)C(=O)N23 593 | C1=CC=C2C(N=CC(=C)3)=C3C=C4C=C5C=CC=CC5=C(N=CC(=C)3)C4=CC3=CC2=C1 594 | C1=CC=C2C=C3C(C3C3=C1)=C4C=C5C(=N3=C2)=C6C=CC=CC6=CC5=C(C3C3=C1)C4=CC3=C(=N3=C2)C2=C1 595 | C1=CC=C2C=C3C(C=O5)=C4C=C5C=CC=CC5=CC4=C(C=O5)C3=CC2=C1 596 | C1=CC=C2C(C=N0)=C3C(=S(=C4O)=CC=CC3=C(C=N0)C2=C(=S(=C4O)1 597 | CCCCCOC1=C(C(=O)C2=C(C1=O)C(C3(N2CC4C3N4)OC)COC(=O)N)C(SN 598 | C1=CC(=C1NO)=C2C=C3C=C4C(CCC49CC)=CC=C(=C1NO)C4=CC3=CC2=C(CCC49CC)1 599 | C1=CC=C2C=C3C(=C(=C)=C4C=C5C(C5)C4)=C6C=CC=CC6=CC5=C(=C(=C)C4=CC3=C(C5)C4)C2=C1 600 | C1=CC(C2=C3=N)=C2C(=C4=C6)=C3C=C4C=C5C=C6C=CC=C(C2=C3=N)C6=C(=C4=C6)C5=CC4=CC3=CC2=C1 601 | CC1=CCC2=C1C(=O)C(=O)C3=C2C=CC4=C3CCCC4(C)O 602 | C1=CC(=O)C1=N()=C2C=CC=C(=O)C1=N()C2=C1 603 | CCC1=C(C=CC(=C1)[N+](=O)[O-])NC 604 | CC1=CC=C2C=C3C=C4C=C5C=C6C=CC=CC6=CC5=CC4=CC3=CC2=C1C 605 | CCCC1=CC=C2C=CC=CC2=C1(CC 606 | C1=CC(=C(C(=)=C2C=C3C=C4C=C5C=CC=C(=C(C(=)C5=CC4=CC3=CC2=C1 607 | C1=CC=C2C=C3C=C4C=CC=CC4=CC3=CC2=C1C 608 | CC1=C(C(=O)N(C1)C2=C(=CC=C2)N=NC3=C(C=C(C4=CC=CC=C43)S(=O)(=O)O)O 609 | C1=CC(=O)C(=CC=C1N=O)N=NC(=C2C=CN(C=C2)O)O 610 | COC(=O)C=C[C-]1[CH-][CH-][CH-][CH-]1.[CH-]1[CH-][CH-][CH-][CH-]1.[Fe] 611 | O=Cl(=O)(=O)OCl(=O)(=O)=O 612 | C1=CC=C2C=C3C(=NF1)=CC=CC3=CC2=C(=NF1)1 613 | C1=CC(=C(C)=C2C(O(3O)OC4)=C3C=C4C=CC=C(=C(C)C4=C(O(3O)OC4)C3=CC2=C1 614 | CCCC1=CC(=CC=C1[N+](=O)[O-])OP(=S)(OC2=CC=C(C=C2)[N+](=O)[O-])OC3=CC=C(C=C3)[N+](=O)[O-]-CC 615 | C1=CC=C2C((=C(=O)3)=CC=CC2=C((=C(=O)3)1 616 | C1=CC(=O)C(=O1)=C2C=CC=C(=O)C(=O1)C2=C1 617 | C1=CCN=F1NC 2/C3C@@N(F(C(CC=C3)[N+](=O)[O-] 618 | C1=CC(3=C(C=C2N)=C2C=C3C(O)OO)=CC=C(3=C(C=C2N)C3=CC2=C(O)OO)1 619 | CCCOC(=O)NC1=C(C(=O)C(=C(C1=O)C2CC2)NC(=O)OCC)C3CC3C 620 | C1=CC=C2C=C3C(=O)C3C()=CC=CC3=CC2=C(=O)C3C()1 621 | CCCCC(C=O)C(CNS(=O)(=O)C1=CC=C(C=C1)[N+](=O)[O-])C(=O)OFP 622 | C1=CC(5O=OC)=C2C=CC=C(5O=OC)C2=C1 623 | CC1=C(C(=O)C(=CC1=NOC(=O)C2=CC=CC=C2) r)F 624 | C1=CC=C2C=C3C=C4C(3C7C(7C2)=C5C=CC=CC5=CC4=CC3=C(3C7C(7C2)C2=C1 625 | C1=CC=C2C=C3C=C4C(C(S5N)=C5C=C6C(C=C3)=CC=CC6=CC5=CC4=C(C(S5N)C3=CC2=C(C=C3)1 626 | CCCCCOC1=C(C(=O)C2=C(C1=O)C(C3(N2CC4C3N4)OC)COC(=O)N)C*SN 627 | C1=CC(C(1)O-)=C2C=C3C(=(C4)OC)=CC=C(C(1)O-)C3=CC2=C(=(C4)OC)1 628 | [CH2-][CH-][CH-][CH2-].[CH-]1[CH-][CH-][CH-][CH-]1.[Ir] 629 | CC1(=C(C(=O)C(C1=O)(Cl)Cl)Cl)Cl= 630 | C1=CC(=C)C(=C(N)=C2C(C(=C(C=O1)=C3C=C4C((=S()=CC=C(=C)C(=C(N)C4=C(C(=C(C=O1)C3=CC2=C((=S()1 631 | C1=CC=C2C=C3C=C4C(2C)C(7C2)=C5C=CC=CC5=CC4=CC3=C(2C)C(7C2)C2=C1 632 | CC.C1=CC(=CC=C1N2C(=O)C(=C(N2)C(=O)O)C=CC=CC=C3C(=NN(C3=O)C4=CC=C(C=C4)S(=O)(=O)O)C(=O)O)S(=O)(=O)OF 633 | C1=CC=C2C=C3C=C4C=C5C(N=C(O=N1)=CC=CC5=CC4=CC3=CC2=C(N=C(O=N1)1 634 | CC1=CC(=CC=C1N=NC2=C(C=C(C=C2Cl)[N+](=O)[O-])Cl)N(CCO)CCOC 635 | CC1=CC=C2C(=C1)C3=C4C(=CC=C5C4=C(C=C3)C6=CC=C7C8=C(C=CC5=C68)C9=C(C7=O)C=CC(=C9)Br)C2=OS 636 | CC1=C(C(=O)C(=CC1=NOC(=O)C2=CC=CC=C2) r)D 637 | C1=CC=C2C(=C1)C3=C(C2=O)C=CC=C3C(=O)SC4=C(C=C(C=C4)Cl)C(=O)NCCC(=O)N 638 | C1=CC=C2C(N=N1=C3=O)=CC=CC2=C(N=N1=C3=O)1 639 | CCCCC1=C2C(=C(C=C1)C(=O)O)N=C3C(=C(C(=O)C(=C3O2)C)N)C(=O)O(N= 640 | N.N.N.N.N.N.N.N.N.N.N.N.N.N.[O-2].[O-2].[Cl-].[Cl-].[Cl-].[Cl-].[Cl-].[Cl-].[Ru].[Ru].[Ru] 641 | C1=CC(2=C(C=C2N)=C2C=C3C(N(OO)=CC=C(2=C(C=C2N)C3=CC2=C(N(OO)1 642 | CCN(C1=CC=C2C(=C1)C(=O)C3=C(C2=O)C(=C(C=C3O)OC4=CC=C(C=C4)O)NCCPC 643 | CC1=CC2=CC(=O)C3=C(C2=C(C1=O)O)C(=O)C4=CC=CC(=O)C4=C3O 644 | C1=CC(=C(=N1)=C2C=C3C(=C(\(O)=CC=C(=C(=N1)C3=CC2=C(=C(\(O)1 645 | C1=CC(=CC=C1N=NC2=C(NC(=N)C=C2)O)S(=O)(=O)N 646 | C1=CC(C3=C)=C2C(5)C(C=O=C)=C3C(=C2=CC)=C4C=CC=C(C3=C)C4=C(5)C(C=O=C)C3=C(=C2=CC)C2=C1 647 | CC(=CC=C1=O)C(=O(s=NC(=N1)C(=O)OCC(C)C)C(=O)OCC(C)C 648 | COC1=CC=CC2=C1C(=O)C3=C(C2=O)C(=CC=C3)OC 649 | C1=CC=C2C=C3C=C4C=C5C=C6C(=O3CI(N5)=CC=CC6=CC5=CC4=CC3=CC2=C(=O3CI(N5)1 650 | CC1=CC=C2C=C3C=C4C=CC=CC4=CC3=CC2=C1N 651 | C1=CC=C2C(N=N(3)=CC=CC2=C(N=N(3)1 652 | C1=CC=C2C=C3C(C3CN=C3)=C4C=C5C(=N6=C2)=C6C=CC=CC6=CC5=C(C3CN=C3)C4=CC3=C(=N6=C2)C2=C1 653 | C1CCC2=C(C1)C3=C(N=CN=C3S2)N=NC(=O)C4=CC(=CC=C4)N(O)O 654 | C1=CC(=C(C1C )=C2C(=CC1)=C3C=C4C(C)C3=CC)=C5C=C6C=CC=C(=C(C1C )C6=C(=CC1)C5=CC4=C(C)C3=CC)C3=CC2=C1 655 | C1=CC=C2C(=C1)C(=O)C(=C(C2=O)Cl)NC3=NC=CS3 656 | C1=CC=C2C=C3C=C4C=C5C=CC=CC5=CC4=CC3=CC2=C1F 657 | C1=CC(=CC2=NC3=C(C=CC(=C3)NN=O)C=C21)NN=O 658 | 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CCC(=O)C(=O)C(=O)N(C1=CC=CC=C1)N(C)N=OC 796 | -------------------------------------------------------------------------------- /Obtain_smiles/Considered_smiles_set2.smi: -------------------------------------------------------------------------------- 1 | N.N.N.N.N.N.N.N.N.N.N.N.N.N.[O-2].[O-2].[Cl-].[Cl-].[Cl-].[Cl-].[Cl-].[Cl-].[Ru].[Ru].[Ru] 2 | CCCNC1=C2C(=C(C=C1)NCC)C(=O)C3=CC=CC=C3C2=OC 3 | CC1=CC=C2C(=C1)C(=O)C3=CC(=C(C(=C3C2=O)O)O)S(=O)(=O)OC 4 | C1=CC=C2C(C=N0)=C3C(=S(=C4O)=CC=CC3=C(C=N0)C2=C(=S(=C4O)1 5 | CC1=CC=C(C=C1)C(=C2C(F)C(=O)S2=N(S3=NC4=C(S3)C=C(C=C4)F)C5=CC(=C(C=C5)OC)OC)O 6 | CC1=CC=C2C=C3C=C4C=CC=CC4=CC3=CC2=C1C 7 | CCCOCNC1=CC=CC2=C1C(=O)C3=C(C2=O)C(=CC=C3)NCl=CP 8 | C1=CC(C2=OC(C(1)=C2C=C3C=CC=C(C2=OC(C(1)C3=CC2=C1 9 | CC1=NSC2=C1C(=O)C(=O)C3=C2C=CC4=C3CCCC4(C)O 10 | C1=CC(=N=O)=C2C=C3C=CC=C(=N=O)C3=CC2=C1 11 | C1=CC=C2C(C=3C(=C(3)=C3C=C4C=C5C=CC=CC5=C(C=3C(=C(3)C4=CC3=CC2=C1 12 | C1=CC=C2C=C3C(=ON3)=CC=CC3=CC2=C(=ON3)1 13 | C1=CC(B0(C1)F)=C2C=C3C=C4C=CC=C(B0(C1)F)C4=CC3=CC2=C1 14 | C1=CC=C2C=C3C((#O)C)=C4C=CC=CC4=CC3=C((#O)C)C2=C1 15 | C1=CC=C2C=C3C(C3CN=C3)=C4C=C5C(=O2=C2)=C6C=CC=CC6=CC5=C(C3CN=C3)C4=CC3=C(=O2=C2)C2=C1 16 | C1=CN=C2C(=N1)C(=O)OC2=O 17 | C1=CC(C(1)N-)=C2C=C3C(=(C4)OC)=CC=C(C(1)N-)C3=CC2=C(=(C4)OC)1 18 | C1=CC=C2C(==N((=C5)=C3C=C4C=CC=CC4=C(==N((=C5)C3=CC2=C1 19 | C1=CC(=O=N)=C2C=C3C=CC=C(=O=N)C3=CC2=C1 20 | C1=CC=C2C=CC=CC2=C1C-== 21 | C1=CC=C2C(C=C3=N4)=CC=CC2=C(C=C3=N4)1 22 | CC1=NCC2=C1C(=O)C(=O)C3=C2C=CC4=C3CCCC4(C)O 23 | C1=CC=C2C=C3C=C4C=C5C=C6C(=O)CN(N5)=CC=CC6=CC5=CC4=CC3=CC2=C(=O)CN(N5)1 24 | CC1=C(C(=C(C(=C1Cl)Cl)[N+](=O)[O-])Cl)[N+](=O)[O-]C 25 | CC(=O)2C(=CC(C2C11C)N=NC2=C(C3=C(C=C(C=C3C=C2S(=O)(=O)O)S(=O)(=O)O)NS(=O)(=O)C4=CC=CC=C4)O 26 | C1=CC=C2C(C=N1)=C3C(=C(=C)N)=CC=CC3=C(C=N1)C2=C(=C(=C)N)1 27 | CCCCNC1=CC=CC2=C1C(=O)C3=CC=CC=C3C2=O 28 | CC1=CC(=O)C(=CC1=O)N(CCO)CCOP 29 | C1=CC=C2C=C3C=C4C(OC=CC(=C)=C5C=C6C=CC=CC6=CC5=CC4=C(OC=CC(=C)C3=CC2=C1 30 | CC1=CC(=CC=C1N2C(=O)C(=C(C2=O)Cl)Cl)FO 31 | C1=CC(=N3S((O)=C2C=C3C=C4C(C3=C)C==C)=CC=C(=N3S((O)C4=CC3=CC2=C(C3=C)C==C)1 32 | CC1=C(N(C(=S)C(=C1C)C#N)[C@H]2[C@@H]([C@@H]([C@@H]([C@H](O2)CO 33 | CCS(C1=CC=C2C=C3C=CC=CC3=CC2=C1)NOO 34 | C1=CC=C2C(C=CC3O)=C3C(4)C((C((C)=C4C=C5C=CC=CC5=C(C=CC3O)C4=C(4)C((C((C)C3=CC2=C1 35 | CC(C1=CC=C2C(=C1)C(=O)C=C(C2=O)NC(CO)(CO)CO(CO 36 | C1=CSN=F1NC 2/C3C@@NIF(C(CC=C3)[N+](=O)[O-] 37 | C[C-]1[C-]([C-]([C-]([C-]1 OY)N)C0)NC-]([CH2-])[CH2-].[Co] 38 | C1=CC(=C)C(4)=C2C=C3C=C4C=C5C=CC=C(=C)C(4)C5=CC4=CC3=CC2=C1 39 | C(CC(C)(C)OC(=O)ON1C(=O)C2=CC=CC=C2C1=OCF 40 | C1=CC=C2C=C3C(=N(C3C))=CC=CC3=CC2=C(=N(C3C))1 41 | CCCCCOC1=C(C(=O)C2=C(C1=O)C(C3(N2CC4C3N4)OC)COC(=O)N)C 42 | CC1=CC=C2C=C3C=C4C=CC=CC4=CC3=CC2=C1O 43 | C1=CC=C2C=C3C((=O)S3=C)=C4C=C5C=CC=CC5=CC4=C((=O)S3=C)C3=CC2=C1 44 | C1=CC(=C(C)=C2C(O(3O)NC4)=C3C=C4C=CC=C(=C(C)C4=C(O(3O)NC4)C3=CC2=C1 45 | CN(C)CCNC(=O)C1=C2C(=NC3=C(N2)C=C(C(=C3)OC)C(=O)OC)C4=C$=CC=C4C1=O 46 | C1=CC(=C(C=C1C2=C(C=C3C(=CC(=O)C=C3O2)O)O[C@H]4[C@@H]([C@H]([C@@H]([C@H](O4)COC(=O)CC(=O)O)O)O)O)O)O 47 | COC1=CC=C(C=C1)C(=O)CC2=NC3(C(=C(N3N=C2CC(=O)C4=CC=C(C=C4)OC)NC(=O)NC5=CC=C(C=C5)Cl 48 | C1=CC=C2C=C3C=C4C=C5C=C6C(C=O2C)=CC=CC6=CC5=CC4=CC3=CC2=C(C=O2C)1 49 | C1=CC=C2C(=C1)N=C3C=CC(=O)C=C3S2 50 | C1=CC=C2C=C3C(=O)C3C()=CC=CC3=CC2=C(=O)C3C()1 51 | C1=CC=C2C=C3C(C=O5)=C4C=C5C=CC=CC5=CC4=C(C=O5)C3=CC2=C1 52 | C1=CC(=C(=O)=C2C=C3C=C4C=C5C((C(N)CC)=CC=C(=C(=O)C5=CC4=CC3=CC2=C((C(N)CC)1 53 | C1=CC=C2C=C3C=C4C=C5C(O=C(N=O1)=CC=CC5=CC4=CC3=CC2=C(O=C(N=O1)1 54 | C1=CC(=O)C1=N()=C2C=CC=C(=O)C1=N()C2=C1 55 | C1=CC=C2C(=C1)C(=O)C3=C(C2=O)C(=C(C=C3NC4=CC(=C(C=C4)Cl)S(=O)(=O)C)S(=O)(=O)O)N 56 | CCCOC1=C(C=CC(=C1)N=NC2=CC(=CC=C2)S(=O)(=O)O)N=NC3=CN(N=C3C(=O)O)C4=CC(=CC=C4)NC= 57 | C=C1=CC(=NC=C1[N+](=O)[O-])BrCC 58 | C1=CC=C2C=C3C(=O(C3C()=CC=CC3=CC2=C(=O(C3C()1 59 | C1=CC(=C1NO)=C2C=C3C=C4C(CCB4=CC)=CC=C(=C1NO)C4=CC3=CC2=C(CCB4=CC)1 60 | 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C1=CC=C2C(O3C4C3N(C)=C3C(CO)C)=C4C(=C()SC)=C5C=CC=CC5=C(O3C4C3N(C)C4=C(CO)C)C3=C(=C()SC)C2=C1 76 | C1=CSN=F1NC 2/C3C@DNIF(C(CC=C3)[N+](=O)[O-] 77 | CCC(=O)NC1=CC=CC2=C1C(=O)C3=C(C2=O)C=CC=C3NC(=O)CC 78 | CCN1C(=C(C(=C(C1=O)N=NC2=C(C=CC(=C2)NC3=NC(=NC(=N3)Cl)OC(C)C)S(=O)(=O)O)C)CS(=O)(=O)O)O 79 | CCCCSC(=C[N+](=O)[O-])SCCSC 80 | C1=CC=C2C(5)C4(S)=CC=CC2=C(5)C4(S)1 81 | C1=CC=C2C(C=CC3O)=C3C(4)C()NC(C)=C4C=C5C=CC=CC5=C(C=CC3O)C4=C(4)C()NC(C)C3=CC2=C1 82 | CCC=CCN(CC)C1=CC(=C(C=C1)N=NC2=NC(=C(N2C)C#N)C#N)NC(=O)C(C=O 83 | C1=CC=C2C((=C(=O)2)=CC=CC2=C((=C(=O)2)1 84 | C1=CC(=C(=N1)=C2C=C3C(=C(\(O)=CC=C(=C(=N1)C3=CC2=C(=C(\(O)1 85 | C1=CC=C2C=C3C=CC=CC3=CC2=C1C 86 | C1=CC(1C(C)=C2C=C3C(N23=N4)=CC=C(1C(C)C3=CC2=C(N23=N4)1 87 | C1=CC=C2C=C3C((0O)C)=C4C=CC=CC4=CC3=C((0O)C)C2=C1 88 | C1=CC(=C5(C1)C)=C2C=C3C=C4C(F9C(S4)O)=C5C=CC=C(=C5(C1)C)C5=CC4=CC3=C(F9C(S4)O)C2=C1 89 | C1=CC(=S(C)=C2C(N(1O)OC4)=C3C=C4C=CC=C(=S(C)C4=C(N(1O)OC4)C3=CC2=C1 90 | C1=CC(=N1C((O)=C2C=C3C=C4C(C3=C)C==C)=CC=C(=N1C((O)C4=CC3=CC2=C(C3=C)C==C)1 91 | C1=CC(=N1C)(O)=C2C=C3C=C4C(C3=C(C==C)=CC=C(=N1C)(O)C4=CC3=CC2=C(C3=C(C==C)1 92 | C1=CC=C2C=C3C=C4C(3C(C46C2)=C5C=CC=CC5=CC4=CC3=C(3C(C46C2)C2=C1 93 | C1=CC=C2C=C3C=C4C=C5C=C6C(O=O1)=CC=CC6=CC5=CC4=CC3=CC2=C(O=O1)1 94 | CCCC12CCC3C(C1CC(=O)C2=O)CCC4=C3C=CC(=C4)O=C 95 | C1=CC=C2C(O1C=OC)=CC=CC2=C(O1C=OC)1 96 | C1=CC(S0(C1)N)=C2C=C3C=C4C=CC=C(S0(C1)N)C4=CC3=CC2=C1 97 | C1=C(C=C2C(=C1Br)N=C(N2)C F)(F)F)[N+](=O)[O-] 98 | CCCOCNC1=CC=CC2=C1C(=O)C3=C(C2=O)C(=CC=C3)NCn=CP 99 | C1=CC(=C1(O=OC)=C2C(=NC(9C)=C3C=C4C(2=OS)C4)=CC=C(=C1(O=OC)C4=C(=NC(9C)C3=CC2=C(2=OS)C4)1 100 | C1=CC=C2C(N=3C(=C(3)=C3C=C4C=C5C=CC=CC5=C(N=3C(=C(3)C4=CC3=CC2=C1 101 | CON(CCC(=O)OC1=C(C=C(C=C1)C=C[N+](=O)[O-])OC(=NP 102 | CC(=O)C(=O)C(=O)N(C1=CC=CC=C1)N(C)N=O.O 103 | C1=CC=C2C(C=O(/C4)=CC=CC2=C(C=O(/C4)1 104 | C1=CC(=N1C)(F)=C2C=C3C=C4C(C3=C(N==C)=CC=C(=N1C)(F)C4=CC3=CC2=C(C3=C(N==C)1 105 | C1=CC=C2C=C3C=C4C(NC(C46C2)=C5C=CC=CC5=CC4=CC3=C(NC(C46C2)C2=C1 106 | C1=CC=C2C(=C(=N5)=C3C=C4C=C5C=C6C=CC=CC6=C(=C(=N5)C5=CC4=CC3=CC2=C1 107 | C1=CC(C2=NC(C(1)=C2C=C3C=CC=C(C2=NC(C(1)C3=CC2=C1 108 | C1=CC=C2C=C3C(C5N4C3(Cl)=CC=CC3=CC2=C(C5N4C3(Cl)1 109 | CCC1=CC(=CC(=C1)[N+](=O)[O-])C(O)S(=O)(=O)OSC 110 | C1=CC(N1)C2=C()=C2C(=C((C)C)=C3C(C)C2)C()=C4C=CC=C(N1)C2=C()C4=C(=C((C)C)C3=C(C)C2)C()C2=C1 111 | C1=CC(B0(C1)n)=C2C=C3C=C4C=CC=C(B0(C1)n)C4=CC3=CC2=C1 112 | C1=CC=C(C=C1)SC2=CC(=O)C3=C(N=C=C3C2=O 113 | CC1=C2C=C(C=C(C2=C(C=C1S(=O)(=O)O)O)S(=O)(=O)O)S(=O)(=O)OC 114 | C1=CC=C2C(C=C1=N4)=CC=CC2=C(C=C1=N4)1 115 | CC1C2=C(C=CC(=C2)[N+](=O)[O-])C3=C1C=C(C=C3)F= 116 | C1=CC=C2C(C-C(=O)C4)=C3C=C4C=CC=CC4=C(C-C(=O)C4)C3=CC2=C1 117 | C[C-]1[C-]([C-]([C-]([C-]1 OY)N)C4)NC-]([CH2-])[CH2-].[Co] 118 | C1=CC=C2C(C=C(=OO)=CC=CC2=C(C=C(=OO)1 119 | C1=CC=C2C(=C(=O5)=C3C=C4C=C5C=C6C=CC=CC6=C(=C(=O5)C5=CC4=CC3=CC2=C1 120 | C1=CC=C2C=C3C(C3C3=C1)=C4C=C5C(=N3=C2)=C6C=CC=CC6=CC5=C(C3C3=C1)C4=CC3=C(=N3=C2)C2=C1 121 | C1=CC=C2C(5)C4(C)=CC=CC2=C(5)C4(C)1 122 | C1=CC(=C=N)=C2C=C3C(=OC(C3=NC)=C4C=C5C=CC=C(=C=N)C5=CC4=C(=OC(C3=NC)C3=CC2=C1 123 | CC1CC2=NC1=CC3=NC(=CC4=CCC(=CC5=NC(=C2)C=C5)N4)C=C3C 124 | C1=CC=C2C=C3C(C3CN=C3)=C4C=C5C(=N6=C2)=C6C=CC=CC6=CC5=C(C3CN=C3)C4=CC3=C(=N6=C2)C2=C1 125 | C1=CC=C2C=C3C=C4C=C5C=C6C(=O)CO(N5)=CC=CC6=CC5=CC4=CC3=CC2=C(=O)CO(N5)1 126 | C-CC(CCOC1=C(C2=C(C(=C1)O)C(=O)C3=CC=CC=C3C2=O)N)O=P 127 | C1=CC=C2C(=C1)C=CC(=N2)C(=O)NN=CC3=C(C=C(C=C3)O)O 128 | CCN-CC(C)(C)C1=CC2=C(C=C1)C(=O)C3=CC=CC=C3C2=O (FN 129 | C1=CC=C2C(C=C(S)=C3C(N=2C((O)=CC=CC3=C(C=C(S)C2=C(N=2C((O)1 130 | C1=CC(C2=CC)=C2C=C3C=C4C=C5C=C6C=CC=C(C2=CC)C6=CC5=CC4=CC3=CC2=C1 131 | CCCC1(C(N2C(S1)C(C2=O)NC(=O)C3=NC4=CC=CC=C4N=C3C(=O)O)C(=O)O)CFP 132 | C1=CC=C2C=C3C(=CC(3C)=C4C(=O)(C)C6C)=C5C=CC=CC5=CC4=C(=CC(3C)C3=C(=O)(C)C6C)C2=C1 133 | CCC1=C[N+](=O)C2=CC=CC=C2N1[O-]N 134 | 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CC1=CC=C2C=C3C=C4C=C5C=C6C=CC=CC6=CC5=CC4=CC3=CC2=C1O 167 | C1=CC=C2C=C3C=C4C=C5C=C6C=CC=CC6=CC5=CC4=CC3=CC2=C1C 168 | C1=CC=C2C=C3C(=NF1)=CC=CC3=CC2=C(=NF1)1 169 | CCOC1=CC=C2C(=C1)C(=O)C3=C(C2=O)C(=C(C=C3O)O)O-SC 170 | CCCOC1=C(C2=C(C=C1)C(=O)C3=CC=CC=C3C2=O)OOC 171 | C1=CC=C2C(C=C(2)=C3C(N=2C((O)=CC=CC3=C(C=C(2)C2=C(N=2C((O)1 172 | C1=CC=C2C(=C1)C(=O)N(C2=O)C3=NNN=N3 173 | CCNCC1=CC=C(C=C1)S(=O)(=O)N(C)CC2=CC=C(C=C2)NC3=CC(=C(C4=C3C(=O)C5=CC=CC=C5C4=O)N)S(=O)(=O)O(CC 174 | C1=CC=C2C(=C1)C=C(C(=O)O2)C(=O)NC3=CC4=NN(N=C4C=C3)C5=CC=C(C=C5)B 175 | C1=CC=C2C=C3C(=C(C(C)C2)=C4C=C5C=CC=CC5=CC4=C(=C(C(C)C2)C3=CC2=C1 176 | C1=CC=C2C(=C1)C(=O)C3=C(C2=O)C(=C(C=C3NC4=CC(=C(C=C4)Cl)S(=C)(=O)C)S(=O)(=O)O)N 177 | CC(=CC=C1=O)C(=O(s=NC(=N1)C(=O)OCC(C)C)C(=O)OCC(C)C 178 | C1=CC=C2C=C3C(=C(23C1C)=C4C(N(C)C4=C)=CC=CC4=CC3=C(=C(23C1C)C2=C(N(C)C4=C)1 179 | C1=CC=C2C(C=N(=C(C3)=CC=CC2=C(C=N(=C(C3)1 180 | CCN1C(=C(C(=O)N(C1=S)CC)N=O)O.CCN1C(=C(C(=O)N(C1=S)CC)N=O)O.CCN1C(=C(C(=O)O(C1=S)CC)N=O)O.