├── README.md ├── __pycache__ └── clustering.cpython-36.pyc ├── figures ├── Figure3 - Supplementary │ ├── G2P-2_AE_512_100_qm9_qm9_greatest_0_nodes.png │ ├── G2P-2_AE_512_100_qm9_qm9_greatest_10_nodes.png │ ├── G2P-2_AE_512_100_qm9_qm9_greatest_11_nodes.png │ ├── G2P-2_AE_512_100_qm9_qm9_greatest_12_nodes.png │ ├── G2P-2_AE_512_100_qm9_qm9_greatest_13_nodes.png │ ├── G2P-2_AE_512_100_qm9_qm9_greatest_14_nodes.png │ ├── G2P-2_AE_512_100_qm9_qm9_greatest_15_nodes.png │ ├── G2P-2_AE_512_100_qm9_qm9_greatest_16_nodes.png │ ├── G2P-2_AE_512_100_qm9_qm9_greatest_17_nodes.png │ ├── G2P-2_AE_512_100_qm9_qm9_greatest_18_nodes.png │ ├── G2P-2_AE_512_100_qm9_qm9_greatest_19_nodes.png │ ├── G2P-2_AE_512_100_qm9_qm9_greatest_1_nodes.png │ ├── G2P-2_AE_512_100_qm9_qm9_greatest_20_nodes.png │ ├── G2P-2_AE_512_100_qm9_qm9_greatest_21_nodes.png │ ├── G2P-2_AE_512_100_qm9_qm9_greatest_22_nodes.png │ ├── G2P-2_AE_512_100_qm9_qm9_greatest_23_nodes.png │ ├── G2P-2_AE_512_100_qm9_qm9_greatest_24_nodes.png │ ├── G2P-2_AE_512_100_qm9_qm9_greatest_25_nodes.png │ ├── G2P-2_AE_512_100_qm9_qm9_greatest_26_nodes.png │ ├── G2P-2_AE_512_100_qm9_qm9_greatest_27_nodes.png │ ├── G2P-2_AE_512_100_qm9_qm9_greatest_28_nodes.png │ ├── G2P-2_AE_512_100_qm9_qm9_greatest_29_nodes.png │ ├── G2P-2_AE_512_100_qm9_qm9_greatest_2_nodes.png │ ├── G2P-2_AE_512_100_qm9_qm9_greatest_30.png │ ├── G2P-2_AE_512_100_qm9_qm9_greatest_3_nodes.png │ ├── G2P-2_AE_512_100_qm9_qm9_greatest_4_nodes.png │ ├── G2P-2_AE_512_100_qm9_qm9_greatest_5_nodes.png │ ├── G2P-2_AE_512_100_qm9_qm9_greatest_6_nodes.png │ ├── G2P-2_AE_512_100_qm9_qm9_greatest_7_nodes.png │ ├── G2P-2_AE_512_100_qm9_qm9_greatest_8_nodes.png │ ├── G2P-2_AE_512_100_qm9_qm9_greatest_9_nodes.png │ ├── G2P-2_AE_512_100_qm9_qm9_least_0_nodes.png │ ├── G2P-2_AE_512_100_qm9_qm9_least_10_nodes.png │ ├── G2P-2_AE_512_100_qm9_qm9_least_11_nodes.png │ ├── G2P-2_AE_512_100_qm9_qm9_least_12_nodes.png │ ├── G2P-2_AE_512_100_qm9_qm9_least_13_nodes.png │ ├── G2P-2_AE_512_100_qm9_qm9_least_14_nodes.png │ ├── G2P-2_AE_512_100_qm9_qm9_least_15_nodes.png │ ├── G2P-2_AE_512_100_qm9_qm9_least_16_nodes.png │ ├── G2P-2_AE_512_100_qm9_qm9_least_17_nodes.png │ ├── G2P-2_AE_512_100_qm9_qm9_least_18_nodes.png │ ├── G2P-2_AE_512_100_qm9_qm9_least_19_nodes.png │ ├── G2P-2_AE_512_100_qm9_qm9_least_1_nodes.png │ ├── G2P-2_AE_512_100_qm9_qm9_least_20_nodes.png │ ├── G2P-2_AE_512_100_qm9_qm9_least_21_nodes.png │ ├── G2P-2_AE_512_100_qm9_qm9_least_22_nodes.png │ ├── G2P-2_AE_512_100_qm9_qm9_least_23_nodes.png │ ├── G2P-2_AE_512_100_qm9_qm9_least_24_nodes.png │ ├── G2P-2_AE_512_100_qm9_qm9_least_25_nodes.png │ ├── G2P-2_AE_512_100_qm9_qm9_least_26_nodes.png │ ├── G2P-2_AE_512_100_qm9_qm9_least_27_nodes.png │ ├── G2P-2_AE_512_100_qm9_qm9_least_28_nodes.png │ ├── G2P-2_AE_512_100_qm9_qm9_least_29_nodes.png │ ├── G2P-2_AE_512_100_qm9_qm9_least_2_nodes.png │ ├── G2P-2_AE_512_100_qm9_qm9_least_30.png │ ├── G2P-2_AE_512_100_qm9_qm9_least_3_nodes.png │ ├── G2P-2_AE_512_100_qm9_qm9_least_4_nodes.png │ ├── G2P-2_AE_512_100_qm9_qm9_least_5_nodes.png │ ├── G2P-2_AE_512_100_qm9_qm9_least_6_nodes.png │ ├── G2P-2_AE_512_100_qm9_qm9_least_7_nodes.png │ ├── G2P-2_AE_512_100_qm9_qm9_least_8_nodes.png │ ├── G2P-2_AE_512_100_qm9_qm9_least_9_nodes.png │ ├── G2P-2_cep_512_100_CEP_CEP_greatest_0_nodes.png │ ├── G2P-2_cep_512_100_CEP_CEP_greatest_10_nodes.png │ ├── G2P-2_cep_512_100_CEP_CEP_greatest_11_nodes.png │ ├── G2P-2_cep_512_100_CEP_CEP_greatest_12_nodes.png │ ├── G2P-2_cep_512_100_CEP_CEP_greatest_13_nodes.png │ ├── G2P-2_cep_512_100_CEP_CEP_greatest_14_nodes.png │ ├── G2P-2_cep_512_100_CEP_CEP_greatest_15_nodes.png │ ├── G2P-2_cep_512_100_CEP_CEP_greatest_16_nodes.png │ ├── G2P-2_cep_512_100_CEP_CEP_greatest_17_nodes.png │ ├── G2P-2_cep_512_100_CEP_CEP_greatest_18_nodes.png │ ├── G2P-2_cep_512_100_CEP_CEP_greatest_19_nodes.png │ ├── G2P-2_cep_512_100_CEP_CEP_greatest_1_nodes.png │ ├── G2P-2_cep_512_100_CEP_CEP_greatest_20_nodes.png │ ├── G2P-2_cep_512_100_CEP_CEP_greatest_21_nodes.png │ ├── G2P-2_cep_512_100_CEP_CEP_greatest_22_nodes.png │ ├── G2P-2_cep_512_100_CEP_CEP_greatest_23_nodes.png │ ├── G2P-2_cep_512_100_CEP_CEP_greatest_24_nodes.png │ ├── G2P-2_cep_512_100_CEP_CEP_greatest_25_nodes.png │ ├── G2P-2_cep_512_100_CEP_CEP_greatest_26_nodes.png │ ├── G2P-2_cep_512_100_CEP_CEP_greatest_27_nodes.png │ ├── G2P-2_cep_512_100_CEP_CEP_greatest_28_nodes.png │ ├── G2P-2_cep_512_100_CEP_CEP_greatest_29_nodes.png │ ├── G2P-2_cep_512_100_CEP_CEP_greatest_2_nodes.png │ ├── G2P-2_cep_512_100_CEP_CEP_greatest_3_nodes.png │ ├── G2P-2_cep_512_100_CEP_CEP_greatest_4_nodes.png │ ├── G2P-2_cep_512_100_CEP_CEP_greatest_5_nodes.png │ ├── G2P-2_cep_512_100_CEP_CEP_greatest_6_nodes.png │ ├── G2P-2_cep_512_100_CEP_CEP_greatest_7_nodes.png │ ├── G2P-2_cep_512_100_CEP_CEP_greatest_8_nodes.png │ ├── G2P-2_cep_512_100_CEP_CEP_greatest_9_nodes.png │ ├── G2P-2_cep_512_100_CEP_CEP_least_0_nodes.png │ ├── G2P-2_cep_512_100_CEP_CEP_least_10_nodes.png │ ├── G2P-2_cep_512_100_CEP_CEP_least_11_nodes.png │ ├── G2P-2_cep_512_100_CEP_CEP_least_12_nodes.png │ ├── G2P-2_cep_512_100_CEP_CEP_least_13_nodes.png │ ├── G2P-2_cep_512_100_CEP_CEP_least_14_nodes.png │ ├── G2P-2_cep_512_100_CEP_CEP_least_15_nodes.png │ ├── G2P-2_cep_512_100_CEP_CEP_least_16_nodes.png │ ├── G2P-2_cep_512_100_CEP_CEP_least_17_nodes.png │ ├── G2P-2_cep_512_100_CEP_CEP_least_18_nodes.png │ ├── G2P-2_cep_512_100_CEP_CEP_least_19_nodes.png │ ├── G2P-2_cep_512_100_CEP_CEP_least_1_nodes.png │ ├── G2P-2_cep_512_100_CEP_CEP_least_20_nodes.png │ ├── G2P-2_cep_512_100_CEP_CEP_least_21_nodes.png │ ├── G2P-2_cep_512_100_CEP_CEP_least_22_nodes.png │ ├── G2P-2_cep_512_100_CEP_CEP_least_23_nodes.png │ ├── G2P-2_cep_512_100_CEP_CEP_least_24_nodes.png │ ├── G2P-2_cep_512_100_CEP_CEP_least_25_nodes.png │ ├── G2P-2_cep_512_100_CEP_CEP_least_26_nodes.png │ ├── G2P-2_cep_512_100_CEP_CEP_least_27_nodes.png │ ├── G2P-2_cep_512_100_CEP_CEP_least_28_nodes.png │ ├── G2P-2_cep_512_100_CEP_CEP_least_29_nodes.png │ ├── G2P-2_cep_512_100_CEP_CEP_least_2_nodes.png │ ├── G2P-2_cep_512_100_CEP_CEP_least_3_nodes.png │ ├── G2P-2_cep_512_100_CEP_CEP_least_4_nodes.png │ ├── G2P-2_cep_512_100_CEP_CEP_least_5_nodes.png │ ├── G2P-2_cep_512_100_CEP_CEP_least_6_nodes.png │ ├── G2P-2_cep_512_100_CEP_CEP_least_7_nodes.png │ ├── G2P-2_cep_512_100_CEP_CEP_least_8_nodes.png │ ├── G2P-2_cep_512_100_CEP_CEP_least_9_nodes.png │ ├── G2P-2_logP_512_100_ZINC_re_ZINC_greatest_0_nodes.png │ ├── G2P-2_logP_512_100_ZINC_re_ZINC_greatest_10_nodes.png │ ├── G2P-2_logP_512_100_ZINC_re_ZINC_greatest_11_nodes.png │ ├── G2P-2_logP_512_100_ZINC_re_ZINC_greatest_12_nodes.png │ ├── G2P-2_logP_512_100_ZINC_re_ZINC_greatest_13_nodes.png │ ├── G2P-2_logP_512_100_ZINC_re_ZINC_greatest_14_nodes.png │ ├── G2P-2_logP_512_100_ZINC_re_ZINC_greatest_15_nodes.png │ ├── G2P-2_logP_512_100_ZINC_re_ZINC_greatest_16_nodes.png │ ├── G2P-2_logP_512_100_ZINC_re_ZINC_greatest_17_nodes.png │ ├── G2P-2_logP_512_100_ZINC_re_ZINC_greatest_18_nodes.png │ ├── G2P-2_logP_512_100_ZINC_re_ZINC_greatest_19_nodes.png │ ├── G2P-2_logP_512_100_ZINC_re_ZINC_greatest_1_nodes.png │ ├── G2P-2_logP_512_100_ZINC_re_ZINC_greatest_20_nodes.png │ ├── G2P-2_logP_512_100_ZINC_re_ZINC_greatest_21_nodes.png │ ├── G2P-2_logP_512_100_ZINC_re_ZINC_greatest_22_nodes.png │ ├── G2P-2_logP_512_100_ZINC_re_ZINC_greatest_23_nodes.png │ ├── G2P-2_logP_512_100_ZINC_re_ZINC_greatest_24_nodes.png │ ├── G2P-2_logP_512_100_ZINC_re_ZINC_greatest_2_nodes.png │ ├── G2P-2_logP_512_100_ZINC_re_ZINC_greatest_3_nodes.png │ ├── G2P-2_logP_512_100_ZINC_re_ZINC_greatest_4_nodes.png │ ├── G2P-2_logP_512_100_ZINC_re_ZINC_greatest_5_nodes.png │ ├── G2P-2_logP_512_100_ZINC_re_ZINC_greatest_6_nodes.png │ ├── G2P-2_logP_512_100_ZINC_re_ZINC_greatest_7_nodes.png │ ├── G2P-2_logP_512_100_ZINC_re_ZINC_greatest_8_nodes.png │ ├── G2P-2_logP_512_100_ZINC_re_ZINC_greatest_9_nodes.png │ ├── G2P-2_logP_512_100_ZINC_re_ZINC_least_0_nodes.png │ ├── G2P-2_logP_512_100_ZINC_re_ZINC_least_10_nodes.png │ ├── G2P-2_logP_512_100_ZINC_re_ZINC_least_11_nodes.png │ ├── G2P-2_logP_512_100_ZINC_re_ZINC_least_12_nodes.png │ ├── G2P-2_logP_512_100_ZINC_re_ZINC_least_13_nodes.png │ ├── G2P-2_logP_512_100_ZINC_re_ZINC_least_14_nodes.png │ ├── G2P-2_logP_512_100_ZINC_re_ZINC_least_15_nodes.png │ ├── G2P-2_logP_512_100_ZINC_re_ZINC_least_16_nodes.png │ ├── G2P-2_logP_512_100_ZINC_re_ZINC_least_17_nodes.png │ ├── G2P-2_logP_512_100_ZINC_re_ZINC_least_18_nodes.png │ ├── G2P-2_logP_512_100_ZINC_re_ZINC_least_19_nodes.png │ ├── G2P-2_logP_512_100_ZINC_re_ZINC_least_1_nodes.png │ ├── G2P-2_logP_512_100_ZINC_re_ZINC_least_20_nodes.png │ ├── G2P-2_logP_512_100_ZINC_re_ZINC_least_21_nodes.png │ ├── G2P-2_logP_512_100_ZINC_re_ZINC_least_22_nodes.png │ ├── G2P-2_logP_512_100_ZINC_re_ZINC_least_23_nodes.png │ ├── G2P-2_logP_512_100_ZINC_re_ZINC_least_24_nodes.png │ ├── G2P-2_logP_512_100_ZINC_re_ZINC_least_2_nodes.png │ ├── G2P-2_logP_512_100_ZINC_re_ZINC_least_3_nodes.png │ ├── G2P-2_logP_512_100_ZINC_re_ZINC_least_4_nodes.png │ ├── G2P-2_logP_512_100_ZINC_re_ZINC_least_5_nodes.png │ ├── G2P-2_logP_512_100_ZINC_re_ZINC_least_6_nodes.png │ ├── G2P-2_logP_512_100_ZINC_re_ZINC_least_7_nodes.png │ ├── G2P-2_logP_512_100_ZINC_re_ZINC_least_8_nodes.png │ ├── G2P-2_logP_512_100_ZINC_re_ZINC_least_9_nodes.png │ ├── G2P-4_AE_512_100_qm9_qm9_greatest_0_nodes.png │ ├── G2P-4_AE_512_100_qm9_qm9_greatest_10_nodes.png │ ├── G2P-4_AE_512_100_qm9_qm9_greatest_11_nodes.png │ ├── G2P-4_AE_512_100_qm9_qm9_greatest_12_nodes.png │ ├── G2P-4_AE_512_100_qm9_qm9_greatest_13_nodes.png │ ├── G2P-4_AE_512_100_qm9_qm9_greatest_14_nodes.png │ ├── G2P-4_AE_512_100_qm9_qm9_greatest_15_nodes.png │ ├── G2P-4_AE_512_100_qm9_qm9_greatest_16_nodes.png │ ├── G2P-4_AE_512_100_qm9_qm9_greatest_17_nodes.png │ ├── G2P-4_AE_512_100_qm9_qm9_greatest_18_nodes.png │ ├── G2P-4_AE_512_100_qm9_qm9_greatest_19_nodes.png │ ├── G2P-4_AE_512_100_qm9_qm9_greatest_1_nodes.png │ ├── G2P-4_AE_512_100_qm9_qm9_greatest_20_nodes.png │ ├── G2P-4_AE_512_100_qm9_qm9_greatest_21_nodes.png │ ├── G2P-4_AE_512_100_qm9_qm9_greatest_22_nodes.png │ ├── G2P-4_AE_512_100_qm9_qm9_greatest_23_nodes.png │ ├── G2P-4_AE_512_100_qm9_qm9_greatest_24_nodes.png │ ├── G2P-4_AE_512_100_qm9_qm9_greatest_25_nodes.png │ ├── G2P-4_AE_512_100_qm9_qm9_greatest_26_nodes.png │ ├── G2P-4_AE_512_100_qm9_qm9_greatest_27_nodes.png │ ├── G2P-4_AE_512_100_qm9_qm9_greatest_28_nodes.png │ ├── G2P-4_AE_512_100_qm9_qm9_greatest_29_nodes.png │ ├── G2P-4_AE_512_100_qm9_qm9_greatest_2_nodes.png │ ├── G2P-4_AE_512_100_qm9_qm9_greatest_3_nodes.png │ ├── G2P-4_AE_512_100_qm9_qm9_greatest_4_nodes.png │ ├── G2P-4_AE_512_100_qm9_qm9_greatest_5_nodes.png │ ├── G2P-4_AE_512_100_qm9_qm9_greatest_6_nodes.png │ ├── G2P-4_AE_512_100_qm9_qm9_greatest_7_nodes.png │ ├── G2P-4_AE_512_100_qm9_qm9_greatest_8_nodes.png │ ├── G2P-4_AE_512_100_qm9_qm9_greatest_9_nodes.png │ ├── G2P-4_AE_512_100_qm9_qm9_least_0_nodes.png │ ├── G2P-4_AE_512_100_qm9_qm9_least_10_nodes.png │ ├── G2P-4_AE_512_100_qm9_qm9_least_11_nodes.png │ ├── G2P-4_AE_512_100_qm9_qm9_least_12_nodes.png │ ├── G2P-4_AE_512_100_qm9_qm9_least_13_nodes.png │ ├── G2P-4_AE_512_100_qm9_qm9_least_14_nodes.png │ ├── G2P-4_AE_512_100_qm9_qm9_least_15_nodes.png │ ├── G2P-4_AE_512_100_qm9_qm9_least_16_nodes.png │ ├── G2P-4_AE_512_100_qm9_qm9_least_17_nodes.png │ ├── G2P-4_AE_512_100_qm9_qm9_least_18_nodes.png │ ├── G2P-4_AE_512_100_qm9_qm9_least_19_nodes.png │ ├── G2P-4_AE_512_100_qm9_qm9_least_1_nodes.png │ ├── G2P-4_AE_512_100_qm9_qm9_least_20_nodes.png │ ├── G2P-4_AE_512_100_qm9_qm9_least_21_nodes.png │ ├── G2P-4_AE_512_100_qm9_qm9_least_22_nodes.png │ ├── G2P-4_AE_512_100_qm9_qm9_least_23_nodes.png │ ├── G2P-4_AE_512_100_qm9_qm9_least_24_nodes.png │ ├── G2P-4_AE_512_100_qm9_qm9_least_25_nodes.png │ ├── G2P-4_AE_512_100_qm9_qm9_least_26_nodes.png │ ├── G2P-4_AE_512_100_qm9_qm9_least_27_nodes.png │ ├── G2P-4_AE_512_100_qm9_qm9_least_28_nodes.png │ ├── G2P-4_AE_512_100_qm9_qm9_least_29_nodes.png │ ├── G2P-4_AE_512_100_qm9_qm9_least_2_nodes.png │ ├── G2P-4_AE_512_100_qm9_qm9_least_3_nodes.png │ ├── G2P-4_AE_512_100_qm9_qm9_least_4_nodes.png │ ├── G2P-4_AE_512_100_qm9_qm9_least_5_nodes.png │ ├── G2P-4_AE_512_100_qm9_qm9_least_6_nodes.png │ ├── G2P-4_AE_512_100_qm9_qm9_least_7_nodes.png │ ├── G2P-4_AE_512_100_qm9_qm9_least_8_nodes.png │ ├── G2P-4_AE_512_100_qm9_qm9_least_9_nodes.png │ ├── G2P-4_TPSA2_512_100_ZINC_re_ZINC_greatest_0_nodes.png │ ├── G2P-4_TPSA2_512_100_ZINC_re_ZINC_greatest_10_nodes.png │ ├── G2P-4_TPSA2_512_100_ZINC_re_ZINC_greatest_11_nodes.png │ ├── G2P-4_TPSA2_512_100_ZINC_re_ZINC_greatest_12_nodes.png │ ├── G2P-4_TPSA2_512_100_ZINC_re_ZINC_greatest_13_nodes.png │ ├── G2P-4_TPSA2_512_100_ZINC_re_ZINC_greatest_14_nodes.png │ ├── G2P-4_TPSA2_512_100_ZINC_re_ZINC_greatest_15_nodes.png │ ├── G2P-4_TPSA2_512_100_ZINC_re_ZINC_greatest_16_nodes.png │ ├── G2P-4_TPSA2_512_100_ZINC_re_ZINC_greatest_17_nodes.png │ ├── G2P-4_TPSA2_512_100_ZINC_re_ZINC_greatest_18_nodes.png │ ├── G2P-4_TPSA2_512_100_ZINC_re_ZINC_greatest_19_nodes.png │ ├── G2P-4_TPSA2_512_100_ZINC_re_ZINC_greatest_1_nodes.png │ ├── G2P-4_TPSA2_512_100_ZINC_re_ZINC_greatest_20_nodes.png │ ├── G2P-4_TPSA2_512_100_ZINC_re_ZINC_greatest_21_nodes.png │ ├── G2P-4_TPSA2_512_100_ZINC_re_ZINC_greatest_22_nodes.png │ ├── G2P-4_TPSA2_512_100_ZINC_re_ZINC_greatest_23_nodes.png │ ├── G2P-4_TPSA2_512_100_ZINC_re_ZINC_greatest_24_nodes.png │ ├── G2P-4_TPSA2_512_100_ZINC_re_ZINC_greatest_25.png │ ├── G2P-4_TPSA2_512_100_ZINC_re_ZINC_greatest_2_nodes.png │ ├── G2P-4_TPSA2_512_100_ZINC_re_ZINC_greatest_3_nodes.png │ ├── G2P-4_TPSA2_512_100_ZINC_re_ZINC_greatest_4_nodes.png │ ├── G2P-4_TPSA2_512_100_ZINC_re_ZINC_greatest_5_nodes.png │ ├── G2P-4_TPSA2_512_100_ZINC_re_ZINC_greatest_6_nodes.png │ ├── G2P-4_TPSA2_512_100_ZINC_re_ZINC_greatest_7_nodes.png │ ├── G2P-4_TPSA2_512_100_ZINC_re_ZINC_greatest_8_nodes.png │ ├── G2P-4_TPSA2_512_100_ZINC_re_ZINC_greatest_9_nodes.png │ ├── G2P-4_TPSA2_512_100_ZINC_re_ZINC_least_0_nodes.png │ ├── G2P-4_TPSA2_512_100_ZINC_re_ZINC_least_10_nodes.png │ ├── G2P-4_TPSA2_512_100_ZINC_re_ZINC_least_11_nodes.png │ ├── G2P-4_TPSA2_512_100_ZINC_re_ZINC_least_12_nodes.png │ ├── G2P-4_TPSA2_512_100_ZINC_re_ZINC_least_13_nodes.png │ ├── G2P-4_TPSA2_512_100_ZINC_re_ZINC_least_14_nodes.png │ ├── G2P-4_TPSA2_512_100_ZINC_re_ZINC_least_15_nodes.png │ ├── G2P-4_TPSA2_512_100_ZINC_re_ZINC_least_16_nodes.png │ ├── G2P-4_TPSA2_512_100_ZINC_re_ZINC_least_17_nodes.png │ ├── G2P-4_TPSA2_512_100_ZINC_re_ZINC_least_18_nodes.png │ ├── G2P-4_TPSA2_512_100_ZINC_re_ZINC_least_19_nodes.png │ ├── G2P-4_TPSA2_512_100_ZINC_re_ZINC_least_1_nodes.png │ ├── G2P-4_TPSA2_512_100_ZINC_re_ZINC_least_20_nodes.png │ ├── G2P-4_TPSA2_512_100_ZINC_re_ZINC_least_21_nodes.png │ ├── G2P-4_TPSA2_512_100_ZINC_re_ZINC_least_22_nodes.png │ ├── G2P-4_TPSA2_512_100_ZINC_re_ZINC_least_23_nodes.png │ ├── G2P-4_TPSA2_512_100_ZINC_re_ZINC_least_24_nodes.png │ ├── G2P-4_TPSA2_512_100_ZINC_re_ZINC_least_25.png │ ├── G2P-4_TPSA2_512_100_ZINC_re_ZINC_least_2_nodes.png │ ├── G2P-4_TPSA2_512_100_ZINC_re_ZINC_least_3_nodes.png │ ├── G2P-4_TPSA2_512_100_ZINC_re_ZINC_least_4_nodes.png │ ├── G2P-4_TPSA2_512_100_ZINC_re_ZINC_least_5_nodes.png │ ├── G2P-4_TPSA2_512_100_