[Fe] 181 | CCC1=CC=C2C(=C1)C3=C4C(=C(C=C3)SCC(=O)O)C=CC=C4C2=OON 182 | C1=CC2=C(C(=CC(=C2C=C1N)C(=O)(=O)O)O)N=NC3=C(C4=C(C=C3)C(=CC(=C4N)S(=O)(=O)O)S(=O)(=O)O)O 183 | CCN-CC(CNC1=C2C(=C(C=C1)O)C(=O)C3=CC=CC=C3C2=O)C#N=NC= 184 | CFCC(C)CC(=O)OCC1(C(=O)OC2(N1C(=O)C3=C(C2)C=C4C=CC5=C(C4=C3)C(=O)C6=C(C5=O)OC7=CC(=C(C=C7C6=O)O)OC)C 185 | CNC(=O)C1=C(C(=C(C(=C1I)NC(=O)CC(=O)N(C=C(C3=C(S(=C3I)C(=O)O)I)C(=O)NC)I)I)C(=O)O)I 186 | CCC(=O)NC1=C(C(=O)C2=CC=CC=C2C1=O)ClF 187 | C1=CC=C2C=C3C=C4C(2C-C(7C2)=C5C=CC=CC5=CC4=CC3=C(2C-C(7C2)C2=C1 188 | CCC1=CC=C(C=C1)N=C(N=C2C=C(C=CC2=O)N(O)O)SCC 189 | C1=CC(=C(C(=C(N)=C2C(C(=C(C=O1)=C3C=C4C(8=S))=CC=C(=C(C(=C(N)C4=C(C(=C(C=O1)C3=CC2=C(8=S))1 190 | CCCCCOC1=C(C(=O)C2=C(C1=O)C(C3(N2CC4C3N4)OC)COC(=O)N)C(SN 191 | C1=CC=C2C=C3C=C4C=CC=CC4=CC3=CC2=C1=CC= 192 | C1=CC(=CC=C1N2C(=O)C(=C(N2)C(=O)O)C=CC=CC=C3C(=NN(C3=O)C4=CC=C(C=C4)S(=O)(=O)O)C(=O)O)S(=O)(=O)O 193 | C1=CC=C2C(O=CC5)=CC=CC2=C(O=CC5)1 194 | C1=CC=C2C(C=C=OC)=C3C(\==C)=C4C(N6=C)=CC=CC4=C(C=C=OC)C3=C(\==C)C2=C(N6=C)1 195 | CNC(=O)C1=C(C(=C(C(=C1I)NC(=O)CC(=O)N(C=C(C3=C(C(=C3I)C(=O)O)I)C(=O)NC)I)I)C(=O)O)I 196 | C1=CC=C2C(=C1)C(=O)C3=C(C2=O)C(=C(C=C3NC4=CC(=C(C=C4)Cl)S(=C)(=O)C(S(=O)(=O)O)N 197 | C1=CCN=F1NC 2/C3C@@NHF(C(CC=C3)[N+](=O)[O-] 198 | CCNC1=CC(=C2C(=C1N)C(=O)C3=C(C2=O)C(=CC(=C3O)S(=O)(=O)O)N)O(CN 199 | CCSOCCN(CC)C1=CC2=C(C=C1)C(=C3C=CC(=[N+](CC)CC)C=C3O2)C4=C(C=C(C=C4)S(=O)(=O)Cl)S(=O)(=O)[O-]C(=F 200 | C1=CC(=C(3=C(N)=C2C=C3C=C4C(O=C)NO)=C5C=C6C=CC=C(=C(3=C(N)C6=CC5=CC4=C(O=C)NO)C3=CC2=C1 201 | CCC(=O)C(=O)C(=O)N(C1=CC=CC=C1)N(C)N=OC 202 | C1C(C(=O)N(C1=O)COCN2C(=O)C=CC2=O)SCC(C(=O)O)N 203 | C1=CC=C2C=CC=CC2=C1NP 204 | C1=CC=C2C=C3C(=O)(3O)=C4C=CC=CC4=CC3=C(=O)(3O)C2=C1 205 | C1=CC=C2C=C3C(=OC1(=)F)=CC=CC3=CC2=C(=OC1(=)F)1 206 | CCC1=C(C=CC(=C1)N)N=NC2=CC(=C(C=C2)N=NC3=CC4=C(C=C(C=C4C=C3)S(=O)(=O)O)S(=O)(=O)O)CS 207 | CCOC1=CC=C(C=C1)N=NC2=CC=C(C=C2)N=NC3=CC4=CC(=CC(=C4C=C3)S(=O)(=O)O)S(=C)C 208 | CC1=C(C=C(C=C1Cl)Cl)[N+](=O)[O-]C 209 | C1=CC=C2C=C3C=C4C(C(S5N)=C5C=C6C(C=C2)=CC=CC6=CC5=CC4=C(C(S5N)C3=CC2=C(C=C2)1 210 | C1=CC=C2C(C=C(2)=C3C(N=2C)(O)=CC=CC3=C(C=C(2)C2=C(N=2C)(O)1 211 | C1=CC=C2C(=C1)C(=O)C3=C(C2=O)C(=C(C=C3NC4=CC(=C(C=C4)Cl)S(=O)(=O)C(S(=O)(=O)O)N 212 | C1=CC=C2C(C=O1)=CC=CC2=C(C=O1)1 213 | CC.C1=CC(=CC=C1N2C(=O)C(=C(N2)C(=O)O)C=CC=CC=C3C(=NN(C3=O)C4=CC=C(C=C4)S(=O)(=O)O)C(=O)O)S(=O)(=O)OF 214 | CCCC(C)CCNC1=C(C(=O)C2=CC=CC=C2C1=O)ClFC 215 | C1=CC=C2C(N=C4=CN5)=C3C=C4C=C5C=CC=CC5=C(N=C4=CN5)C4=CC3=CC2=C1 216 | C1=CC=C2C=C3C(=CC(3N)=C4C(=O)(C)N6C)=C5C=CC=CC5=CC4=C(=CC(3N)C3=C(=O)(C)N6C)C2=C1 217 | C1=CC(=C=O)=C2C=C3C=C4C=CC=C(=C=O)C4=CC3=CC2=C1 218 | CC1C(C(CC(O1)OC2CC(CC3=C(C4=C(C(=C23)O)C(=O)C5=C(C4=O)C=CC=C5O(OC(C)=O)C)O)N)O 219 | CCC1=CC=C(C=C1)C(=NC2=CC=C(C=C2)[N+](=O)[O-])NCC 220 | C1=CC(C3=C)=C2C(5)C(C=O=C)=C3C(=C2=CC)=C4C=CC=C(C3=C)C4=C(5)C(C=O=C)C3=C(=C2=CC)C2=C1 221 | C1=CC=C2C=C3C=C4C(C(S4C)=C5C=C6C(C=C2)=CC=CC6=CC5=CC4=C(C(S4C)C3=CC2=C(C=C2)1 222 | CC1=CC=C2C=CC=CC2=C1F 223 | C1=CC(=C2C(=C1)OC3=CC(=O)C(=C(C3=N2)C(=O)O)N)CO 224 | C1=CC(=C*=O)=C2C=C3C=C4C=C5C((C(N)CC)=CC=C(=C*=O)C5=CC4=CC3=CC2=C((C(N)CC)1 225 | COCC1CCCN1S(=O)(=O)C2=CC3=C(C=C2)NC(=O)C3=O 226 | CC(=O)NC1=CC=CC2=C1C(=O)C3=C(C2=O)C(=CC=C3)N 227 | C1=CC=C2C=C3C=C4C=C5C=C6C=CC=CC6=CC5=CC4=CC3=CC2=C1CS 228 | C1=CC(=OCC(=OC1)=C2C(N3C(F)=C3C=C4C=C5C=CC=C(=OCC(=OC1)C5=C(N3C(F)C4=CC3=CC2=C1 229 | CCCCCCCCCCCCC1=C(C(=O)C2=CC=CC=C2C1=O)OC(=O)C 230 | CCN(C1=CC=C2C=C3C=CC=CC3=CC2=C1)N=N 231 | C1=CC=C2C=C3C=C4C=C5C(N=C(N=N3)=CC=CC5=CC4=CC3=CC2=C(N=C(N=N3)1 232 | C1=CC=C2C=C3C(C3CF=C3)=C4C=C5C(=C2=C2)=C6C=CC=CC6=CC5=C(C3CF=C3)C4=CC3=C(=C2=C2)C2=C1 233 | CCC(=O)NC1=CC2=C(C=C1)C(=O)C3=CC=CC=C3C2=OC 234 | C1=CC(C(1)N-)=C2C=C3C(=(C4)NC)=CC=C(C(1)N-)C3=CC2=C(=(C4)NC)1 235 | C1=CC=C2C=C3C=C4C=C5C=C6C(N=O5)=CC=CC6=CC5=CC4=CC3=CC2=C(N=O5)1 236 | CC1=CC=C(C=C1)NC2=C3C(=C(C=C2)O)C(=O)C4=C(C=CC(=C4C3=O)O)NS 237 | CCC(C1=CC=C2C=C3C=CC=CC3=CC2=C1 3CF 238 | CCN(C1=CC2=[N+](C=CC(=C2C=C1C(=O)O)[N+](=O)[O-])[O-]C(CC 239 | C1=CC(2=C(C=C3N)=C2C=C3C(N)OO)=CC=C(2=C(C=C3N)C3=CC2=C(N)OO)1 240 | C1=CSN=F1NC 2/C3C@@NHF(C(CC=C3)[N+](=O)[O-] 241 | C=C(C1CN1C2=CC(=O)C(=O)C3=CC=CC=C32)CCC 242 | CCC1=C2C3=C(C=C1)C(=COC3=C(C(=O)C2=O)C)CCC=C(C)CS 243 | C1=CC=C2C=C3C=C4C=CC=CC4=CC3=CC2=C1COC 244 | C1=CC=C2C=C3C(C3C3=C3)=C4C=C5C(=N3=C2)=C6C=CC=CC6=CC5=C(C3C3=C3)C4=CC3=C(=N3=C2)C2=C1 245 | CC1=CC(=C(C=C1O)[N+](=O)[O-])ClC 246 | C1=CC(=C(=)C(C1)=C2C=C3C=C4C=C5C=C6C=CC=C(=C(=)C(C1)C6=CC5=CC4=CC3=CC2=C1 247 | CCCCC1(C2=CC=CC=C2N=C1SC3=CC=CC=C3[N+](=O)[O-])BrOOP 248 | C1=CC(SC=C)=C2C(5)C(C=N=C)=C3C(=N2=CC)=C4C=CC=C(SC=C)C4=C(5)C(C=N=C)C3=C(=N2=CC)C2=C1 249 | C1=CC(=C(=)C(C5)=C2C=C3C=C4C=C5C=C6C=CC=C(=C(=)C(C5)C6=CC5=CC4=CC3=CC2=C1 250 | C1=CC=C2C=C3C(=CC(3C)=C4C(=O)(C(C6C)=C5C=CC=CC5=CC4=C(=CC(3C)C3=C(=O)(C(C6C)C2=C1 251 | CC1=CC=C2C=C3C=C4C=C5C=CC=CC5=CC4=CC3=CC2=C1C 252 | CC(=CC(=O)O)C(=O)NC(=O)NC 253 | C1=CC=C2C=C3C(=O)C2C()=CC=CC3=CC2=C(=O)C2C()1 254 | C1=CC=C2C(=OC()=C3C=C4C=CC=CC4=C(=OC()C3=CC2=C1 255 | C1=CC(S0(C1)F)=C2C=C3C=C4C=CC=C(S0(C1)F)C4=CC3=CC2=C1 256 | CSC1=CC=C2C=C3C=C4C=C5C=C6C=CC=CC6=CC5=CC4=CC3=CC2=C1NC 257 | C1=CC=C2C(C=CC3O)=C3C(4)C((NC(C)=C4C=C5C=CC=CC5=C(C=CC3O)C4=C(4)C((NC(C)C3=CC2=C1 258 | CCCCCNC(=O)OCCN(CC)C1=CC=C(C=C1)N=NC2=C3C=C(C=CC3=NS2)[N+](=O)[O-]F 259 | CCCOC1=C2C(=C(C3=C1OC=C3)[N+](=O)[O-])C=CC(=O)O2CP 260 | C1=CC(C3=NC(C(1)=C2C=C3C=CC=C(C3=NC(C(1)C3=CC2=C1 261 | C1=CC=C2C=C3C(=OC0)=(F)=CC=CC3=CC2=C(=OC0)=(F)1 262 | C1=CC(=O)C(=N1)=C2C=CC=C(=O)C(=N1)C2=C1 263 | C1=CC=C2C=C3C=C4C=C5C=C6C(NO4=C4)=CC=CC6=CC5=CC4=CC3=CC2=C(NO4=C4)1 264 | CS(=O)(=O)NC1=CC=C(C=C1)NC2=C3C=CC=CC3=NC4=CC=CC=C42.CS(=O)(=O)O 265 | C1=CC2=C(C=C1OC(F)(F)F)C(=O)C3=NC4=C(C=NC=C4)C(=O)N23 266 | COC(CC1C=NC(=B1)C2N(C(=O)CS2)C3=CC=C(C=C3)C4=CSC(=N4)NS(=O)(=O)C5=CC(=C(C=C5)Cl)OC)O 267 | C1=CC(C2=C)=C2C(5)C(C=C(C)=C3C(=C2=CC)=C4C=CC=C(C2=C)C4=C(5)C(C=C(C)C3=C(=C2=CC)C2=C1 268 | CC1=CC2=C(C(=C1)N)C(=O)C3=C(C2=O)C(=CC=C3)N= 269 | C1=CC(5-=OC)=C2C=CC=C(5-=OC)C2=C1 270 | CSCOC1=C2C(=C(C3=C1OC=C3)[N+](=O)[O-])C=CC(=O)O2CP 271 | CC(=CC(=O)C)O.CC(=CC(=O)C)O.O=[V] 272 | CCOC1=CC=C(C=C1)N=NC2=CC=C(C=C2)N=NC3=CC4=CC(=CC(=C4C=C3)S(=O)(=O)O)S(=C(C 273 | CC(COCCNC1=C2C(=C(C=C1)NCCOC)C(=O)C3=CC=CC=C3C2=O(P= 274 | COC(=O)C=C[C-]1[CH-][CH-][CH-][CH-]1.[CH-]1[CH-][CH-][CH-][CH-]1.[Fe] 275 | C1=CC=C2C=C3C=C4C=C5C=C6C(=N/Cn(N5)=CC=CC6=CC5=CC4=CC3=CC2=C(=N/Cn(N5)1 276 | C1=CC=C2C=C3C(C3CN=C3)=C4C=C5C(=N3=C2)=C6C=CC=CC6=CC5=C(C3CN=C3)C4=CC3=C(=N3=C2)C2=C1 277 | CCCOC(=O)C1=CC(=CC(=C1O)[N+](=O)[O-])[N+](=O)[O-]=C 278 | CON(CCC(=O)OC1=C(C=C(C=C1)C=C[N+](=O)[O-])OC)nNP 279 | CC[O-]S(=O)(=O)[O-].[O-]S(=O)(=O)[O-].[O-]S(=O)(=O)[O-].[V].[V] 280 | CC1=CC(=CC=C1C(=O)C=O)NCC2=CN=C3C(=N2)C(=NC(=N3)N)NF 281 | C1=CC=C2C=C3C=CC=CC3=CC2=C1= 282 | CC1CN(CCN1C2=CC=CC=C2)C3=C(C4=NO[N+](=C4C=C3)[O-])[N+](=O)[O-]C 283 | C1=CC=C2C=C3C=C4C(OC5CC(=C)=C5C=C6C=CC=CC6=CC5=CC4=C(OC5CC(=C)C3=CC2=C1 284 | C=CCCN1C(=O)C=CC1=ON= 285 | C1=CC=C2C(C=C(O1)=C3C(C(=C)=C4C(O6=C)=CC=CC4=C(C=C(O1)C3=C(C(=C)C2=C(O6=C)1 286 | C1=CC=C2C(C=N1)=C3C(=C(=C.N)=CC=CC3=C(C=N1)C2=C(=C(=C.N)1 287 | C1=CC2=C(C(=C1=C(S(=O)C(=O)C3=C(C2=O)C=CC=C3O 288 | C1=CC=C2C=C3C=C4C=C5C=C6C(=O(C5)N3)=CC=CC6=CC5=CC4=CC3=CC2=C(=O(C5)N3)1 289 | CCCCO.CCCCO.CC(=C)C(=O)O.CC(=C)C(=OOS RTi] 290 | CCCC1CCC2(C(C1(C)CC3=CC(=O)C=CC3=O)CCC=C2C)CPC 291 | CCN(C1=CC(=CC=C1[N+](=O)[O-])S(=O)C2=CC=C(C=C2)[N+](=O)[O-])CPN 292 | CC1=CC(=O)C=CC1=NC2=CC(=C(C(=C2)Cl)O)ClC 293 | C1=CC=C2C(N=N3=C3=O)=CC=CC2=C(N=N3=C3=O)1 294 | C1=CC=C2C=C3C(=OC1(=(F)=CC=CC3=CC2=C(=OC1(=(F)1 295 | C1=CCN=F1NC 2.C3C@@N(F(C(CC=C3)[N+](=O)[O-] 296 | CCNC1=CC=C2C(=C1)C3=C4C(=C5C=CC=C6C5=C3C7=CC=CC=C7C6=O)C=CC=C4C2=O(SF 297 | C1=CC=C2C=C3C=C4C=C5C(N=C(O=N1)=CC=CC5=CC4=CC3=CC2=C(N=C(O=N1)1 298 | C1=CSN=F1NC 2/C3C@@NIF C(CC=C3)[N+](=O)[O-] 299 | CON(CCC(=O)OC1=C(C=C(C=C1)C=C[N+](=O)[O-])OC)-NP 300 | CCC1=CC=C(C=C1)NC2=CC(=C(C3=C2C(=O)C4=CC=CC=C4C3=O)N)BrC 301 | C1=CC=C2C=C3C=C4C(2C)C(7C2)=C5C=CC=CC5=CC4=CC3=C(2C)C(7C2)C2=C1 302 | C1=CC=C2C(C=N1)=C3C(=S(=C.N)=CC=CC3=C(C=N1)C2=C(=S(=C.N)1 303 | C1=CC=C2C(C=C(O3)=C3C(S(=C)=C4C(N6=C)=CC=CC4=C(C=C(O3)C3=C(S(=C)C2=C(N6=C)1 304 | C1=CC=C2C(C=\0)3C(F)=CC=CC2=C(C=\0)3C(F)1 305 | C1=CC(=C(C3F()=C2C(=CC1)=C3C=C4C(C)C3=CC)=C5C=C6C=CC=C(=C(C3F()C6=C(=CC1)C5=CC4=C(C)C3=CC)C3=CC2=C1 306 | C1=CC=C2C=C3C(C3CF=C4)=C4C=C5C(=C2=C2)=C6C=CC=CC6=CC5=C(C3CF=C4)C4=CC3=C(=C2=C2)C2=C1 307 | C1=CC(C2=C)=C2C(5)C(C=C(F)=C3C(=C2=CC)=C4C=CC=C(C2=C)C4=C(5)C(C=C(F)C3=C(=C2=CC)C2=C1 308 | CC1=CC(=CC=C1N2C(=O)C=CC2=O)OC 309 | C1=CC=C2C(C=CC3O)=C3C(4)C((C)(C)=C4C=C5C=CC=CC5=C(C=CC3O)C4=C(4)C((C)(C)C3=CC2=C1 310 | C1=CC(=C(C(=C)N)=C2C(C(=C(C=O1)=C3C=C4C((=S))=CC=C(=C(C(=C)N)C4=C(C(=C(C=O1)C3=CC2=C((=S))1 311 | CC1=CC=C2C=C3C(=CC2=C1)C=CC(=O)C3=OC 312 | C1=CC(CC=C)=C2C(5)C(C=N=C)=C3C(=N2=CC)=C4C=CC=C(CC=C)C4=C(5)C(C=N=C)C3=C(=N2=CC)C2=C1 313 | CC1=CC=C(2=NC(C3C1=C=C(=NC=C(C=C3)C)O)C(=O)C(=O)N2C4=NN=CS4 314 | CC(=CCC(C1=CC(=O)C2=C(C=CC(=C2C1=O)O)O)O)C 315 | CC(CC1=CC=CC=C1NC(=O)C(C(=O)C)N=NC2=C(C=C(C=C2)C3=CC(=C(C=C3)N=NC(C(=O)C)C(=O)NC4=CC=CC=C4C)OC)OC 316 | C1=CC2=C(C(=C1)CNC3=CC=C(C=C3)S(=O)(=O)C4)S(=C(C2=O)C=CC(=C4)Cl 317 | C1=CC=C2C(C=N(=S(C1)=CC=CC2=C(C=N(=S(C1)1 318 | CSCCOC(=O)C1=CC2=C(C=C1)OC(=O)C(=C2)C(=O)OCCS 319 | CC(C1CCC(CC1)CCCCCCCCC2=C(C3=CC=CC=C3C(=O)C2=O)O(CS 320 | C[C-]1[C-]([C-]([C-]([C-]1 OY(N(C4)NC-]([CH2-])[CH2-].[Co] 321 | C1=CC=C2C(N=CC5)=CC=CC2=C(N=CC5)1 322 | C1=C(C=C(C(=C1C(=O)Cl)C(=O)Cl)C(=O)Cl)C(=O)Cl 323 | C1=CC=C2C=CC=CC2=C1S 324 | C1=CC(=C(C)=C2C(N(1O)OC4)=C3C=C4C=CC=C(=C(C)C4=C(N(1O)OC4)C3=CC2=C1 325 | CC(C1=CC(=CC=C1CCO)NC2=C3C(=C(C=C2)O)C(=O)C4=C(C=CC(=C4C3=O)N)O)SP 326 | CSCCOC1=CC=C(C=C1)N=NC2=CC=C(C=C2)N=NC3=C(C=CC(=C3)S(=O)(=O)O)Cl=C 327 | C1=CC(2=C(C=C2N)=C2C=C3C(N)OO)=CC=C(2=C(C=C2N)C3=CC2=C(N)OO)1 328 | C1=CCN=F1NC 2.C3C@@NIF(C,CC=C3)[N+](=O)[O-] 329 | CSC1=CC=C(C=C1)C(=NC2=CC=C(C=C2)[N+](=O)[O-])NCC 330 | CCC1=CC=C2C(=C1)C(=O)C(C2=O)C3=CC=C4C(=CC=C5C4=CC(=CC5=O)S(=O)(=O)O)N3SC 331 | C1=CC=C2C=C3C=C4C=C5C(O=C(N=O3)=CC=CC5=CC4=CC3=CC2=C(O=C(N=O3)1 332 | C1=CC(SC=C)=C2C(5)C(C=N=C)=C3C(=C2=CC)=C4C=CC=C(SC=C)C4=C(5)C(C=N=C)C3=C(=C2=CC)C2=C1 333 | CC1=CC=C2C=C3C=C4C=C5C=C6C=CC=CC6=CC5=CC4=CC3=CC2=C1F 334 | C1=CC(=O)C(=CN=NC2=NC=C(C=C2)N(O)O)C=C1C 335 | C1=CC=C2C(O=CC4)=CC=CC2=C(O=CC4)1 336 | C1=CCN=F1NC 2.C3C@@NIF(C(CC=C3)[N+](=O)[O-] 337 | CCnCC1=C(C(=O)C2=C(C1=O)CC3C4C5=C(CC(N4C)CN3C2CNC(=O)C(C)N)C(=C(C(=C5O)OC)C)Br)OC NC 338 | C1=CC(=C1NO)=C2C=C3C=C4C(CCC49CC)=CC=C(=C1NO)C4=CC3=CC2=C(CCC49CC)1 339 | CCOC1=CC(=CC=C1[N+](=O)[O-])OP(=S)(OC2=CC=C(C=C2)[N+](=O)[O-])OC3=CC=C(C=C3)[N+](=O)[O-]=CC 340 | C1=CC=C2C(C=C(C)=C3C(N=2C((O)=CC=CC3=C(C=C(C)C2=C(N=2C((O)1 341 | C1=CC=C2C(N=C(=O3=C)=C3C=C4C(=O(=C4)=CC=CC4=C(N=C(=O3=C)C3=CC2=C(=O(=C4)1 342 | CCC12CCCC(O1)C3=C(O2)C=C4C(=C3O)C(=O)C5=C(C=C(C=C5C4=O)O)OC 343 | C1=CC=C2C(N=C(=C4)=CC=CC2=C(N=C(=C4)1 344 | CCC1=CC=C2C=C3C=CC=CC3=CC2=C1PC 345 | CC1=CC=C(C=C1)C(=C2C(N)C(=O)C2=N(S3=NC4=C(S3)C=C(C=C4)F)C5=CC(=C(C=C5)OC)OC)O 346 | CCONCC1=C(C=CC(=C1)OP(=S)(OC)OC)N=OC(CO 347 | C1=CC=C2C=C3C=C4C=C5C(N=C(N=N1)=CC=CC5=CC4=CC3=CC2=C(N=C(N=N1)1 348 | C1=CC=C2C(C(2C5)=C3C=C4C=C5C=C6C=CC=CC6=C(C(2C5)C5=CC4=CC3=CC2=C1 349 | C1=CC=C2C(C=N()=CC=CC2=C(C=N()1 350 | COC1=CC=C(C=C1)S(=O)(=O)N=C2C=C(C(=C2(C=C)Cl)Cl)Cl 351 | C1=CC=C2C(C=C(3)=C3C(N53C)(O)=CC=CC3=C(C=C(3)C2=C(N53C)(O)1 352 | C1=CC=C(C=C1)OC2=CC=C(C=C2)N=O 353 | CC1=CC=C2C(=C1)C=CC3=C2C(=O)OC3=OC 354 | C1=CC(C3=NC(C(5)=C2C=C3C=CC=C(C3=NC(C(5)C3=CC2=C1 355 | [Cr] 356 | C1=CC(=CC2=C(C(=C(C=C21)S(=O)(=O)O)N=NC3=C(C=CC(=C3)NC(=O)C(CBr)Br)S(=O)(=O)O)O)NC(=O)C(CBr)Br 357 | C1=CC=C2C=C3C(=N(C3C()=CC=CC3=CC2=C(=N(C3C()1 358 | CN(C)C1=C(C(=O)C2=CC=CC=C2C1=O)Cl 359 | CN(C)CCN1C(=O)C2=C3C(=CC=C4C3=C(C=C2)C(=O)N(C4=O)CCN(C)C)C1=O 360 | CCC1=CC(=C2C(=O)C=C(C(=O)C2=C1C(=O)C(Cl)Cl)C)OC 361 | COC1=CC=CC2=C1C(=O)C3=C(C2=O)C(=CC=C3)OC 362 | C(C1=NC(=NC(=N1)C(Cl)(Cl)Cl)C(Cl)(Cl)Cl)Cl 363 | COC(=O)C1=CC=C(C=C1)N=NC2=C(NC(=S)NC2=O)O 364 | CC1=CC=C2C=C3C=C4C=CC=CC4=CC3=CC2=C1S 365 | C1=CC(=C)C(=)=C2C=C3C=C4C=C5C=CC=C(=C)C(=)C5=CC4=CC3=CC2=C1 366 | CC1C(C(C(OC1OC2CC(CC3=C(C4=C(C(=C23)O)C(=O)C5=C(C4=O)C=CC=C5OC)O)(C(=O)C)C)C)N 367 | C1=CC=C2C(N=N1=C3=O)=CC=CC2=C(N=N1=C3=O)1 368 | CC(=COC(=O)C1=CC=C(S1)[N+](=O)[O-])CNS 369 | C1=CSC2=C1C(=O)C3=C(C2=O)SC(=C3)C=O 370 | CSC1=CC=C2C=C3C=C4C=C5C=C6C=CC=CC6=CC5=CC4=CC3=CC2=C1CC 371 | CC1(=C(C(=O)C(C1=O)(Cl)Cl)Cl)Cl= 372 | C1=CC=C2C(C-C(=N)C4)=C3C=C4C=CC=CC4=C(C-C(=N)C4)C3=CC2=C1 373 | CCOC1=CC=C(C=C1)NC2=CC=CC3=C2C(=O)C4=C(C3=O)C(=CC=C4)NC5=CC=C(C=C5)OCC 374 | CS 375 | CNN(CCC(=O)OC1=C(C=C(C=C1)C=C[N+](=O)[O-])OC).NP 376 | C1=CC=C2C(C=CC3O)=C3C(4)C((S)(C)=C4C=C5C=CC=CC5=C(C=CC3O)C4=C(4)C((S)(C)C3=CC2=C1 377 | C1=CC=C2C=C3C=C4C=C5C=CC=CC5=CC4=CC3=CC2=C1S= 378 | CSN(C1=CC=C2C=C3C=CC=CC3=CC2=C1)N=N 379 | C1=CC=C2C=C3C(=CC(3C)=C4C(=O)(C)O6C)=C5C=CC=CC5=CC4=C(=CC(3C)C3=C(=O)(C)O6C)C2=C1 380 | CCN=CC(CNC1=C2C(=C(C=C1)O)C(=O)C3=CC=CC=C3C2=O)C#N=NC= 381 | C1=CC=C2C=C3C(C3CN=C3)=C4C=C5C(=C2=C2)=C6C=CC=CC6=CC5=C(C3CN=C3)C4=CC3=C(=C2=C2)C2=C1 382 | C1=CC=C2C=C3C=CC=CC3=CC2=C1S 383 | COC1=CC(=CC(=C1O)OC)/C=C/2\C(=O)NN(C2=O)C3=C(=C(C=C3)Cl 384 | C1=CC=C2C=C3C=C4C=CC=CC4=CC3=CC2=C1CCSC 385 | C1=CC=C2C=C3C(C3CF1C1)=C4C=C5C(=C2=C2)=C6C=CC=CC6=CC5=C(C3CF1C1)C4=CC3=C(=C2=C2)C2=C1 386 | C1=CC(=C(C(=C)N)=C2C(C(=C(C=O1)=C3C=C4C(8=S))=CC=C(=C(C(=C)N)C4=C(C(=C(C=O1)C3=CC2=C(8=S))1 387 | C1=CC=C2C(C=N1)=C3C(=C(=C(N)=CC=CC3=C(C=N1)C2=C(=C(=C(N)1 388 | C1=CC=C2C(C=CC3O)=C3C(4)C()S((C)=C4C=C5C=CC=CC5=C(C=CC3O)C4=C(4)C()S((C)C3=CC2=C1 389 | C1=CC=C2C=C3C=C4C=C5C(4=C0)=CC=CC5=CC4=CC3=CC2=C(4=C0)1 390 | C1=CC=C2C=C3C=C4C=C5C=C6C(=O/Cn(N5)=CC=CC6=CC5=CC4=CC3=CC2=C(=O/Cn(N5)1 391 | C1=CC(=C(=O1)=C2C(=C2=C4)=C3C(C=C2)F)=C4C=CC=C(=C(=O1)C4=C(=C2=C4)C3=C(C=C2)F)C2=C1 392 | CCC1=CC=C(C=C1)N=NC2=CC=C(C=C2)N3C(=O)C=CC3=OCC 393 | CC1=CC=C(C=C1)C(=C2C(C)C(=O)S2=N(S3=NC4=C(S3)C=C(C=C4)F)C5=CC(=C(C=C5)OC)OC)O 394 | C1=CC=C2C(=O)O(=O1)=C3C=C4C=C5C=CC=CC5=C(=O)O(=O1)C4=CC3=CC2=C1 395 | C1=CC=C2C(N=CC(2=C)=C3C=C4C(=Cl=C41C4)=C5C=C6C=CC=CC6=C(N=CC(2=C)C5=CC4=C(=Cl=C41C4)C3=CC2=C1 396 | C[C-]1[C-]([C-]([C-]([C-]1 OI)N)C4)NC-]([CH2-])[CH2-].[Co] 397 | C1=CC=C2C=C3C=C4C=C5C=C6C(N=N1)=CC=CC6=CC5=CC4=CC3=CC2=C(N=N1)1 398 | C1=CC(C)=NC(C(1)=C2C=C3C=CC=C(C)=NC(C(1)C3=CC2=C1 399 | CSC1=CC=C2C(=C1)C(=O)C(C2=O)C3=CC=C4C(=CC=C5C4=CC(=CC5=O)S(=O)(=O)O)N3SC 400 | C1=CC((=C)O=C()=C2C=C3C=C4C=C5C=CC=C((=C)O=C()C5=CC4=CC3=CC2=C1 401 | C1=CC=C2C=C3C=C4C(NC(C66C2)=C5C=CC=CC5=CC4=CC3=C(NC(C66C2)C2=C1 402 | C1=CC=C2C=C3C=C4C=C5C(O=C(O=O1)=CC=CC5=CC4=CC3=CC2=C(O=C(O=O1)1 403 | C1=CCN=F1NC 2/C3C@@NIF(C(CC=C3)[N+](=O)[O-] 404 | C1=CC(=O=C)=C2C=C3C=CC=C(=O=C)C3=CC2=C1 405 | C1=CC2=C(C(=CC(=C2C=C1N)S(=O)(=O)O)O)N=NC3=C(C4=C(C=C3)C(=CC(=C4N)S(=O)(=O)O)S(=O)(=O)O)O 406 | C1=CC=C2C(C=C(O3)=C3C(S(=C)=C4C(O6=C)=CC=CC4=C(C=C(O3)C3=C(S(=C)C2=C(O6=C)1 407 | C1=CC(C3=C)=C2C(5)C(C=N=C)=C3C(=C2=CC)=C4C=CC=C(C3=C)C4=C(5)C(C=N=C)C3=C(=C2=CC)C2=C1 408 | C1=CC(=C(=N1)=C2C(=C2=C4)=C3C(C=C6)F)=C4C=CC=C(=C(=N1)C4=C(=C2=C4)C3=C(C=C6)F)C2=C1 409 | C1=CC=C2C(N3C4C3N(C)=C3C(CO)C)=C4C(=C()SC)=C5C=CC=CC5=C(N3C4C3N(C)C4=C(CO)C)C3=C(=C()SC)C2=C1 410 | C1=CC=C2C=C3C=C4C=C5C=C6C(N=N5)=CC=CC6=CC5=CC4=CC3=CC2=C(N=N5)1 411 | C1=CC=C2C=C3C(=NO5)=C4C=CC=CC4=CC3=C(=NO5)C2=C1 412 | C1=CC(=C=N(=C)=C2C=CC=C(=C=N(=C)C2=C1 413 | CSC1=C(C=C(C(=C1O)O)O)C2=C(C=C3C(=CC(=O)C=C3O2)O)O=C 414 | CC1=CC(=C(C=C1Cl)S(=O)(=O)Cl)ClP 415 | C1=CC(2C=C)=C2C=C3C(N23=N4)=CC=C(2C=C)C3=CC2=C(N23=N4)1 416 | C1=CC=C2C=C3C=C4C(C(S5N)=C5C=C6C(C=C3)=CC=CC6=CC5=CC4=C(C(S5N)C3=CC2=C(C=C3)1 417 | CC1=CC(=O)OC2=C1C(=O)C3=C(C2=O)OC=C3O 418 | C1=CC=C2C(C$2C5)=C3C=C4C=C5C=C6C=CC=CC6=C(C$2C5)C5=CC4=CC3=CC2=C1 419 | C1=CC=C2C=C3C(=CC(3C)=C4C(=O)(C)N6C)=C5C=CC=CC5=CC4=C(=CC(3C)C3=C(=O)(C)N6C)C2=C1 420 | C1=CC=C2C=C3C=C4C=C5C=C6C=CC=CC6=CC5=CC4=CC3=CC2=C1PN 421 | C1=CC(=C(OO)=C2C=C3C=C4C(CCS0=CC)=CC=C(=C(OO)C4=CC3=CC2=C(CCS0=CC)1 422 | CNN(CCC(=O)OC1=C(C=C(C=C1)C=C[N+](=O)[O-])OC(=NP 423 | CC(C1=CC=C2C=CC=CC2=C1(CS 424 | CC1=C(C=CC(=C1)C2=CC(=C(C=C2)N=NC3=CC(=C(C=C3)O)C(=O)O)O[O-](4C(=NN(C4=O)C5=CC=C(C=C5)S(=O)(=O)O)C 425 | C1=CC=C2C(N=CC(2(C)=C3C=C4C(=Cl=C41C4)=C5C=C6C=CC=CC6=C(N=CC(2(C)C5=CC4=C(=Cl=C41C4)C3=CC2=C1 426 | CCC1=CC(=C(C(=C1CNC(=O)C2=CC=CC=C2)C)NC3=CC(=C(C4=C3C(=O)C5=CC=CC=C5C4=O)N)S(=O)(=O)O)CS 427 | C1=CC=C2C=C3C=C4C(SC(C66C2)=C5C=CC=CC5=CC4=CC3=C(SC(C66C2)C2=C1 428 | CNN(CCC(=O)OC1=C(C=C(C=C1)C=C[N+](=O)[O-])OC)=NP 429 | C1=CC=C2C=CC=CC2=C1Cl== 430 | CCOC1=CC(=C2N=NN=N2)C=C(C1=O)N(O)O 431 | CCOC1=CC(=O)C=CC1=OC 432 | CCN(C1=CC2=[N+](C=CC(=C2C=C1C(=O)O)[N+](=O)[O-])[O-]C=CC 433 | C1=CC(=O(NS(2O5)=C2C=C3C(CC(C(C)=CC=C(=O(NS(2O5)C3=CC2=C(CC(C(C)1 434 | CCC1=C(C=CC(=C1)N(CCO)CCO)N=NC2=C(C=C(C=C2Cl)[N+](=O)[O-])[N+](=O)[O-]F 435 | CC1=OCC2=C1C(=O)C(=O)C3=C2C=CC4=C3CCCC4(C)O 436 | C1=CC=C2C(C=N1)=CC=CC2=C(C=N1)1 437 | C1=CC=C2C=CC=CC2=C1NS 438 | C-CN1C(=O)C2=C3C(=C(C=C2)Br)C=CC=C3C1=OCF 439 | C1=CC=C2C(C=3C(=C)3)=C3C=C4C=C5C=CC=CC5=C(C=3C(=C)3)C4=CC3=CC2=C1 440 | C1=CC=C2C=C3C((2O)C)=C4C=CC=CC4=CC3=C((2O)C)C2=C1 441 | C1=CCN=N1NC 2.C3C@@N(F(C(CC=C3)[N+](=O)[O-] 442 | C1=CC=C2C(C=N0)=C3C(=S(=C.N)=CC=CC3=C(C=N0)C2=C(=S(=C.N)1 443 | CC1=CC(=C(C=C1)N=NC2=C3C=CC(=CC3=CC(=C2O)S(=O)(=O)O)S(=O)(=O)O)C 444 | C1=CC=C2C=C3C=C4C=C5C(N=C(N=O3)=CC=CC5=CC4=CC3=CC2=C(N=C(N=O3)1 445 | COC1=CC=CC(=C1)/C=C/2\C(=O)C3=C(O2)C=C(C=C3)OCC(=O)N 446 | C1=CC=C2C(C=N1)=C3C(=C(=C/N)=CC=CC3=C(C=N1)C2=C(=C(=C/N)1 447 | C1=CC=C2C(1=N()=CC=CC2=C(1=N()1 448 | C1=CC(3C=C)=C2C=C3C(N23=N4)=CC=C(3C=C)C3=CC2=C(N23=N4)1 449 | C1=CC(=C=C(4)=C2C=C3C=C4C=C5C=CC=C(=C=C(4)C5=CC4=CC3=CC2=C1 450 | CCC12CCC3C(C1CCC2=O)CCC4=C3C=CC(=C4)OC(=O)C5=CC=C(C=C5)[N+](=O)[O-]S 451 | C1=CC=C2C(=C1)C=C(C(=O)O2)C(=O)NC3=CC4=NN(N=C4C=C3)C5=CC=C(C=C5)S 452 | CCCC1=CC=C2C=CC=CC2=C1(CO 453 | CCOC1=CC=C(C=C1)S(=O)(=O)OC2=C(C3=C(C(=C2)O)C(=O)C4=CC=CC=C4C3=O)NC 454 | CCCCN(CC)C1=CC(=C(C=C1)N=C2C=C(C(=O)C3=CC=CC=C32)C(=O)NCCC4=CC=CC=C4NC(=O)C)C=C 455 | CCOC1=CC=C2C(=C1)C(=O)C3=C(C2=O)C(=C(C=C3O)O)O(SC 456 | C1=CC=C2C=C3C=C4C=CC=CC4=CC3=CC2=C1CC 457 | C1=CC=C2C(N=CC(=C)3)=C3C=C4C=C5C=CC=CC5=C(N=CC(=C)3)C4=CC3=CC2=C1 458 | C1=CC=C2C=C3C=C4C(3C(C66C2)=C5C=CC=CC5=CC4=CC3=C(3C(C66C2)C2=C1 459 | C1=CC(=C=C)=C2C=CC=C(=C=C)C2=C1 460 | C1=CC=C2C=C3C=C4C(C4=OS3C)=C5C=CC=CC5=CC4=CC3=C(C4=OS3C)C2=C1 461 | C1=CC=C2C(3=N()=CC=CC2=C(3=N()1 462 | CCCNCC(C)(C)OC(=O)NCC(=O)OC1=CC=C(C=C1)[N+](=O)[O-]CCN= 463 | CC(=CC(=O)C(=CC1=CC(=CC=C1)[N+](=O)[O-])C(=O)C(CCF 464 | CCCOCCCNC1=CC(=C2C(=C1O)C(=O)C3=CC=CC=C3C2=O)OPN 465 | C1=CC(C(1)O-)=C2C=C3C(=(C4)NC)=CC=C(C(1)O-)C3=CC2=C(=(C4)NC)1 466 | C1=CC(=S=N(=C)=C2C=CC=C(=S=N(=C)C2=C1 467 | C1=CC=C2C(2=C(=O)3)=CC=CC2=C(2=C(=O)3)1 468 | CCCCN(CCNC(=O)C)C1=CC(=C(C=C1)N=NC2=NC3=C(S2)C=C(C=C3)C)CFC 469 | CC1=C(C(=O)C2=C(C1=O)N3CC4C(C3(C2COC(=O)N)OC)N4)OC(C)C 470 | C1=CCN=F1NC 2/C3C@DNHF(C(CC=C3)[N+](=O)[O-] 471 | 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C1=CC=C2C=CC=CC2=C1SO 712 | C1=CC=C2C(C=C()=CC=CC2=C(C=C()1 713 | C1=CC(=C)C(=)=C2C=C3C=CC=C(=C)C(=)C3=CC2=C1 714 | CC1=CC2=CC(=O)C3=C(C2=C(C1=O)O)C(=O)C4=CC=CC(=O)C4=C3O 715 | CCCOC(=O)NC1=C(C(=O)C(=C(C1=O)C2CC2)NC(=O)OCC)C3CC3C 716 | C1=CC=C2C=C3C=C4C=CC=CC4=CC3=CC2=C1CCO 717 | C1=CC=C2C=C3C(C3=C(C)=C4C=C5C=C6C=CC=CC6=CC5=C(C3=C(C)C4=CC3=CC2=C1 718 | CC1=CC=C2C=C3C=C4C=CC=CC4=CC3=CC2=C1N 719 | C1=CC=C2C((=C(=O)3)=CC=CC2=C((=C(=O)3)1 720 | C1=CC=C2C=C3C=C4C=C5C=C6C=CC=CC6=CC5=CC4=CC3=CC2=C1 721 | C=C1=C(C=C(C(=C1[N+](=O)[O-])O)[N+](=O)[O-])ClCC 722 | C1=CC(=C(=O)=C2C=C3C=C4C=C5C((C)N)CC)=CC=C(=C(=O)C5=CC4=CC3=CC2=C((C)N)CC)1 723 | C1=CC=C2C=C3C(=C(=C)=C4C=C5C(C5)C4)=C6C=CC=CC6=CC5=C(=C(=C)C4=CC3=C(C5)C4)C2=C1 724 | C1=CC=C2C=C3C=C4C=C5C(4=C2)=CC=CC5=CC4=CC3=CC2=C(4=C2)1 725 | C1=CC(=C1(O=OC)=C2C(=OC2(C)=C3C=C4C(2=OS)C4)=CC=C(=C1(O=OC)C4=C(=OC2(C)C3=CC2=C(2=OS)C4)1 726 | C1=CC=C2C(N=N1=C2=O)=CC=CC2=C(N=N1=C2=O)1 727 | C1=CC(=C(3=C(N)=C2C=C3C=C4C(O5C)OO)=C5C=C6C=CC=C(=C(3=C(N)C6=CC5=CC4=C(O5C)OO)C3=CC2=C1 728 | C1=CCN=F1NC 2/C3C@@N(F(C(CC=C3)[N+](=O)[O-] 729 | C1=CC=C2C(C=C5OC)=C3C(\==C)=C4C(N6=C)=CC=CC4=C(C=C5OC)C3=C(\==C)C2=C(N6=C)1 730 | CCC1CC(=O)N(C1=O)OC(=O)C2=CC(=CC=C2)N3C(=O)C=CC3=OOS 731 | C1=CC=C2C=CC=CC2=C1C(== 732 | CCC1=C(C(=O)C2=C(C1=O)N3CC4C(C3(C2COC(=O)N)OC)N4)OCCC(C)CC 733 | CCCCN(CCC)C1=CC(=C(C=C1)N=NC2=NN=C(S2)SCC)NC(=O)C= 734 | C1=CC(=O)C(=CC1=N)N(O)O 735 | CC(C1=CC(=CC=C1C(F)(F)F)[N+](=O)[O-]=FP 736 | C1=CC=C2C(=C1)C=CC(=C2N=NC3=CC=C(C=C3)N=NC4=CC=C(N=C4=S(=O)(=O)O)O 737 | [C-]#[C].[Ce] 738 | C1=CC(O(=O)=C2C=CC=C(O(=O)C2=C1 739 | C1=CC=C2C=C3C(=C(3)C(C2)=C4C=C5C=CC=CC5=CC4=C(=C(3)C(C2)C3=CC2=C1 740 | C1=CC=C2C=C3C=CC=CC3=CC2=C1 741 | C1=CC=C2C(C=N2)=C3C(=C(=C4O)=CC=CC3=C(C=N2)C2=C(=C(=C4O)1 742 | CC1=C(C(=O)C(=CC1=NOC(=O)C2=CC=CC=C2) p)N 743 | C1=CC(C2=C(=O)=C2C=C3C(=O)O)=C4C=CC=C(C2=C(=O)C4=CC3=C(=O)O)C2=C1 744 | CCC1=CC(=C(C=C1Cl)S(=O)(=O)O)N=NC2=C(C=CC3=CC=CC=C32)OC 745 | CSC1=CC(=CC=C1C2=CC=C(C=C2)C(=O)Cl)C(=O)ClNS 746 | C1=CC((C)C(C5)=C2C(C=CC)=C3C((OC)OC)=C4C=C5C=C6C=CC=C((C)C(C5)C6=C(C=CC)C5=C((OC)OC)C4=CC3=CC2=C1 747 | CC1=OSC2=C1C(=O)C(=O)C3=C2C=CC4=C3CCCC4(C)O 748 | C1=CC=C2C=C3C(2(=N)C()=CC=CC3=CC2=C(2(=N)C()1 749 | C1=CC(=N1C)(F)=C2C=C3C=C4C(C3=C(C==C)=CC=C(=N1C)(F)C4=CC3=CC2=C(C3=C(C==C)1 750 | C[C-]1[C-]([C-]([C-]([C-]1 OY)N(C4)NC-]([CH2-])[CH2-].[Co] 751 | CC1=CC=C(C=C1)CCCS(=O)(=O)C2=C(C(=C(C(=C2Cl)Cl)C#N)Cl)ClS 752 | C1=CC=C2C=C3C((=O)C)=C4C=CC=CC4=CC3=C((=O)C)C2=C1 753 | C1=CC(=S(=C5)=C2C=C3C=C4C=C5C=C6C=CC=C(=S(=C5)C6=CC5=CC4=CC3=CC2=C1 754 | CCCCCOC1=C(C(=O)C2=C(C1=O)C(C3(N2CC4C3N4)OC)COC(=O)N)C*SN 755 | C1=CC2=C(C(=C1=C(C(=O)C(=O)C3=C(C2=O)C=CC=C3O 756 | COC(=O)C1=CC=C(C=C1)O=NC2=C(NC(=S)NC2=O)O 757 | CCC(C1CCC(C(C1)NC(=O)N(CCCl)N=O)Cl)NCC 758 | C1=CC=C2C(=C1)C(=O)C3=C(C=CC(=C3C2=O)O)NCCNCCO 759 | C1=CC(=C5(C1)C)=C2C=C3C=C4C(N9C(S4)O)=C5C=CC=C(=C5(C1)C)C5=CC4=CC3=C(N9C(S4)O)C2=C1 760 | CC-C1=CC(=CC=C1N2C(=O)C(=C(N2)C(=O)O)C=CC=CC=C3C(=NN(C3=O)C4=CC=C(C=C4)S(=O)(=O)O)C(=O)O)S(=O)(=O)OF 761 | C1=CC=C2C(1=N()=C5)=C3C=C4C=CC=CC4=C(1=N()=C5)C3=CC2=C1 762 | C1=CC=C2C(C=C(O1)=C3C(C(=C)=C4C(O)=C)=CC=CC4=C(C=C(O1)C3=C(C(=C)C2=C(O)=C)1 763 | C(C(Cl)(Br)(Br)BrCC 764 | C1=CC=C2C(C1C4C5N(C)=C3C(CC(C)=C4C(=O#CSC)=C5C=CC=CC5=C(C1C4C5N(C)C4=C(CC(C)C3=C(=O#CSC)C2=C1 765 | CCOC1=CC=C(C=C1)N=NC2=CC=C(C=C2)N=NC3=CC4=CC(=CC(=C4C=C3)S(=O)(=O)O)S(=B)C 766 | CC1=CC=C2C=C(C=CC2=C1)C=C(C#N)C#NC 767 | CC((C1=CC=C2C=C3C=C4C=C5C=C6C=CC=CC6=CC5=CC4=CC3=CC2=C1=)NC 768 | C1=CC=C2C(C=C4O3)=C3C=C4C=CC=CC4=C(C=C4O3)C3=CC2=C1 769 | C1=CC(C3=NC)C(5)=C2C=C3C=CC=C(C3=NC)C(5)C3=CC2=C1 770 | CCOC1=CC=CC=C1C2C(=CC3=C(O2)C=CC(=C3)Br)[N+](=O)[O-]S 771 | C1=CC=C2C=C3C(SS(F)=C4C(=C(NC)=C5C=C6C=CC=CC6=CC5=C(SS(F)C4=C(=C(NC)C3=CC2=C1 772 | C1=CC(3=C(C=C2N)=C2C=C3C(N(OO)=CC=C(3=C(C=C2N)C3=CC2=C(N(OO)1 773 | CCN(C1=CC2=[N+](C=CC(=C2C=C1C(=O)O)[N+](=O)[O-])[O-]C)CC 774 | C1=CC=C2C(=C1)C(=O)C(=C2C3=O)C=C(C=C3)CO 775 | C1=CC=C2C(=O)C(7)=CC=CC2=C(=O)C(7)1 776 | CON(CCC(=O)OC1=C(C=C(C=C1)C=C[N+](=O)[O-])OC).NP 777 | C1=CC=C2C(=C1)C(=O)C(=C3C2=O)C=C(C=C3)CO 778 | C1=CC=C2C(O=N1)=CC=CC2=C(O=N1)1 779 | CC(=O)OC1=CC=C(C=C1)/C=C/2\C(=O)C3=CC=CC=C3S2 780 | C1=CC=C2C(1=N((=C5)=C3C=C4C=CC=CC4=C(1=N((=C5)C3=CC2=C1 781 | CCCCCCCCCCCCC1=C(C(=O)C=C(C1=O)O)O 782 | C=CC(CCOC1=C(C2=C(C(=C1)O)C(=O)C3=CC=CC=C3C2=O)N)O=P 783 | C1=CC()=C(O=C()=C2C=C3C=C4C=C5C=CC=C()=C(O=C()C5=CC4=CC3=CC2=C1 784 | C1=CC=C2C=C3C(C3CN=C1)=C4C=C5C(=O2=C2)=C6C=CC=CC6=CC5=C(C3CN=C1)C4=CC3=C(=O2=C2)C2=C1 785 | C1=CC=C2C=C3C=C4C=C5C(O=C(O=N1)=CC=CC5=CC4=CC3=CC2=C(O=C(O=N1)1 786 | C1=CC=C2C=CC=CC2=C1CNC 787 | CCOCC1=CC=C(C=C1)S(=O)(=O)NC2=CC=CC3=C2C(=O)C4=C(C3=O)C(=CC=C4)Cl=CS 788 | C1=CC=C2C(2-C(=N)C4)=C3C=C4C=CC=CC4=C(2-C(=N)C4)C3=CC2=C1 789 | C1=CC=C2C=C3C(C3CN1C3)=C4C=C5C(=C2=C2)=C6C=CC=CC6=CC5=C(C3CN1C3)C4=CC3=C(=C2=C2)C2=C1 790 | CNCC1=C(C(=O)C2=CC=CC=C2C1=O)CC=C(C)CCCC(C)CCCC(C)CCCC(C)C 791 | C=C1=C(C=C(C(=O)C1=N)Cl)N(O)OOP 792 | C1=COC(=C1)/C=C/C(=O)NC2=C(C(=CS2)C3=CC=CS3)C(=O)O 793 | C1=CC=C2C=C3C=C4C=C5C=C6C=CC=CC6=CC5=CC4=CC3=CC2=C1= 794 | CC1=CC=C2C=CC=CC2=C1C 795 | C1=CC=C2C=C3C(=NN5)=CC=CC3=CC2=C(=NN5)1 796 | -------------------------------------------------------------------------------- /Obtain_smiles/get_final_structures.py: -------------------------------------------------------------------------------- 1 | # ------------------------------------------------------------------- 2 | import numpy as np 3 | import scipy as sc 4 | import sys, string, os, glob 5 | import pickle 6 | # ------------------------------------------------------------------- 7 | def get_filesize(): 8 | # ------------------------------------------------------------------- 9 | fname = 'Generated_unique_smiles.smi' 10 | with open(fname, 'r') as fp: 11 | smiles_data = fp.read().splitlines() 12 | 13 | files = glob.glob('*.svg') 14 | 15 | considered_files = [] 16 | considered_smiles = [] 17 | 18 | for d in range(0, len(files)): 19 | f = files[d] 20 | get_size = os.path.getsize(f) 21 | 22 | if get_size > 0: 23 | indx = np.int_(f[:-4]) 24 | considered_files.append(f) 25 | smiles = str(smiles_data[indx]).strip(' ') 26 | considered_smiles.append(smiles) 27 | else: 28 | cmd = 'rm ' + f 29 | os.system(cmd) 30 | 31 | print(len(considered_files)) 32 | 33 | 34 | with open('Considered_smiles.smi', 'w') as fp: 35 | for i in range(0, len(considered_files)): 36 | fp.write("{}\n".format(considered_smiles[i])) 37 | 38 | 39 | 40 | if __name__ == "__main__": 41 | 42 | get_filesize() 43 | 44 | -------------------------------------------------------------------------------- /Obtain_smiles/validate_structures.py: -------------------------------------------------------------------------------- 1 | # ------------------------------------------------------------------- 2 | import numpy as np 3 | import scipy as sc 4 | import sys, string, os 5 | # ------------------------------------------------------------------- 6 | def validate_structures(fname): 7 | # ------------------------------------------------------------------- 8 | with open(fname, 'r') as fp: 9 | smiles_data = fp.read().splitlines() 10 | # ------------------------------------------------------------------- 11 | command = "obabel -:" 12 | for i in range(0, len(smiles_data)): 13 | smiles = str(smiles_data[i]).strip(' ') 14 | 15 | s = '"' + smiles + '"' 16 | op = str(i)+'.svg' 17 | cmd = command + s + ' -O ' + op 18 | 19 | try: 20 | os.system(cmd) 21 | except: 22 | print('Null') 23 | #print(cmd) 24 | 25 | if __name__ == "__main__": 26 | 27 | validate_rbm('Generated_unique_smiles.smi') 28 | 29 | -------------------------------------------------------------------------------- /Obtain_unique/postprocess_smiles.py: -------------------------------------------------------------------------------- 1 | # ------------------------------------------------------ 2 | import numpy as np 3 | import scipy as sc 4 | import sys, string, os 5 | import glob 6 | # ------------------------------------------------------ 7 | def obtain_unique_smiles(fname, smiles_set): 8 | # ------------------------------------------------------ 9 | with open(fname, 'r') as fp: 10 | smiles_data = fp.read().splitlines() 11 | # ------------------------------------------------------ 12 | smiles = [] 13 | for i in range(0, len(smiles_data)): 14 | smiles.append(str(smiles_data[i]).strip(' ')) 15 | # ------------------------------------------------------ 16 | #smiles = list(set(smiles)) 17 | 18 | smiles_set_diff = list(set(smiles) - set(smiles_set)) 19 | 20 | #print(set(smiles) - set(smiles_set)) 21 | 22 | for i in range(0, len(smiles_set_diff)): 23 | smiles_set.append(smiles_set_diff[i]) 24 | # ------------------------------------------------------ 25 | return smiles_set 26 | # ------------------------------------------------------- 27 | if __name__ == '__main__': 28 | 29 | smiles = [] 30 | 31 | all_files = glob.glob('*.smi') 32 | 33 | 34 | for f in range(0, len(all_files)): 35 | fname = all_files[f] 36 | print('File No. = ', f) 37 | smiles = obtain_unique_smiles(fname, smiles) 38 | 39 | 40 | with open('Generated_unique_smiles.smi', 'w') as fp: 41 | for j in range(0, len(smiles)): 42 | st = smiles[j] + "\n" 43 | fp.writelines("{}".format(st)) 44 | 45 | 46 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # SLAMDUNCS 2 | 3 | This repository contains codes for a deep learning inverse prediction framework "SLAMDUNCS: Structure Learning for Attribute-driven Materials Design Using Novel Conditional Sampling (SLAMDUNCS)" for efficient and accurate prediction of molecules exhibiting target properties. Databases used for training and testing the framework are also made available in this repository. 4 | 5 | Details of the methodology and the results are presented in a publication: "A deep learning Bayesian framework for attribute driven inverse materials design". 6 | 7 | 8 | Steps for code usage: 9 | 1) Run bayes_pubchem.py 10 | 2) Move files "Generated_smiles_" to the folder "Obtain_unique" 11 | 3) Run postprocess_smiles.py 12 | 4) Move "Generated_unique_smiles.smi" to the folder "Obtain_smiles" 13 | 5) Run validate_structures.py 14 | 6) Run get_final_structures.py 15 | 7) File "Considered_smiles.smi" contains all the valid molecular structures predicted by SLAMDUNCS. 16 | 8) For final screening, use forward prediction to ensure structures exhibit desired attributes. Use DFT to validate properties for selected structures. 17 | 18 | Database used for training and testing the models is available in the files : a) Molecular_data_pubchem.jsn b) IQR_screened_redox_homolumo.jsn 19 | 20 | Usage of the code is governed by "Samsung Publication License". 21 | -------------------------------------------------------------------------------- /Samsung Publication License.docx: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/piyushtagade/SLAMDUNCS/4227a92121091b017edde679a615df94690cc0ae/Samsung Publication License.docx -------------------------------------------------------------------------------- /anova.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | """ 3 | Created on Tue May 30 10:53:32 2017 4 | 5 | @author: piyush.t 6 | """ 7 | 8 | ''' Functions for analysis of variance ''' 9 | import numpy as np 10 | def rsquare(y, f): 11 | ''' Function for obtaining R^2 of fitted curve ''' 12 | #indx = np.where(f[:,0] > 0)[0] 13 | #f = f[indx,:]; y = y[indx,:] 14 | return (1.0 - (np.sum(np.square(y-f), axis=0)/(np.sum(np.square(y-np.mean(y, axis=0)), axis=0)))) 15 | 16 | def rmse(y, f): 17 | ''' Function for obtaining root mean squared error of fitted curve ''' 18 | #indx = np.where(f[:,0] > 0)[0] 19 | #f = f[indx,:]; y = y[indx,:] 20 | return np.sqrt(np.mean(np.square(y - f), axis = 0)); 21 | 22 | def rmse_accuracy(y, f): 23 | ''' Function for obtaining rmse accuracy ''' 24 | err = np.sqrt(np.mean(np.square((y - f)/y), axis = 0)); 25 | return (1.0 - err)*100 26 | 27 | -------------------------------------------------------------------------------- /bayes_pubchem.py: -------------------------------------------------------------------------------- 1 | # ----------------------------------------------------------------------------- 2 | # Deep Learning Bayesian framework for inverse materials design 3 | # ----------------------------------------------------------------------------- 4 | import scipy as sc 5 | import numpy as np 6 | # ----------------------------------------------------------------------------- 7 | from pprint import pprint 8 | # Load the dataset 9 | #from deep_learning import * 10 | from deep_neural_network import * 11 | from deep_bayesian_inference import * 12 | from process_smiles import * 13 | from pubchem_data import * 14 | import pickle 15 | from anova import * 16 | import time 17 | from train_rbm import * 18 | from train_dnn import * 19 | 20 | # ----------------------------------------------------------------------------- 21 | # For ensuring that same random number is always generated 22 | # ----------------------------------------------------------------------------- 23 | np.random.seed(105) 24 | 25 | mxl = 100 26 | # ----------------------------------------------------------------------------- 27 | # Training the rbm for unsupervised learning of molecular structures 28 | # ----------------------------------------------------------------------------- 29 | pubchem_data_filename = 'Molecular_data_pubchem' 30 | train_rbm(pubchem_data_filename, max_smiles_length=100) 31 | with open('rbm_trained.pkl', 'rb') as fp: 32 | rbm = pickle.load(fp) 33 | # ----------------------------------------------------------------------------- 34 | # ----------------------------------------------------------------------------- 35 | # Training the dnn for semi-supervised learning of structure-property 36 | # correlation 37 | # ----------------------------------------------------------------------------- 38 | properties_data_filename = 'IQR_screened_redox_homolumo' 39 | train_dnn(pubchem_data_filename, properties_data_filename, 40 | max_smiles_length=100) 41 | 42 | with open('dnn_trained.pkl', 'rb') as fp: 43 | dnn = pickle.load(fp) 44 | 45 | # ----------------------------------------------------------------------------- 46 | # Creating a Deep Bayesian Inference object 47 | # ----------------------------------------------------------------------------- 48 | 49 | dbi = Deep_Bayesian_Inference(dnn, rbm) 50 | 51 | max_smiles_length = 100 52 | 53 | pah_smiles = [] 54 | fixed_smiles = [] 55 | copy_loc_indes = [] 56 | 57 | # ----------------------------------------------------------------------------- 58 | # Some sample smiles for initialization of Markov Chain 59 | # ----------------------------------------------------------------------------- 60 | pah_smiles.append('C1=CC2=C3C(=CC=C4C3=C1C5=C6C4=CC=C7C6=C(C=C5)C(=O)NC7=O)C(=O)NC2=O') 61 | pah_smiles.append('O=C1NC(=O)C2=C3C1=CC=C1C(=O)NC(=O)C(C=C2)=C31') 62 | 63 | # ----------------------------------------------------------------------------- 64 | # Number of chains to run 65 | # ----------------------------------------------------------------------------- 66 | num_parallel_chains = 5000 67 | # ----------------------------------------------------------------------------- 68 | # Randomly creating initial state of the Markov Chain 69 | # ----------------------------------------------------------------------------- 70 | ini_smiles = [] 71 | fixed_smiles_index = [] 72 | copy_loc_index = [] 73 | for par in range(0, num_parallel_chains): 74 | if np.random.random(1) > 0.00: 75 | i_pah = np.random.choice(len(pah_smiles), 1)[0] 76 | 77 | #i_pah = 0 78 | if i_pah < 0: 79 | if np.random.random(1) > 0.05: 80 | side_add = 'both' 81 | else: 82 | side_add = 'one' 83 | if np.random.random(1) > 0.05: 84 | symmetry = True 85 | else: 86 | symmetry = False 87 | print('Before initialize') 88 | 89 | updated_smiles, fixed_index, copy_loc = initialize_smiles(pah_smiles[i_pah], symmetry=symmetry, side_add = side_add) 90 | print(updated_smiles) 91 | else: 92 | 93 | if i_pah == 1: 94 | i_rand = np.random.random(1) 95 | 96 | if i_rand < 0.3: 97 | updated_smiles, fixed_index, copy_loc = initialize_smiles_dimer(pah_smiles[1], symmetry=True, add_side = 1) 98 | if i_rand >= 0.3 and i_rand < 0.6: 99 | updated_smiles, fixed_index, copy_loc = initialize_smiles_dimer(pah_smiles[1], symmetry=True, add_side = 2) 100 | if i_rand >= 0.6 and i_rand < 0.9: 101 | updated_smiles, fixed_index, copy_loc = initialize_smiles_dimer(pah_smiles[1], symmetry=True, add_side = 0) 102 | if i_rand > 0.9: 103 | updated_smiles, fixed_index, copy_loc = initialize_smiles_dimer(pah_smiles[1], symmetry=False, add_side = 2) 104 | 105 | else: 106 | i_rand = np.random.random(1) 107 | if i_rand < 0.3: 108 | updated_smiles, fixed_index, copy_loc = initialize_smiles_dimer(pah_smiles[0], symmetry=True, add_side = 1, rings=2) 109 | if i_rand >= 0.3 and i_rand < 0.6: 110 | updated_smiles, fixed_index, copy_loc = initialize_smiles_dimer(pah_smiles[0], symmetry=True, add_side = 2, rings=2) 111 | if i_rand >= 0.6 and i_rand < 0.9: 112 | updated_smiles, fixed_index, copy_loc = initialize_smiles_dimer(pah_smiles[0], symmetry=True, add_side = 0, rings=2) 113 | if i_rand > 0.9: 114 | updated_smiles, fixed_index, copy_loc = initialize_smiles_dimer(pah_smiles[0], symmetry=False, add_side = 2, rings=2) 115 | 116 | else: 117 | if np.random.rand(1) > 0.0: 118 | i_pah = np.random.choice(len(indx_lumo), 1)[0] 119 | print(i_pah) 120 | updated_smiles, fixed_index, copy_loc = initialize_smiles(smiles1[indx_lumo[i_pah]], symmetry=False, side_add = 'both') 121 | else: 122 | i_pah = np.random.choice(len(smiles_intrinsic), 1)[0] 123 | #updated_smiles, fixed_index, copy_loc = initialize_smiles(smiles_intrinsic[i_pah], symmetry=False, side_add = 'none') 124 | updated_smiles = smiles_intrinsic[i_pah] 125 | change_index = [] 126 | try: 127 | cstart = np.random.choice(len(updated_smiles)-20, 1, replace=False) 128 | except: 129 | cstart = 0 130 | 131 | cend = cstart + np.random.choice(18, 1, replace=False) + 1 132 | 133 | change_index.append(np.arange(cstart, cend, 1)) 134 | 135 | fixed_index = np.setdiff1d(np.array([i for i in range(0, len(updated_smiles))]), np.array(change_index)) 136 | copy_loc = [] 137 | 138 | ini_smiles.append(updated_smiles) 139 | fixed_smiles_index.append(fixed_index) 140 | copy_loc_index.append(copy_loc) 141 | 142 | # ----------------------------------------------------------------------------- 143 | 144 | list_of_mxl = [] 145 | 146 | ini_samp = np.zeros((1, max_smiles_length*8), dtype = np.int) 147 | 148 | par_samp = [] 149 | 150 | indx_db = np.array([2, 5, 9, 13, 17]) 151 | find_db = [] 152 | #indx = np.random.choice(int(len(training_data)), int(num_parallel_chains), replace = False) 153 | for i in range(0, num_parallel_chains): 154 | #loc_db = [pos for pos, char in enumerate(pah_smiles[i]) if char == '='] 155 | #find_db.append(loc_db) 156 | list_of_mxl.append(min(len(ini_smiles[i]), max_smiles_length)) 157 | st = ini_smiles[i] #+ 'CCCCC' 158 | b = smiles_to_binary(st, max_smiles_length) 159 | par_samp.append(np.reshape(b, [1, -1])) 160 | # ----------------------------------------------------------------------------- 161 | # Options for Bayesian inference 162 | # ----------------------------------------------------------------------------- 163 | chain_length = 50000 164 | burnout_period = 10000 165 | dbi.options(burnout_period, chain_length) 166 | 167 | data = np.zeros(2); sdev = np.zeros(2) 168 | # ----------------------------------------------------------------------------- 169 | # Running the Markov Chain Monte Carlo sampling algorithm 170 | # ----------------------------------------------------------------------------- 171 | homo_state_all = []; lumo_state_all = [] 172 | #fname = 173 | #cons_chain = [76, 83, 97] 174 | for par_chain in range(0, num_parallel_chains): 175 | print('--------------------------------------------------------------------') 176 | print(' chain number = ', par_chain) 177 | print('--------------------------------------------------------------------') 178 | 179 | try: 180 | change_smiles_index = [] 181 | 182 | smiles_index = np.array([i for i in range(0, list_of_mxl[par_chain])]) 183 | 184 | change_smiles_index.append(np.setdiff1d(smiles_index, fixed_smiles_index[par_chain])) 185 | 186 | print(change_smiles_index) 187 | 188 | 189 | start_time = time.time() 190 | fname = 'Generated_smiles_bay_lumo_with_fixed_pah' + str(par_chain) + '_new.smi' 191 | #ini_samp = np.zeros((1, list_of_mxl[par_chain]*8), dtype = np.int) 192 | ini_samp[0,:] = par_samp[par_chain][:] 193 | post_samples, accepted_samples, homo_state, lumo_state, visited_pred = dbi.inference(data, sdev, ini_samp, change_index = change_smiles_index, copy_loc=copy_loc_index[par_chain]) 194 | homo_state_all.append(homo_state); lumo_state_all.append(lumo_state) 195 | #post_samples, accepted_samples = dbi.inference(data, sdev, ini_samp, change_index = change_smiles_index) 196 | print("Time taken = ", time.time() - start_time) 197 | #fp = open('Generated_smiles_bay_redox_new.smi', 'w') 198 | with open(fname, 'w') as fp: 199 | for i in range(0, len(post_samples)): 200 | smiles = visualize_smiles(np.array(post_samples[i]))# 201 | for j in range(0, len(smiles)): 202 | st = str(smiles[j]) + "\n" 203 | fp.writelines("{}".format(st)) 204 | except: 205 | pass 206 | 207 | 208 | -------------------------------------------------------------------------------- /deep_bayesian_inference.py: -------------------------------------------------------------------------------- 1 | ''' 2 | Provides a class for Deep Bayesian Infererence. 3 | ''' 4 | import numpy as np 5 | import scipy as sc 6 | from deep_learning import * 7 | # ---------------------------------------------------- 8 | class Deep_Bayesian_Inference: 9 | ''' Initializing the class ''' 10 | def __init__(self, likelihood, prior): 11 | ''' Give details of initialization ''' 12 | # --------------------------------------------------- 13 | # Functions for likelihood and prior 14 | self.likelihood = likelihood 15 | self.prior = prior 16 | # ---------------------------------------------------- 17 | # Parameters for MCMC sampling 18 | self.number_of_samples = 50000 19 | self.burnout_period = 10000 20 | # ---------------------------------------------------- 21 | # Parameters for annealed sampling 22 | # Annealing is found to work fine with random beta 23 | # ---------------------------------------------------- 24 | self.ini_fwd_temp = 0.1 25 | self.fin_fwd_temp = 0.9 26 | 27 | self.ini_back_temp = 0.1 28 | self.fin_back_temp = 0.9 29 | 30 | self.anealing_samples = 1000 31 | # ---------------------------------------------------- 32 | # Setting options 33 | # ---------------------------------------------------- 34 | def options(self, number_of_samples, burnout_period, 35 | initial_fwd_temp = 0.1, final_fwd_temp = 0.9, 36 | initial_back_temp = 0.1, final_back_temp = 0.9, 37 | annealing_samples = 1000): 38 | # ---------------------------------------------------- 39 | # Parameters for MCMC sampling 40 | self.number_of_samples = number_of_samples 41 | self.burnout_period = burnout_period 42 | # ---------------------------------------------------- 43 | # Parameters for annealed sampling 44 | self.ini_fwd_temp = initial_fwd_temp 45 | self.fin_fwd_temp = final_fwd_temp 46 | 47 | self.ini_back_temp = initial_back_temp 48 | self.fin_back_temp = final_back_temp 49 | 50 | self.annealing_samples = annealing_samples 51 | 52 | 53 | # ---------------------------------------------------- 54 | # Function for Bayesian inference 55 | # ---------------------------------------------------- 56 | def inference(self, data, sdev, ini_samp, 57 | change_index = [], copy_loc = []): 58 | ''' Function for Bayesian inference ''' 59 | # ---------------------------------------------------- 60 | # Setting prior and likelihood 61 | # ---------------------------------------------------- 62 | prior = self.prior 63 | likelihood = self.likelihood 64 | # ---------------------------------------------------- 65 | if data.ndim == 1: 66 | nd = np.shape(data)[0] 67 | data.reshape(1, nd) 68 | else: 69 | nd = np.shape(data)[1] 70 | if sdev.ndim > 1: 71 | sdev = sdev[0,:] 72 | # ---------------------------------------------------- 73 | # Defining covariance matrix 74 | cov = np.diag(sdev*sdev) 75 | # ---------------------------------------------------- 76 | # Parameters for annealing 77 | # ---------------------------------------------------- 78 | ''' 79 | ift = self.ini_fwd_temp; fft = self.fin_fwd_temp 80 | ibt = self.ini_back_temp; fbt = self.fin_back_temp 81 | anneal_samp = self.annealing_samples ''' 82 | # ---------------------------------------------------- 83 | # Number of samples 84 | number_of_samples = self.number_of_samples 85 | burnout_period = self.burnout_period 86 | # ---------------------------------------------------- 87 | # For collecting the samples 88 | post_samples = []; accepted_samples = []; visited_pred = []; visited_states = [] 89 | # ---------------------------------------------------- 90 | # Initializing the MCMC sampling 91 | pres_state = ini_samp 92 | #pres_prior = prior.probability(pres_state) 93 | # Likelihood for present state 94 | pres_pred = likelihood.predict(pres_state) 95 | 96 | 97 | print(np.shape(pres_pred)) 98 | 99 | 100 | homo_max = -4.62512166; homo_min = -8.49539908; homo_xh = -5.6; homo_maxx = homo_max 101 | lumo_max = 0.70178201; lumo_min = -3.91109452 102 | slp_homo = 2.0/(homo_maxx + 5.6); cnst_homo = -((homo_maxx - 5.6)/(homo_maxx + 5.6)) 103 | #slp_lumo = 2.0/(lumo_max + 2.15); cnst_lumo = -((lumo_max - 2.15)/(lumo_max + 2.15)) 104 | slp_lumo = 2.0/(-3.15-lumo_min); cnst_lumo = -(( - 3.15 + lumo_min)/(-3.15 - lumo_min)) 105 | 106 | #slp_homo = 1.0/(homo_xh-homo_max); cnst_homo = -((homo_max )/(homo_xh-homo_max)) 107 | 108 | homo = (pres_pred[:,0])*(homo_max - homo_min) + homo_min 109 | lumo = (pres_pred[:,1])*(lumo_max - lumo_min) + lumo_min 110 | y_homo = (slp_homo*homo + cnst_homo)*10.0 111 | y_lumo = (slp_lumo*lumo + cnst_lumo)*10.0 112 | 113 | p_homo = (1.0/(1.0+np.exp(-y_homo))) 114 | p_lumo = (1.0/(1.0+np.exp(y_lumo))) 115 | 116 | #alpha_gamma = 1.0; beta_gamma = 0.5 117 | #p_homo = y_homo**(alpha_gamma - 1)*np.exp(-beta_gamma*y_homo) 118 | 119 | #if p_homo < 1e-20: 120 | # p_homo = 0.0 121 | 122 | #if p_lumo < 1e-10: 123 | # p_lumo = 0.0 124 | 125 | pres_like = np.log(p_lumo) # * p_lumo) 126 | 127 | '''if p_homo > 1e-03 and p_lumo > 1e-03: 128 | 129 | else: 130 | pres_like = -1e300 #np.log(1e-300)''' 131 | 132 | print(homo, lumo, y_homo, y_lumo, pres_like) 133 | #input("PRESS RETURN TO CONTINUE!!!!") 134 | # ---------------------------------------------------- 135 | # Starting MCMC sampling 136 | 137 | num_parallel_chains = np.shape(ini_samp)[0] 138 | 139 | accepted = np.zeros(num_parallel_chains, dtype=np.int); is_accepted = False 140 | homo_state = []; lumo_state = []; visited_pred = [] 141 | for samp in range(0, burnout_period+number_of_samples): 142 | # ----------------------------------------------------- 143 | # Proposed state 144 | if samp < 2000: 145 | beta = 0.2*np.random.random(1) 146 | tiny_prob = 1e-100 147 | 148 | if samp < 10000 and samp >= 2000: 149 | beta = 0.3*np.random.random(1) 150 | tiny_prob = 1e-50 151 | 152 | 153 | if samp < 15000 and samp >= 10000: 154 | beta = 0.4*np.random.random(1) 155 | tiny_prob = 1e-20 156 | 157 | if samp < 20000 and samp >= 15000: 158 | beta = 0.5*np.random.random(1) 159 | tiny_prob = 1e-10 160 | 161 | if samp < 30000 and samp >= 20000: 162 | beta = 0.6*np.random.random(1) 163 | tiny_prob = 1e-7 164 | 165 | 166 | if samp > 30000: 167 | beta = 0.7*np.random.random(1) 168 | tiny_prob = 1e-5 169 | 170 | prop_state, prop_ratio, prior_ratio = prior.proposal(pres_state, 171 | beta, beta, change_index=change_index, copy_loc = copy_loc) 172 | 173 | 174 | # Likelihood for proposed state 175 | prop_pred = likelihood.predict(prop_state) 176 | #print(prop_pred) 177 | #visited_states.append(prop_state) 178 | 179 | #smiles = visualize_smiles(np.array(prop_state)) 180 | 181 | #print(smiles) 182 | 183 | '''sigma = cov + np.diag(prop_sd*prop_sd) 184 | 185 | x = data - prop_pred 186 | prop_like = -0.5*np.log(np.linalg.det(sigma)) -0.5*np.dot(np.dot(x, sigma), x.T) ''' 187 | 188 | homo = (prop_pred[:,0])*(homo_max - homo_min) + homo_min 189 | lumo = (prop_pred[:,1])*(lumo_max - lumo_min) + lumo_min 190 | y_homo = (slp_homo*homo + cnst_homo)*10.0 191 | y_lumo = (slp_lumo*lumo + cnst_lumo)*10.0 192 | #prop_like = np.log(((1.0/(1.0+np.exp(-y_homo))))*((1.0/(1.0+np.exp(y_lumo))))) 193 | #p_homo = y_homo**(alpha_gamma - 1)*np.exp(-beta_gamma*y_homo) 194 | p_homo = (1.0/(1.0+np.exp(-y_homo))) 195 | p_lumo = (1.0/(1.0+np.exp(y_lumo))) 196 | 197 | visited_pred.append(homo) 198 | #print(homo, y_homo, p_homo) 199 | 200 | if p_homo < tiny_prob: 201 | p_homo = 0.0 202 | 203 | if p_lumo < tiny_prob: 204 | p_lumo = 0.0 205 | 206 | prop_like = np.log(p_lumo) # * p_lumo) 207 | 208 | 209 | '''if p_homo > 1e-03 and p_lumo > 1e-03: 210 | prop_like = np.log(p_homo * p_lumo) 211 | else: 212 | prop_like = -1e300; #np.log(1e-300)''' 213 | 214 | 215 | '''oxid = (prop_pred[:,0]/10.0)*(oxid_max - oxid_min) + oxid_min 216 | red = (prop_pred[:,1]/10.0)*(red_max - red_min) + red_min 217 | redox = oxid - red 218 | y_red = (slp_red*red + cnst_red)*6.0 219 | y_redox = (slp_redox*redox + cnst_redox)*6.0 220 | prop_like = (1.0/(1.0+np.exp(-y_red)))*(1.0/(1.0+np.exp(-y_redox)))''' 221 | # Acceptance probability 222 | acceptance_prob = prop_like - pres_like + prop_ratio + prior_ratio 223 | 224 | #print(prop_like, pres_like, prop_ratio, prior_ratio) 225 | 226 | # Metropolis-Hastings criterion 227 | urand = np.random.random(1) 228 | '''acceptance_decision = (np.exp(acceptance_prob) > np.random.random(np.shape(acceptance_prob))) 229 | 230 | acc_index = np.where(acceptance_decision >0.99) 231 | 232 | 233 | #print(acc_index, np.max(prop_state - pres_state)) 234 | 235 | pres_state = pres_state 236 | pres_like = pres_like 237 | pres_pred = pres_pred 238 | acc_pred = pres_pred 239 | state = pres_state 240 | is_accepted = False 241 | # -------------------------------------------------------------------- 242 | # Changing the accepted states 243 | accepted[acc_index] = accepted[acc_index] + 1 244 | pres_state[acc_index,:] = prop_state[acc_index,:] 245 | pres_like[acc_index] = prop_like[acc_index] 246 | pres_pred[acc_index,:] = prop_pred[acc_index,:] 247 | state[acc_index,:] = prop_state[acc_index,:] 248 | acc_pred[acc_index,:] = prop_pred[acc_index,:] 249 | is_accepted = True''' 250 | 251 | 252 | 253 | 254 | if np.exp(acceptance_prob) > urand: 255 | # Proposed state accepted 256 | accepted = accepted + 1 257 | # -------------------------------------------------------- 258 | pres_state = prop_state 259 | pres_like = prop_like 260 | pres_pred = prop_pred 261 | state = prop_state 262 | acc_pred = prop_pred 263 | is_accepted = True 264 | 265 | 266 | else: 267 | # -------------------------------------------------------- 268 | pres_state = pres_state 269 | pres_like = pres_like 270 | pres_pred = pres_pred 271 | acc_pred = pres_pred 272 | state = pres_state 273 | is_accepted = False 274 | # --------------------------------------------------------- 275 | # Collecting samples after burnout period 276 | # --------------------------------------------------------- 277 | homo_state.append(pres_pred[:,0]) 278 | lumo_state.append(pres_pred[:,1]) 279 | 280 | if samp > burnout_period: 281 | post_samples.append(state) 282 | 283 | if is_accepted == True: 284 | accepted_samples.append(state) 285 | 286 | '''print(state[0, 152:160]) 287 | print(pres_vis[0, 152:160]) 288 | print(prop_vis[0, 152:160])''' 289 | # --------------------------------------------------------- 290 | # Keeping count 291 | # --------------------------------------------------------- 292 | if np.mod(samp, 1000) == 0: 293 | print(samp, accepted[0], homo, lumo) #, np.exp(acceptance_prob), urand) 294 | 295 | return post_samples, accepted_samples, homo_state, lumo_state, visited_pred #, visited_states, visited_pred 296 | # -------------------------------------------------------- 297 | 298 | -------------------------------------------------------------------------------- /design_of_experiments.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | """ 3 | Created on Fri Jun 30 12:01:03 2017 4 | 5 | @author: piyush.t 6 | """ 7 | 8 | # ------------------------------------------------------- 9 | # Design of experiment methodology for binary dataset 10 | # Key idea is to select training dataset such that distance 11 | # between points is maximized. 12 | # ------------------------------------------------------- 13 | import scipy as sc 14 | import numpy as np 15 | 16 | from pprint import pprint 17 | # Load the dataset 18 | from deep_learning import * 19 | from process_smiles import * 20 | from pubchem_data import * 21 | import pickle 22 | from anova import * 23 | import time 24 | 25 | 26 | # ---------------------------------------------------------------------- 27 | # Function to define similarity measure between two binary vectors 28 | # ---------------------------------------------------------------------- 29 | def distance(bin1, bin2): 30 | # ---------------------------------------------------------------------- 31 | a = np.sum(bin1*bin2); b = np.sum((1.0-bin1)*bin2); 32 | c = np.sum(bin1*(1.0-bin2)); d = np.sum((1.0-bin1)*(1.0-bin2)) 33 | # -------------------------------------------------------------------------- 34 | # Initially implementing YULEQ/Hamming distance. Other distances and similarity 35 | # measures will be explored in the future. 36 | # -------------------------------------------------------------------------- 37 | dist = (2.0*b*c)/(a*d + b*c) 38 | #dist = b + c 39 | #print('distance =', dist) 40 | return dist 41 | 42 | # --------------------------------------------------------------------------- 43 | # Creating a matrix of distance between datapoints 44 | # --------------------------------------------------------------------------- 45 | def distance_matrix(data): 46 | ''' Function for creating a N X N matrix of distance between datapoints ''' 47 | # --------------------------------------------------------------------------- 48 | num_data = np.shape(data)[0] 49 | dmat = np.zeros((num_data, num_data)) 50 | 51 | for row in range(0, num_data): 52 | dmat[row,row] = 0.0 53 | d1 = data[row,:] 54 | for col in range(row, num_data): 55 | d2 = data[col,:] 56 | d = distance(d1, d2) 57 | dmat[row, col] = d 58 | dmat[col, row] = dmat[row, col] 59 | 60 | # ----------------------------------------------------------------------------- 61 | return dmat 62 | # ----------------------------------------------------------------------------- 63 | 64 | # ----------------------------------------------------------------------------- 65 | # Function for design of experiments 66 | # ----------------------------------------------------------------------------- 67 | def doe(data, initial_index, no_samp): 68 | ''' Function for design of experiments ''' 69 | # Creating a list of indices 70 | indices = [None]*no_samp 71 | # Distance between datapoints 72 | dist_mat = distance_matrix(data) 73 | # First point 74 | indices[0] = initial_index 75 | dist1 = dist_mat[indices[0], :] 76 | # Second point 77 | indx = np.argmax(dist1) 78 | indices[1] = indx 79 | dist2 = dist_mat[indices[1], :] 80 | # Minimum distance 81 | min_dist = np.minimum(dist1, dist2) 82 | # ----------------------------------------------------------------------------- 83 | for i in range(2, no_samp): 84 | dist1 = min_dist 85 | indx = np.argmax(dist1) 86 | indices[i] = indx 87 | dist2 = dist_mat[indices[i], :] 88 | # Minimum distance 89 | min_dist = np.minimum(dist1, dist2) 90 | 91 | return indices 92 | 93 | 94 | 95 | -------------------------------------------------------------------------------- /dnn_pubchem.py: -------------------------------------------------------------------------------- 1 | # ------------------------------------------------------- 2 | # Using Deep Boltzmann Machine for pubchem dataset 3 | # ------------------------------------------------------- 4 | import scipy as sc 5 | import numpy as np 6 | 7 | from pprint import pprint 8 | # Load the dataset 9 | from deep_learning import * 10 | from process_smiles import * 11 | from pubchem_data import * 12 | import pickle 13 | 14 | smiles, properties = read_json_data('Molecular_data_pubchem') 15 | 16 | train_set, test_set = create_smiles_dataset(smiles, properties_data=properties, max_smiles_length=30, percentage_train_data = 0.8) 17 | 18 | training_data = np.array(train_set[0]) 19 | training_target = np.array(train_set[1])/np.max(train_set[1], axis=0); 20 | 21 | [num_data, num_vis] = np.shape(training_data) 22 | num_targ = np.shape(training_target)[1] 23 | 24 | print(num_vis, num_targ) 25 | 26 | num_layers = 4 27 | 28 | num_nodes = [num_vis, 200, 200, num_targ] 29 | 30 | dnn = Deep_Neural_Network(num_layers, num_nodes) 31 | 32 | # ----------------------------------------------------------------------------- 33 | # Setting pretraining options 34 | # ----------------------------------------------------------------------------- 35 | maxepoch = 500 36 | learning_rate_dbn = 0.01 37 | cdn_dbn = 1 38 | 39 | learning_rate_gb = 0.001 # This value is taken after experiments on rbm_pubchem code 40 | cdn_gb = 10 # This value is taken after experiments on rbm_pubchem code 41 | 42 | numbatches = int(floor(num_data/100)) 43 | dnn.pretrain_options(maxepoch, numbatches, learning_rate_dbn, learning_rate_gb, cdn_dbn, cdn_gb, 44 | method = 'Gibbs', initialmomentum=0.5, finalmomentum=0.9, initialepochs=5) 45 | 46 | # ----------------------------------------------------------------------------- 47 | # Setting training options 48 | # ----------------------------------------------------------------------------- 49 | maxepoch = 3000 50 | learning_rate = 0.01 51 | numbatches = int(floor(num_data/100)) 52 | numbatches = 10 53 | dnn.train_options(maxepoch, numbatches, learning_rate, initialmomentum=0.5, finalmomentum=0.9, initialepochs=5) 54 | 55 | # ----------------------------------------------------------------------------- 56 | # Pre-training 57 | # ----------------------------------------------------------------------------- 58 | #training_target[:,0] = np.sum(training_data, axis=1) 59 | #training_target[:,1] = np.sum(training_data, axis=1) 60 | #mx_train = np.max(training_target, axis=0) 61 | 62 | #training_target = training_target/mx_train 63 | #errors_dbn, epochs_dbn = 64 | dnn.pretrain(training_data, training_target) 65 | 66 | with open('dnn_pretrained.pkl', 'wb') as fp: 67 | pickle.dump(dnn, fp) 68 | 69 | epochs, errors = dnn.train(training_data, training_target) 70 | 71 | with open('dnn_trained.pkl', 'wb') as fp: 72 | pickle.dump(dnn, fp) 73 | 74 | 75 | test_data = np.array(test_set[0]) 76 | test_target = np.array(test_set[1])/np.max(train_set[1], axis=0); 77 | 78 | 79 | #test_target[:,0] = np.sum(test_data, axis=1) 80 | #test_target[:,1] = np.sum(test_data, axis=1) 81 | #test_target = test_target/mx_train 82 | 83 | 84 | 85 | #test_target = np.array(test_set[1])/np.max(train_set[1], axis=0); 86 | 87 | 88 | data = training_data[0:100,:] 89 | pred_target_train = dnn.predict(data) 90 | 91 | 92 | data = test_data[0:100,:] 93 | pred_target_test = dnn.predict(data) 94 | 95 | 96 | ''' 97 | with open('dbm_pretrained.pkl', 'wb') as fp: 98 | pickle.dump(dbm, fp) 99 | 100 | 101 | 102 | errors_dbm, epochs_dbm, errors_vis, errors_targ = dbm.train(training_data, training_target) 103 | 104 | 105 | test_data = np.array(test_set[0]) 106 | test_target = np.array(test_set[1])/np.max(train_set[1], axis=0); 107 | 108 | 109 | data = training_data[0:100,:] 110 | pred_target_train = dbm.predict_mc(data) 111 | 112 | 113 | data = test_data[0:100,:] 114 | pred_target_test = dbm.predict_mc(data) 115 | 116 | 117 | no_samples = 100; burn_period = 10000; no_steps = 100 118 | visible, target = dbm.sample(no_samples, burn_period, no_steps) 119 | 120 | fp = open('Generated_smiles_dbm.smi', 'w') 121 | 122 | for i in range(0, len(visible)): 123 | smiles = visualize_smiles(np.array(visible[i]))# 124 | print(len(smiles)) 125 | for j in range(0, len(smiles)): 126 | st = str(smiles[j]) + "\n" 127 | fp.writelines("{}".format(st)) 128 | fp.close() 129 | ''' 130 | #fp = open('Generated_smiles.smi', 'w') 131 | #for i in range(0, len(smiles)): 132 | # st = str(smiles[i]) + "\n" 133 | # fp.writelines("{}".format(st)) 134 | #fp.close() -------------------------------------------------------------------------------- /dnn_redox_add_layer_500.py: -------------------------------------------------------------------------------- 1 | # ------------------------------------------------------- 2 | # Training a deep neural network on properties dataset 3 | # ------------------------------------------------------- 4 | import scipy as sc 5 | import numpy as np 6 | 7 | from pprint import pprint 8 | # Load the dataset 9 | from deep_learning import * 10 | from process_smiles import * 11 | from pubchem_data import * 12 | import pickle 13 | from anova import * 14 | import time 15 | from deep_neural_network import * 16 | # ----------------------------------------------------------------------------- 17 | 18 | # ----------------------------------------------------------------------------- 19 | def train_dnn(pubchem_data_filename, properties_data_filename, 20 | max_smiles_length=100): 21 | # ----------------------------------------------------------------------------- 22 | # For ensuring that same random number is always generated 23 | # ----------------------------------------------------------------------------- 24 | np.random.seed(100) 25 | # ----------------------------------------------------------------------------- 26 | # For ensuring that same random number is always generated 27 | # ----------------------------------------------------------------------------- 28 | smiles, properties_all = read_json_data(pubchem_data_filename, redox = False, max_smiles_length=max_smiles_length) 29 | 30 | train_set_all, test_set_all = create_smiles_dataset(smiles, properties_data=[], max_smiles_length=max_smiles_length, 31 | percentage_train_data = 0.3) 32 | 33 | 34 | smiles1, properties1 = read_json_data(properties_data_filename, redox = True, is_properties = True, 35 | max_smiles_length=max_smiles_length) 36 | 37 | smiles, properties = prune(smiles1, properties1) 38 | tot_data = len(smiles) 39 | train_set_redox, test_set_redox = create_smiles_dataset(smiles, properties_data = properties, 40 | max_smiles_length=max_smiles_length, percentage_train_data = 0.95) 41 | 42 | 43 | target_max = np.max(np.concatenate((np.array(train_set_redox[1]), np.array(test_set_redox[1])), axis=0), axis=0) 44 | target_min = np.min(np.concatenate((np.array(train_set_redox[1]), np.array(test_set_redox[1])), axis=0), axis=0) 45 | 46 | 47 | training_data = np.concatenate((np.array(train_set_all[0]), np.array(train_set_redox[0])), axis=0) 48 | 49 | # ----------------------------------------------------------------------------- 50 | [num_data, num_vis] = np.shape(training_data) 51 | 52 | 53 | # ----------------------------------------------------------------------------- 54 | # Starting DBM 55 | # ----------------------------------------------------------------------------- 56 | # ----------------------------------------------------------------------------- 57 | # Now pre-training the DBM 58 | # ----------------------------------------------------------------------------- 59 | num_test_data = np.shape(test_set_redox)[0] 60 | 61 | 62 | train_targ = (np.array(train_set_redox[1]) - target_min)/(target_max - target_min); 63 | #train_targ = (np.array(train_set_redox[1]))/(target_max); 64 | training_target = train_targ 65 | 66 | print("Training Target = ", np.sum(np.sum(training_target, axis = 0))) 67 | 68 | 69 | num_targ = np.shape(training_target)[1] 70 | 71 | print(num_vis, num_targ) 72 | num_hid = int(np.floor(num_vis*vishid_ratio)) 73 | 74 | [num_data, num_vis] = np.shape(training_data) 75 | 76 | 77 | num_nodes = [num_vis, 800, 500, 200, 200, num_targ] 78 | 79 | 80 | num_layers = len(num_nodes) 81 | dropout = 0.7*np.ones(len(num_nodes)-1) 82 | dnn = Deep_Neural_Network(num_layers, num_nodes, dropout = dropout) 83 | 84 | 85 | # ----------------------------------------------------------------------------- 86 | # Setting pretraining options 87 | # ----------------------------------------------------------------------------- 88 | maxepoch = 100 89 | learning_rate_dbn = 0.01 90 | cdn_dbn = 1 91 | 92 | learning_rate_gb = 0.01 # This value is taken after experiments on rbm_pubchem code 93 | cdn_gb = 1 # This value is taken after experiments on rbm_pubchem code 94 | 95 | numbatches = int(floor(num_data/200)) 96 | #numbatches = 10 97 | dnn.pretrain_options(maxepoch, numbatches, learning_rate_dbn, learning_rate_gb, cdn_dbn, cdn_gb, 98 | method = 'Gibbs', initialmomentum=0.5, finalmomentum=0.9, initialepochs=5) 99 | 100 | # ----------------------------------------------------------------------------- 101 | # Setting training options 102 | # ----------------------------------------------------------------------------- 103 | maxepoch = 10 104 | learning_rate = 0.1 105 | numbatches = int(floor(num_data/600)) 106 | #numbatches = 10 107 | dnn.train_options(maxepoch, numbatches, learning_rate, initialmomentum=0.8, finalmomentum=0.9, initialepochs=5) 108 | 109 | # ----------------------------------------------------------------------------- 110 | # Pretraining the DNN 111 | # ----------------------------------------------------------------------------- 112 | 113 | dnn.pretrain(training_data, training_target) 114 | 115 | with open('dnn_pretrain.pkl', 'wb') as fp: 116 | pickle.dump(dnn, fp) 117 | 118 | 119 | 120 | smiles1, properties1 = read_json_data(properties_data_filename, redox = True, is_properties = True, 121 | max_smiles_length=max_smiles_length) 122 | 123 | smiles, properties = prune(smiles1, properties1) 124 | 125 | #smiles, properties = prune(smiles1, properties1) 126 | 127 | train_set_redox, test_set_redox = create_smiles_dataset(smiles, properties_data = properties, 128 | max_smiles_length=max_smiles_length, ' 129 | percentage_train_data = 0.80, use_doe = False) 130 | training_data = np.array(train_set_redox[0]) 131 | 132 | [num_data, num_vis] = np.shape(training_data) 133 | 134 | train_targ = (np.array(train_set_redox[1]) - target_min)/(target_max - target_min); 135 | #train_targ = (np.array(train_set_redox[1]))/(target_max); 136 | training_target = train_targ 137 | 138 | test_data = np.array(test_set_redox[0]) 139 | 140 | #test_data_dbm = dbn.forward_pass(test_data) 141 | 142 | test_targ = (np.array(test_set_redox[1]) - target_min)/(target_max - target_min); 143 | # test_targ = (np.array(test_set_redox[1]))/(target_max); 144 | num_targ_data = np.shape(train_targ)[0] 145 | 146 | test_target = test_targ 147 | # ------------------------------------------------------------------------------ 148 | epochs, errors = dnn.train(training_data, training_target) 149 | # ------------------------------------------------------------------------------ 150 | with open('trained_dnn.pkl', 'wb') as fp: 151 | pickle.dump(dnn, fp) 152 | # ------------------------------------------------------------------------------ -------------------------------------------------------------------------------- /dnn_trained.pkl: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/piyushtagade/SLAMDUNCS/4227a92121091b017edde679a615df94690cc0ae/dnn_trained.pkl -------------------------------------------------------------------------------- /dnn_trained_redox.pkl: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/piyushtagade/SLAMDUNCS/4227a92121091b017edde679a615df94690cc0ae/dnn_trained_redox.pkl -------------------------------------------------------------------------------- /drawing_tools.py: -------------------------------------------------------------------------------- 1 | # ----------------------------------------------------- 2 | # This file contains different drawing tools that can 3 | # be used for visualization of deep learning results 4 | # ----------------------------------------------------- 5 | import numpy as np 6 | import scipy as sc 7 | import scipy.misc as smp 8 | #import PIL 9 | #from PIL import Image 10 | 11 | 12 | # ----------------------------------------------------- 13 | def draw_greyscale(data, irows, icols, asratio): 14 | # ----------------------------------------------------- 15 | # This function draws a greyscale image from a given pixel data 16 | # ----------------------------------------------------- 17 | img_data = np.reshape(data, (irows, icols)) 18 | img = smp.toimage( img_data ) 19 | img = img.resize((img.size[0]*asratio, img.size[1]*asratio), PIL.Image.ANTIALIAS) 20 | img.show() 21 | 22 | # ----------------------------------------------------- 23 | # For drawing a stack of images 24 | # ----------------------------------------------------- 25 | def draw_stack_of_images(data, irows, icols, nimg): 26 | # ----------------------------------------------------- 27 | 28 | [num_data, num_dim] = np.shape(data) 29 | # Aspect ratio 30 | asratio = 1 31 | 32 | img_data = np.zeros((irows*asratio*nimg, irows*asratio*nimg)) 33 | 34 | 35 | for ir in range(0, nimg): 36 | for ic in range(0, nimg): 37 | # img = draw_greyscale(data[ir*nimg+ic], irows, icols, asratio) 38 | img = np.reshape(data[ir*nimg+ic], (irows, icols)) 39 | ir1 = ir*irows; ir2 = (ir+1)*irows 40 | ic1 = ic*icols; ic2 = (ic+1)*icols 41 | img_data[ir1:ir2, ic1:ic2] = img 42 | 43 | img = smp.toimage( img_data ) 44 | return img.resize((img.size[0]*asratio, img.size[1]*asratio), PIL.Image.ANTIALIAS) 45 | 46 | 47 | 48 | 49 | -------------------------------------------------------------------------------- /process_smiles.py: -------------------------------------------------------------------------------- 1 | # ------------------------------------------------------------- 2 | # Functions for processing SMILES data 3 | # ------------------------------------------------------------- 4 | 5 | import numpy as np 6 | import scipy as sc 7 | from pprint import pprint 8 | from design_of_experiments import * 9 | from matplotlib import pyplot as plt 10 | import re 11 | 12 | 13 | # ------------------------------------------------------------- 14 | # Function to convert SMILES to binary data 15 | # ------------------------------------------------------------- 16 | def smiles_to_binary(smiles, max_smiles_length): 17 | 18 | # ------------------------------------------------------------- 19 | # First checking length of the SMILES 20 | # ------------------------------------------------------------- 21 | smiles_length = len(smiles) 22 | # 23 | 24 | if smiles_length > max_smiles_length: 25 | smiles = smiles[0:max_smiles_length] 26 | #print('Greater') 27 | 28 | if smiles_length < max_smiles_length: 29 | rem = max_smiles_length - smiles_length 30 | for i in range(0, rem): 31 | smiles = smiles + ' ' 32 | 33 | bin_smiles = string_to_binary(smiles) 34 | return bin_smiles 35 | # -------------------------------------------------------------- 36 | # Function to convert string to a list of binary 37 | # -------------------------------------------------------------- 38 | def string_to_binary(string): 39 | binary = np.zeros([len(string), 8]) 40 | for i in range(0, len(string)): 41 | s = string[i] 42 | b = bin(ord(s))[2:].zfill(8) 43 | for j in range(0, 8): 44 | binary[i,j] = b[j] 45 | 46 | return binary.astype(int) 47 | 48 | 49 | # -------------------------------------------------------------- 50 | # Function to convert binary to string 51 | # -------------------------------------------------------------- 52 | def binary_to_string(binary): 53 | if binary.ndim > 1: 54 | smiles_length, bin_length = np.shape(binary) 55 | else: 56 | smiles_length = 1; bin_length = np_shape(binary)[0] 57 | string = [] 58 | for b in binary: 59 | s = [] 60 | for j in range(0,8): 61 | s.append(str(b[j])) 62 | string.append(chr(int(''.join(s), 2))) 63 | return ''.join(string) 64 | 65 | # ------------------------------------------------------------- 66 | # Function to create training and testing dataset 67 | # ------------------------------------------------------------- 68 | def create_smiles_dataset(smiles_data, properties_data=[], max_smiles_length=100, percentage_train_data = 0.7, use_doe = False): 69 | # ------------------------------------------------------------- 70 | total_no_data = len(smiles_data) 71 | is_properties = False 72 | if len(properties_data) > 0: 73 | is_properties = True 74 | properties_data = np.array(properties_data) 75 | no_train_data = np.int(np.floor(percentage_train_data*total_no_data)) 76 | no_test_data = total_no_data - no_train_data 77 | 78 | 79 | # creating dataset 80 | data = [] 81 | 82 | for i in range(0, total_no_data): 83 | smiles = smiles_data[i] 84 | b = smiles_to_binary(smiles, max_smiles_length) 85 | b = np.reshape(b, max_smiles_length*8) 86 | data.append(b) 87 | 88 | if use_doe == True: 89 | initial_index = np.random.randint(total_no_data, size = 1) 90 | indx_train = doe(np.array(data), initial_index, no_train_data) 91 | else: 92 | indx_train = np.random.choice(int(total_no_data), int(no_train_data), replace = False) 93 | #indx_train = [i for i in range(0, no_train_data)]; 94 | # ------------------------------------------------------------- 95 | # Training data 96 | # ------------------------------------------------------------- 97 | smiles_batchdata = []; properties_batchdata = [] 98 | for i in range(0, no_train_data): 99 | j = int(indx_train[i]) 100 | smiles = smiles_data[j] 101 | b = smiles_to_binary(smiles, max_smiles_length) 102 | b = np.reshape(b, max_smiles_length*8) 103 | smiles_batchdata.append(b) 104 | if is_properties == True: 105 | properties_batchdata.append(properties_data[:,j]) 106 | #properties_batchdata.append(np.sum(np.array(b))) 107 | 108 | 109 | 110 | train_data = (smiles_batchdata, properties_batchdata) 111 | 112 | # ------------------------------------------------------------- 113 | # Testing data 114 | # ------------------------------------------------------------- 115 | 116 | 117 | #indx_test = set([i for i in range(0, total_no_data)]).symmetric_difference(indx_train) 118 | indx = [i for i in range(0, total_no_data)]; 119 | indx_test = np.setdiff1d(indx, indx_train) 120 | print(total_no_data, no_train_data, np.shape(indx_train), np.shape(indx_test)) 121 | no_test_data = np.shape(indx_test)[0] 122 | smiles_batchdata = []; properties_batchdata = [] 123 | for i in range(0, no_test_data): 124 | j = int(indx_test[i]) 125 | smiles = smiles_data[j] 126 | b = smiles_to_binary(smiles, max_smiles_length) 127 | b = np.reshape(b, max_smiles_length*8) 128 | smiles_batchdata.append(b) 129 | if is_properties == True: 130 | properties_batchdata.append(properties_data[:,j]) 131 | 132 | test_data = (smiles_batchdata, properties_batchdata) 133 | return train_data, test_data 134 | # ----------------------------------------------------------------- 135 | # For visualization 136 | # ----------------------------------------------------------------- 137 | def visualize_smiles(binary_smiles): 138 | no_smiles, bin_smiles_length = np.shape(binary_smiles) 139 | smiles_length = int(bin_smiles_length/8) 140 | smiles = [] 141 | for i in range(0, no_smiles): 142 | bin_smiles = np.reshape(binary_smiles[i,:], (smiles_length, 8)) 143 | smiles.append(binary_to_string(bin_smiles.astype(int))) 144 | # pprint(smiles) 145 | 146 | return smiles 147 | # draw_smiles(smiles) 148 | 149 | 150 | def draw_smiles(smiles): 151 | # -------------------------------------------------- 152 | # Drawing SMILES 153 | # -------------------------------------------------- 154 | nimg = len(smiles) 155 | 156 | fig = plt.figure(1) 157 | nrow = np.int(np.sqrt(np.int(nimg))) 158 | ncol = nrow 159 | 160 | for i in range(0, nrow*ncol): 161 | ax = fig.add_subplot(nrow*ncol, 1, i+ 1) 162 | plt.text(0.1, 0.1, smiles[i], transform=ax.transAxes) 163 | plt.axis('off') 164 | plt.show() 165 | plt.pause(0.001) 166 | 167 | # ----------------------------------------------------- 168 | # Adding training data to the existing dataset 169 | # ----------------------------------------------------- 170 | def add_data(tot_data, training_data, training_target, test_data, test_target, 171 | true_test, test): 172 | 173 | num_test = np.shape(test_data)[0] 174 | 175 | indx_test = np.array([i for i in range(0, num_test)]) 176 | 177 | diff = np.square(np.abs(true_test - test)) 178 | 179 | diff_oxid = diff[:,2]; diff_red = diff[:,3] 180 | 181 | ids_oxid = np.argsort(diff_oxid); ids_red = np.argsort(diff_red) 182 | 183 | 184 | tot_test = len(ids_oxid) 185 | add_test = np.int(np.floor(tot_data*0.1)) 186 | 187 | add_oxid = ids_oxid[tot_test-add_test:tot_test] 188 | add_red = ids_red[tot_test - add_test : tot_test] 189 | 190 | add_data = np.union1d(add_oxid, add_red) 191 | 192 | rem_testdata = np.setdiff1d(indx_test, add_data) 193 | 194 | add_training_data = test_data[add_data, :] 195 | tt = np.concatenate((training_data, add_training_data), axis = 0) 196 | 197 | add_training_target = test_target[add_data, :] 198 | 199 | tr = np.concatenate((training_target, add_training_target), axis = 0) 200 | 201 | training_data = tt 202 | training_target = tr 203 | 204 | test_tt = test_data[rem_testdata, :] 205 | test_tr = test_target[rem_testdata, :] 206 | test_data = test_tt 207 | test_target = test_tr 208 | return training_data, training_target, test_data, test_target 209 | 210 | # ----------------------------------------------------------------------------- 211 | def initialize_smiles(smiles, symmetry=False, side_add = 'both'): 212 | # ----------------------------------------------------------------------------- 213 | ''' 214 | This function returns a smiles for Markov chain initialization and 215 | associated constraints. 216 | ''' 217 | # ----------------------------------------------------------------------------- 218 | fixed_index = np.array([], dtype=np.int) 219 | copy_loc = [] 220 | rand_char = ['C', 'C', '=', 'O', 'C', 'C', 'C', 'C', 'C'] 221 | 222 | 223 | 224 | i_pah = np.int(len(re.findall(r'\d+', smiles))/2) 225 | 226 | if i_pah > 0: 227 | r_pah = np.random.choice(2, 1, replace=False)[0] + 1 228 | else: 229 | r_pah = 1 230 | 231 | n_pah = np.max(i_pah-r_pah, 0) 232 | if n_pah < 1: 233 | n_pah = 1 234 | #print(n_pah) 235 | n_pah = i_pah 236 | nr = 1; #((i_pah + 1) - n_pah) 237 | #print(nr, i_pah, n_pah) 238 | #input('HHH') 239 | ring_index = nr 240 | 241 | change_index = [] 242 | 243 | if nr > 1: 244 | strt = smiles.find(str(nr))-1; en = smiles.rfind(str(nr))+1 245 | st = smiles[strt:en] 246 | st_mod = st[0:2] + st[3:-3] + 'CC=' + st[-2:] 247 | updated_smiles = smiles[0:strt] + st_mod + smiles[en:] 248 | 249 | '''for k in range(0, strt): 250 | change_index.append(k) 251 | 252 | for k in range(en+1, len(updated_smiles)): 253 | change_index.append(k)''' 254 | 255 | else: 256 | updated_smiles = smiles 257 | 258 | print(smiles, updated_smiles) 259 | 260 | #print(smiles.find(str(nr)), smiles.rfind(str(nr))) 261 | 262 | #if side_add == 'none': 263 | 264 | if symmetry == True: 265 | num_sym = np.maximum(np.random.choice(n_pah, 1, replace=False)[0], 1) 266 | #num_sym = 2 267 | ret_sym = np.linspace(nr,i_pah,n_pah, dtype=np.int) 268 | cons_sym = np.random.choice(len(ret_sym), num_sym, replace=False) 269 | add_char = np.array(np.arange(4,10, dtype=np.int32)) 270 | #print(ret_sym, cons_sym) 271 | #ini_insert_loc = []; fin_insert_loc = [] 272 | for i_sym in range(0, len(cons_sym)): 273 | s1 = updated_smiles.find(str(nr))-1; s2 = updated_smiles.rfind(str(nr))+1 274 | sym = cons_sym[i_sym] 275 | print(sym, n_pah, nr) 276 | n1 = ret_sym[cons_sym[i_sym]]; 277 | n2 = ret_sym[(len(ret_sym)-1)-cons_sym[i_sym]] 278 | if n1 == 1: 279 | strn = updated_smiles.find(str(n1)) + 2 280 | elif len(ret_sym) == 1: 281 | strn = updated_smiles.find(str(n1)) - 1 282 | else: 283 | strn = updated_smiles.find(str(n1)) #- 1 284 | if (len(ret_sym)-1)-cons_sym[i_sym] == 0: 285 | en = updated_smiles.rfind(str(n2)) +1 #2 286 | else: 287 | en = updated_smiles.rfind(str(n2)) 288 | i_char = np.random.choice(len(add_char), 1, replace=False)[0] 289 | ic = np.random.choice(len(rand_char), add_char[i_char], replace=True) 290 | rchar = str('$') 291 | for i in range(0, add_char[i_char]): 292 | rchar = rchar + rchar.join(rand_char[ic[i]]) 293 | rchar = rchar + rchar.join('&') 294 | st = updated_smiles[:strn+2] + rchar + updated_smiles[strn+2:en-1] + rchar + updated_smiles[en-1:] 295 | updated_smiles = st 296 | # -------------------------------------------------------------------------------------------------------------------- 297 | 298 | if ring_index > 1: 299 | strt = updated_smiles.find(str(ring_index))-1; en = updated_smiles.rfind(str(ring_index))+1 300 | 301 | for k in range(0, strt): 302 | change_index.append(k) 303 | 304 | for k in range(en+1, len(updated_smiles)): 305 | change_index.append(k) 306 | 307 | doller_index = [pos for pos, char in enumerate(updated_smiles) if char == '$'] 308 | ampers_index = [pos for pos, char in enumerate(updated_smiles) if char == '&'] 309 | 310 | for pos in range(0, len(doller_index)): 311 | for k in range(doller_index[pos]+1, ampers_index[pos]): 312 | change_index.append(k) 313 | updated_smiles = updated_smiles.replace('$', '(') 314 | updated_smiles = updated_smiles.replace('&', ')') 315 | 316 | fixed_index = np.setdiff1d(np.array([i for i in range(0, len(updated_smiles))]), np.array(change_index)) 317 | #print(updated_smiles) 318 | #print(change_index) 319 | #input('PPP') 320 | for pos in range(0, np.int(len(doller_index)/2)): 321 | copy1 = [k for k in range(doller_index[pos]+1, ampers_index[pos])] 322 | copy2 = [k for k in range(doller_index[np.int(len(doller_index)/2)+(pos)]+1, ampers_index[np.int(len(doller_index)/2)+(pos)])] 323 | 324 | copy_loc.append([copy1, copy2]) 325 | 326 | if symmetry == False: 327 | if ring_index > 1: 328 | strt = updated_smiles.find(str(ring_index))-1; en = updated_smiles.rfind(str(ring_index))+1 329 | 330 | for k in range(0, strt): 331 | change_index.append(k) 332 | 333 | for k in range(en+1, len(updated_smiles)): 334 | change_index.append(k) 335 | 336 | 337 | add_char = np.array(np.arange(0,5, dtype=np.int32)) 338 | 339 | fixed_index = np.array([k for k in range(0, len(updated_smiles))]) 340 | i_char = np.random.choice(len(add_char), 1, replace=False)[0] 341 | ic = np.random.choice(len(rand_char), add_char[i_char], replace=True) 342 | rchar = ''.join('') 343 | for i in range(0, add_char[i_char]): 344 | rchar = rchar + rchar.join(rand_char[ic[i]]) 345 | 346 | 347 | if side_add == 'one': 348 | for k in range(len(updated_smiles), len(updated_smiles)+len(rchar)): 349 | change_index.append(k) 350 | updated_smiles = updated_smiles + rchar 351 | 352 | 353 | 354 | 355 | if side_add == 'both': 356 | for i in range(0, len(change_index)): 357 | change_index[i] = change_index[i] + len(rchar) 358 | 359 | for k in range(0, len(rchar)): 360 | change_index.append(k) 361 | 362 | for k in range(len(rchar)+len(updated_smiles), len(updated_smiles)+2*len(rchar)): 363 | change_index.append(k) 364 | updated_smiles = rchar + updated_smiles + rchar 365 | 366 | 367 | if len(change_index) == 0: 368 | num_change = np.random.choice(len(updated_smiles), 1, replace=False)[0] 369 | change_loc = np.random.choice(len(updated_smiles)-num_change, 1, replace=False)[0] 370 | for k in range(0,num_change): 371 | change_index.append(change_loc+k) 372 | 373 | 374 | 375 | fixed_index = np.setdiff1d(np.array([i for i in range(0, len(updated_smiles))]), np.array(change_index)) 376 | 377 | return updated_smiles, fixed_index, copy_loc 378 | # ---------------------------------------------------------------------------------------------------- 379 | def initialize_smiles_dimer(smiles, rings=1, symmetry=False, add_side = 0): 380 | # ----------------------------------------------------------------------------- 381 | ''' 382 | This function returns a smiles for Markov chain initialization and 383 | associated constraints. 384 | ''' 385 | # ----------------------------------------------------------------------------- 386 | fixed_index = np.array([], dtype=np.int) 387 | copy_loc = [] 388 | change_index = [] 389 | rand_char = ['C', 'C', 'O', 'C', 'C', 'C', 'C', 'C', 'P', '=', 'C'] 390 | 391 | if rings == 1: 392 | if symmetry == True: 393 | n1 = 19; n2 = 37 394 | s1 = smiles[:n1] 395 | s2 = smiles[n1:n2] 396 | s3 = smiles[n2:] 397 | add_char = np.array(np.arange(4,10, dtype=np.int32)) 398 | i_char = np.random.choice(len(add_char), 1, replace=False)[0] 399 | ic = np.random.choice(len(rand_char), add_char[i_char], replace=True) 400 | rchar = str('$') 401 | for i in range(0, add_char[i_char]): 402 | rchar = rchar + rchar.join(rand_char[ic[i]]) 403 | rchar = rchar + rchar.join('&') 404 | updated_smiles = s1 + rchar + s2 + rchar + s3 405 | else: 406 | updated_smiles = smiles 407 | # ----------------------------------------------------------------------------------- 408 | else: 409 | # ----------------------------------------------------------------------------------- 410 | if symmetry == True: 411 | n1 = 4; n2 = 31 412 | n3 = 13; n4 = 41 413 | i_sym = np.random.random(1) 414 | print('i_sym = ', i_sym) 415 | if i_sym < 0.5: 416 | s1 = smiles[:n1] 417 | s2 = smiles[n1:n3] 418 | s3 = smiles[n3:n2] 419 | s4 = smiles[n2:n4] 420 | s5 = smiles[n4:] 421 | # ----------------------------------------------------------------------------------- 422 | add_char = np.array(np.arange(2,5, dtype=np.int32)) 423 | i_char = np.random.choice(len(add_char), 1, replace=False)[0] 424 | ic = np.random.choice(len(rand_char), add_char[i_char], replace=True) 425 | rchar1 = str('$') 426 | for i in range(0, add_char[i_char]): 427 | rchar1 = rchar1 + rchar1.join(rand_char[ic[i]]) 428 | rchar1 = rchar1 + rchar1.join('&') 429 | # ------------------------------------------------------------------------------------ 430 | add_char = np.array(np.arange(2,5, dtype=np.int32)) 431 | i_char = np.random.choice(len(add_char), 1, replace=False)[0] 432 | ic = np.random.choice(len(rand_char), add_char[i_char], replace=True) 433 | rchar2 = str('$') 434 | for i in range(0, add_char[i_char]): 435 | rchar2 = rchar2 + rchar2.join(rand_char[ic[i]]) 436 | rchar2 = rchar2 + rchar2.join('&') 437 | updated_smiles = s1 + rchar1 + s2 + rchar2 + s3 + rchar1 + s4 + rchar2 + s5 438 | else: 439 | if np.random.random(1) < 0.5: 440 | s1 = smiles[:n1] 441 | s2 = smiles[n1:n2] 442 | s3 = smiles[n2:] 443 | else: 444 | s1 = smiles[:n3] 445 | s2 = smiles[n3:n4] 446 | s3 = smiles[n4:] 447 | add_char = np.array(np.arange(2,5, dtype=np.int32)) 448 | i_char = np.random.choice(len(add_char), 1, replace=False)[0] 449 | ic = np.random.choice(len(rand_char), add_char[i_char], replace=True) 450 | rchar = str('$') 451 | for i in range(0, add_char[i_char]): 452 | rchar = rchar + rchar.join(rand_char[ic[i]]) 453 | rchar = rchar + rchar.join('&') 454 | updated_smiles = s1 + rchar + s2 + rchar + s3 455 | else: 456 | updated_smiles = smiles 457 | # ------------------------------------------------------------------------------------ 458 | n1 = updated_smiles.find('N') + 1 459 | n2 = updated_smiles.rfind('N') + 1 460 | # ------------------------------------------------------------------------------------ 461 | add_char = np.array(np.arange(2,5, dtype=np.int32)) 462 | i_char = np.random.choice(len(add_char), 1, replace=False)[0] 463 | ic = np.random.choice(len(rand_char), add_char[i_char], replace=True) 464 | rchar = str('(') 465 | for i in range(0, add_char[i_char]): 466 | rchar = rchar + rchar.join(rand_char[ic[i]]) 467 | rchar = rchar + rchar.join(')') 468 | # ------------------------------------------------------------------------------------ 469 | if add_side == 1: 470 | s1 = updated_smiles[:n1] 471 | s2 = updated_smiles[n1:] 472 | updated_smiles = s1 + rchar + s2 473 | # ------------------------------------------------------------------------------------ 474 | if add_side == 2: 475 | s1 = updated_smiles[:n1] 476 | s2 = updated_smiles[n1:n2] 477 | s3 = updated_smiles[n2:] 478 | # ------------------------------------------------------------------------------------ 479 | add_char = np.array(np.arange(2,5, dtype=np.int32)) 480 | i_char = np.random.choice(len(add_char), 1, replace=False)[0] 481 | ic = np.random.choice(len(rand_char), add_char[i_char], replace=True) 482 | rchar2 = str('(') 483 | for i in range(0, add_char[i_char]): 484 | rchar2 = rchar2 + rchar2.join(rand_char[ic[i]]) 485 | rchar2 = rchar2 + rchar2.join(')') 486 | 487 | updated_smiles = s1 + rchar + s2 + rchar2 + s3 488 | # ------------------------------------------------------------------------------------ 489 | n1 = updated_smiles.find('N') 490 | n2 = updated_smiles.rfind('N') 491 | if add_side == 1: 492 | for k in range(n1+1, n1+len(rchar)): 493 | change_index.append(k) 494 | # ------------------------------------------------------------------------------------ 495 | if add_side == 2: 496 | for k in range(n2+1, n2+len(rchar2)): 497 | change_index.append(k) 498 | # ------------------------------------------------------------------------------------ 499 | doller_index = [pos for pos, char in enumerate(updated_smiles) if char == '$'] 500 | ampers_index = [pos for pos, char in enumerate(updated_smiles) if char == '&'] 501 | # ------------------------------------------------------------------------------------- 502 | for pos in range(0, np.int(len(doller_index)/2)): 503 | copy1 = [k for k in range(doller_index[pos]+1, ampers_index[pos])] 504 | copy2 = [k for k in range(doller_index[np.int(len(doller_index)/2)+(pos)]+1, ampers_index[np.int(len(doller_index)/2)+(pos)])] 505 | 506 | copy_loc.append([copy1, copy2]) 507 | 508 | for pos in range(0, len(doller_index)): 509 | for k in range(doller_index[pos]+1, ampers_index[pos]): 510 | change_index.append(k) 511 | updated_smiles = updated_smiles.replace('$', '(') 512 | updated_smiles = updated_smiles.replace('&', ')') 513 | fixed_index = np.setdiff1d(np.array([i for i in range(0, len(updated_smiles))]), np.array(change_index)) 514 | 515 | print(smiles) 516 | print(updated_smiles) 517 | print(change_index) 518 | print(fixed_index) 519 | 520 | return updated_smiles, fixed_index, copy_loc 521 | # ---------------------------------------------------------------------------------------- 522 | -------------------------------------------------------------------------------- /pubchem_data.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | import scipy as sc 3 | import json 4 | import urllib.request as ur 5 | import os.path 6 | from pprint import pprint 7 | from visualize_mol import draw_smiles 8 | from process_smiles import * 9 | 10 | 11 | def get_properties(pid): 12 | ### This function reads in cid of the PubChem data and returns a smile 13 | url = 'https://pubchem.ncbi.nlm.nih.gov/rest/pug/compound/cid/' + str(pid) + '/property/MolecularWeight,ExactMass,CanonicalSMILES/JSON' 14 | try: 15 | data = ur.urlopen(url).read().decode('utf-8') 16 | json_data = json.loads(data) 17 | smiles = json_data['PropertyTable']['Properties'][0]['CanonicalSMILES'] 18 | mass = json_data['PropertyTable']['Properties'][0]['ExactMass'] 19 | mol_weight = json_data['PropertyTable']['Properties'][0]['MolecularWeight'] 20 | exception = 0 21 | except: 22 | smiles = [] 23 | mass = [] 24 | mol_weight = [] 25 | exception = 1 26 | 27 | return [exception, smiles, mass, mol_weight] 28 | 29 | 30 | 31 | 32 | # this program reads molecule data from PubChem and stores relevent properties in a file. 33 | 34 | 35 | def save_pubchem_data(initial_pid, final_pid, fname): 36 | # fid = open(fname, 'a') 37 | all_pids = [i for i in range(initial_pid, final_pid+1)] 38 | 39 | fjson = fname + '.jsn' 40 | 41 | if os.path.isfile(fjson): 42 | pprint('File Exists') 43 | with open(fjson, 'r') as fid: 44 | data = fid.read() 45 | json_data = json.loads(data) 46 | pids = json_data['Properties']['Pid'] 47 | smiles = json_data['Properties']['SMILES'] 48 | mass = json_data['Properties']['MolecularMass'] 49 | mol_weight = json_data['Properties']['MolecularWeight'] 50 | 51 | else: 52 | pids = []; smiles = []; mass = []; mol_weight = [] 53 | req_pids = set(all_pids).symmetric_difference(pids) 54 | for pid in req_pids: 55 | pprint('PID = ' + str(pid)) 56 | properties = get_properties(pid) 57 | exception = properties[0] 58 | if exception == 0: 59 | pids.append(pid) 60 | smiles.append(properties[1]) 61 | mass.append(properties[2]) 62 | mol_weight.append(properties[3]) 63 | 64 | mol_properties = json.dumps({"Properties":{'Pid': pids, 'SMILES':smiles, 'MolecularMass': mass, 'MolecularWeight': mol_weight}}) 65 | 66 | 67 | with open(fjson, 'w') as fid: 68 | fid.write(mol_properties) 69 | 70 | fdata = fname + '.dat' 71 | 72 | with open(fdata, 'w') as fid: 73 | for i in range(0, len(pids)): 74 | fid.write('{} {} {} {} \n'.format(str(pids[i]), smiles[i], str(mass[i]), str(mol_weight[i]))) 75 | #pprint(mol_properties) 76 | 77 | 78 | 79 | # ------------------------------------------------------------- 80 | # This program reads data from a json file . 81 | # ------------------------------------------------------------- 82 | def read_json_data(fname, redox = False, is_properties = False, max_smiles_length = 1000): 83 | 84 | fjson = fname + '.jsn' 85 | if os.path.isfile(fjson): 86 | pprint('File Exists') 87 | with open(fjson, 'rb') as fid: 88 | #data = fid.read() 89 | json_data = json.load(fid) 90 | pids = json_data['Properties']['Pid'] 91 | smiles = json_data['Properties']['SMILES'] 92 | if is_properties == True: 93 | #mass = json_data['Properties']['MolecularMass'] 94 | #mol_weight = json_data['Properties']['MolecularWeight'] 95 | #xlogp = json_data['Properties']['XLogP'] 96 | #tpsa = json_data['Properties']['TPSA'] 97 | #complexity = json_data['Properties']['Complexity'] 98 | #charge = json_data['Properties']['Charge'] 99 | if redox == True: 100 | oxid = json_data['Properties']['Oxidation'] 101 | red = json_data['Properties']['Reduction'] 102 | homo = json_data['Properties']['homo'] 103 | lumo = json_data['Properties']['lumo'] 104 | 105 | else: 106 | pprint('File does not exists') 107 | pids = []; smiles = []; mass = []; mol_weight = [] 108 | lsmiles = len(smiles) 109 | #print(lsmiles) 110 | sm1 = []; mm1 = []; mlw1 = []; ox1 = []; rd1 = []; hm1 = []; lm1 = [] 111 | xlogp1 = []; tpsa1 = []; cmplx1 = []; chrg1 = [] 112 | for i in range(0, lsmiles): 113 | #print(i) 114 | if len(smiles[i]) <= max_smiles_length: 115 | sm1.append(smiles[i]) 116 | if is_properties == True: 117 | #mm1.append(mass[i]) 118 | #mlw1.append(mol_weight[i]) 119 | #xlogp1.append(xlogp[i]) 120 | #tpsa1.append(tpsa[i]) 121 | #cmplx1.append(complexity[i]) 122 | #chrg1.append(charge[i]) 123 | if redox == True: 124 | ox1.append(oxid[i]) 125 | rd1.append(red[i]) 126 | hm1.append(homo[i]) 127 | lm1.append(lumo[i]) 128 | 129 | 130 | properties = [] 131 | 132 | 133 | if is_properties == True: 134 | #properties.append(mm1); properties.append(mlw1); 135 | if redox == True: 136 | #properties.append(ox1); properties.append(ox1); properties.append(rd1); properties.append(rd1) 137 | properties.append(hm1); properties.append(lm1); 138 | #properties.append(ox1); properties.append(rd1); 139 | #print(min(properties[0]), min(properties[1]), min(properties[2]), min(properties[3])) 140 | #print(max(properties[0]), max(properties[1]), max(properties[2]), max(properties[3])) 141 | 142 | #properties.append(xlogp1); properties.append(tpsa1) 143 | #properties.append(cmplx1); properties.append(chrg1); 144 | #return [smiles, properties] 145 | 146 | return [sm1, properties] 147 | 148 | # ----------------------------------------------------------------------------- 149 | # For data pruning 150 | # ----------------------------------------------------------------------------- 151 | 152 | def prune(smiles, properties): 153 | sm1 = []; hm = []; lm = []; ox = []; rd = []; prop = [] 154 | 155 | 156 | homo = np.array(properties[0]); lumo = np.array(properties[1]) 157 | #oxid = np.array(properties[2]); red = np.array(properties[3]) 158 | #log_oxid = np.log(oxid); log_red = np.log(red) 159 | for i in range(0, len(homo)): 160 | #print(i, len(oxid), len(smiles)) 161 | if (smiles[i].count('.')==0 and smiles[i].count('Br') == 0 and 162 | smiles[i].count('Os')==0 and smiles[i].count('V')==0 and smiles[i].count('Tb')==0 163 | and smiles[i].count('Ru')==0 and smiles[i].count('Pt')==0 and smiles[i].count('Eu')==0 and smiles[i].count('[')<5 164 | and smiles[i].count('Ti')==0 and smiles[i].count('Zr')==0 and smiles[i].count('Nd')==0 and smiles[i].count('Er')==0 165 | and smiles[i].count('Y')==0 and smiles[i].count('U')==0 and smiles[i].count('Mo')==0 and smiles[i].count('La')==0 166 | and smiles[i].count('Re')==0): 167 | # if smiles[i].count('CCC(=O)Cl') > 0: 168 | sm1.append(smiles[i]); 169 | hm.append(homo[i]); lm.append(lumo[i]) 170 | #ox.append(oxid[i]); rd.append(red[i]) 171 | #lox.append(log_oxid[i]); lrd.append(log_red[i]) 172 | 173 | prop.append(hm); prop.append(lm); #prop.append(ox); prop.append(rd) 174 | #prop.append(xl); prop.append(tp); prop.append(compl); prop.append(ch) 175 | #prop.append(lox); prop.append(lrd) 176 | return [sm1, prop] 177 | 178 | 179 | # ----------------------------------------------------------------------------- 180 | # For checking duplicate smiles 181 | # ----------------------------------------------------------------------------- 182 | def list_duplicates(seq): 183 | seen = set() 184 | seen_add = seen.add 185 | # adds all elements it doesn't know yet to seen and all other to seen_twice 186 | seen_twice = set( x for x in seq if x in seen or seen_add(x) ) 187 | # turn the set into a list (as requested) 188 | return list( seen_twice ) 189 | 190 | 191 | 192 | # ----------------------------------------------------------------------------- 193 | # Pruning for duplicate smiles 194 | # ----------------------------------------------------------------------------- 195 | def prune_dupicates(smiles, properties): 196 | duplicate_smiles = list_duplicates(smiles) 197 | pruned_smiles = []; pruned_properties = [] 198 | mw = properties[0]; mm = properties[1]; ox = properties[2]; rd = properties[3] 199 | mw1 = []; mm1 = []; ox1 = []; rd1 = []; 200 | for i in range(0,len(smiles)): 201 | is_duplicate = 0 202 | for j in range(0, len(duplicate_smiles)): 203 | if smiles[i] == duplicate_smiles[j]: 204 | is_duplicate = 1 205 | if is_duplicate == 0: 206 | pruned_smiles.append(smiles[i]) 207 | mw1.append(mw[i]); mm1.append(mm[i]); ox1.append(ox[i]); rd1.append(rd[i]) 208 | pruned_properties.append(mw1); pruned_properties.append(mm1); 209 | pruned_properties.append(ox1); pruned_properties.append(rd1) 210 | return pruned_smiles, pruned_properties 211 | -------------------------------------------------------------------------------- /rbm_trained.pkl: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/piyushtagade/SLAMDUNCS/4227a92121091b017edde679a615df94690cc0ae/rbm_trained.pkl -------------------------------------------------------------------------------- /train_dnn.py: -------------------------------------------------------------------------------- 1 | # ------------------------------------------------------- 2 | # Training a deep neural network on properties dataset 3 | # ------------------------------------------------------- 4 | import scipy as sc 5 | import numpy as np 6 | 7 | from pprint import pprint 8 | # Load the dataset 9 | from deep_learning import * 10 | from process_smiles import * 11 | from pubchem_data import * 12 | import pickle 13 | from anova import * 14 | import time 15 | from deep_neural_network import * 16 | # ----------------------------------------------------------------------------- 17 | 18 | # ----------------------------------------------------------------------------- 19 | def train_dnn(pubchem_data_filename, properties_data_filename, 20 | max_smiles_length=100): 21 | # ----------------------------------------------------------------------------- 22 | # For ensuring that same random number is always generated 23 | # ----------------------------------------------------------------------------- 24 | np.random.seed(100) 25 | # ----------------------------------------------------------------------------- 26 | # For ensuring that same random number is always generated 27 | # ----------------------------------------------------------------------------- 28 | smiles, properties_all = read_json_data(pubchem_data_filename, redox = False, max_smiles_length=max_smiles_length) 29 | 30 | train_set_all, test_set_all = create_smiles_dataset(smiles, properties_data=[], max_smiles_length=max_smiles_length, 31 | percentage_train_data = 0.3) 32 | 33 | 34 | smiles1, properties1 = read_json_data(properties_data_filename, redox = True, is_properties = True, 35 | max_smiles_length=max_smiles_length) 36 | 37 | smiles, properties = prune(smiles1, properties1) 38 | tot_data = len(smiles) 39 | train_set_redox, test_set_redox = create_smiles_dataset(smiles, properties_data = properties, 40 | max_smiles_length=max_smiles_length, percentage_train_data = 0.95) 41 | 42 | 43 | target_max = np.max(np.concatenate((np.array(train_set_redox[1]), np.array(test_set_redox[1])), axis=0), axis=0) 44 | target_min = np.min(np.concatenate((np.array(train_set_redox[1]), np.array(test_set_redox[1])), axis=0), axis=0) 45 | 46 | 47 | training_data = np.concatenate((np.array(train_set_all[0]), np.array(train_set_redox[0])), axis=0) 48 | 49 | # ----------------------------------------------------------------------------- 50 | [num_data, num_vis] = np.shape(training_data) 51 | 52 | 53 | # ----------------------------------------------------------------------------- 54 | # Starting DBM 55 | # ----------------------------------------------------------------------------- 56 | # ----------------------------------------------------------------------------- 57 | # Now pre-training the DBM 58 | # ----------------------------------------------------------------------------- 59 | num_test_data = np.shape(test_set_redox)[0] 60 | 61 | 62 | train_targ = (np.array(train_set_redox[1]) - target_min)/(target_max - target_min); 63 | #train_targ = (np.array(train_set_redox[1]))/(target_max); 64 | training_target = train_targ 65 | 66 | print("Training Target = ", np.sum(np.sum(training_target, axis = 0))) 67 | 68 | 69 | num_targ = np.shape(training_target)[1] 70 | 71 | print(num_vis, num_targ) 72 | [num_data, num_vis] = np.shape(training_data) 73 | 74 | 75 | num_nodes = [num_vis, 800, 500, 200, 200, num_targ] 76 | 77 | 78 | num_layers = len(num_nodes) 79 | dropout = 0.7*np.ones(len(num_nodes)-1) 80 | dnn = Deep_Neural_Network(num_layers, num_nodes, dropout = dropout) 81 | 82 | 83 | # ----------------------------------------------------------------------------- 84 | # Setting pretraining options 85 | # ----------------------------------------------------------------------------- 86 | maxepoch = 100 87 | learning_rate_dbn = 0.01 88 | cdn_dbn = 1 89 | 90 | learning_rate_gb = 0.01 # This value is taken after experiments on rbm_pubchem code 91 | cdn_gb = 1 # This value is taken after experiments on rbm_pubchem code 92 | 93 | numbatches = int(floor(num_data/200)) 94 | #numbatches = 10 95 | dnn.pretrain_options(maxepoch, numbatches, learning_rate_dbn, learning_rate_gb, cdn_dbn, cdn_gb, 96 | method = 'Gibbs', initialmomentum=0.5, finalmomentum=0.9, initialepochs=5) 97 | 98 | # ----------------------------------------------------------------------------- 99 | # Setting training options 100 | # ----------------------------------------------------------------------------- 101 | maxepoch = 10 102 | learning_rate = 0.1 103 | numbatches = int(floor(num_data/600)) 104 | #numbatches = 10 105 | dnn.train_options(maxepoch, numbatches, learning_rate, initialmomentum=0.8, finalmomentum=0.9, initialepochs=5) 106 | 107 | # ----------------------------------------------------------------------------- 108 | # Pretraining the DNN 109 | # ----------------------------------------------------------------------------- 110 | 111 | dnn.pretrain(training_data, training_target) 112 | 113 | with open('dnn_pretrain.pkl', 'wb') as fp: 114 | pickle.dump(dnn, fp) 115 | 116 | 117 | 118 | smiles1, properties1 = read_json_data(properties_data_filename, redox = True, is_properties = True, 119 | max_smiles_length=max_smiles_length) 120 | 121 | smiles, properties = prune(smiles1, properties1) 122 | 123 | #smiles, properties = prune(smiles1, properties1) 124 | 125 | train_set_redox, test_set_redox = create_smiles_dataset(smiles, properties_data = properties, 126 | max_smiles_length=max_smiles_length, 127 | percentage_train_data = 0.80, use_doe = False) 128 | training_data = np.array(train_set_redox[0]) 129 | 130 | [num_data, num_vis] = np.shape(training_data) 131 | 132 | train_targ = (np.array(train_set_redox[1]) - target_min)/(target_max - target_min); 133 | #train_targ = (np.array(train_set_redox[1]))/(target_max); 134 | training_target = train_targ 135 | 136 | test_data = np.array(test_set_redox[0]) 137 | 138 | #test_data_dbm = dbn.forward_pass(test_data) 139 | 140 | test_targ = (np.array(test_set_redox[1]) - target_min)/(target_max - target_min); 141 | # test_targ = (np.array(test_set_redox[1]))/(target_max); 142 | num_targ_data = np.shape(train_targ)[0] 143 | 144 | test_target = test_targ 145 | # ------------------------------------------------------------------------------ 146 | epochs, errors = dnn.train(training_data, training_target) 147 | # ------------------------------------------------------------------------------ 148 | with open('trained_dnn.pkl', 'wb') as fp: 149 | pickle.dump(dnn, fp) 150 | # ------------------------------------------------------------------------------ -------------------------------------------------------------------------------- /train_rbm.py: -------------------------------------------------------------------------------- 1 | # ----------------------------------------------------------------------------- 2 | # Training the Restricted Boltzmann Machine on the pubchem dataset 3 | # ----------------------------------------------------------------------------- 4 | import scipy as sc 5 | import numpy as np 6 | 7 | from pprint import pprint 8 | # Load the dataset 9 | from deep_learning import * 10 | from process_smiles import * 11 | from pubchem_data import * 12 | import pickle 13 | from anova import * 14 | import time 15 | from deep_neural_network import * 16 | # ----------------------------------------------------------------------------- 17 | # Method to train the Restricted Boltzmann Machine 18 | # ----------------------------------------------------------------------------- 19 | def train_rbm(pubchem_data_filename, max_smiles_length=100): 20 | # ----------------------------------------------------------------------------- 21 | ''' 22 | Training the RBM 23 | ''' 24 | # ----------------------------------------------------------------------------- 25 | smiles, properties_all = read_json_data(pubchem_data_filename, redox = False, 26 | max_smiles_length=max_smiles_length) 27 | 28 | train_set_all, test_set_all = create_smiles_dataset(smiles, properties_data=[], 29 | max_smiles_length=max_smiles_length, percentage_train_data = 0.3) 30 | training_data = np.array(train_set_all[0]) 31 | # ----------------------------------------------------------------------------- 32 | [num_data, num_vis] = np.shape(training_data) 33 | 34 | # ----------------------------------------------------------------------------- 35 | # Creating an instance of the RBM class 36 | # ----------------------------------------------------------------------------- 37 | rbm = Restricted_Boltzmann_Machine(num_vis, 800) 38 | # ----------------------------------------------------------------------------- 39 | # Setting training parameters 40 | # ----------------------------------------------------------------------------- 41 | numbatches = int(floor(num_data/1000)) 42 | maxepoch = 3650 43 | learning_rate = 0.01 44 | cdn = 1 45 | rbm.options(maxepoch, numbatches, learning_rate, cdn, method = 'Gibbs', 46 | vistype='BB', initialmomentum=0.5, finalmomentum=0.9, initialepochs=5) 47 | # ----------------------------------------------------------------------------- 48 | # Training the RBM 49 | # ----------------------------------------------------------------------------- 50 | error, epochs = rbm.train(training_data, visualize=False) 51 | # ----------------------------------------------------------------------------- 52 | # Saving the trained RBM as a pickle file for future usage 53 | # ----------------------------------------------------------------------------- 54 | with open('rbm_trained.pkl', 'wb') as fp: 55 | pickle.dump(rbm, fp) -------------------------------------------------------------------------------- /visualize_mol.py: -------------------------------------------------------------------------------- 1 | # ------------------------------------------------------------------------- 2 | # Functions for visualizing a 2D molecule structure from canonical SMILES 3 | # ------------------------------------------------------------------------- 4 | 5 | 6 | import numpy as np 7 | import scipy as sc 8 | 9 | from matplotlib import pyplot as plt 10 | 11 | 12 | def draw_smiles(smiles): 13 | # -------------------------------------------------- 14 | # Drawing SMILES 15 | # -------------------------------------------------- 16 | nimg = len(smiles) 17 | 18 | fig = plt.figure(1) 19 | nrow = np.int(np.sqrt(np.int(nimg))) 20 | ncol = nrow 21 | 22 | for i in range(0, nrow*ncol): 23 | ax = fig.add_subplot(nrow*ncol, 1, i+ 1) 24 | plt.text(0.1, 0.1, smiles[i], transform=ax.transAxes) 25 | plt.axis('off') 26 | 27 | plt.show() 28 | --------------------------------------------------------------------------------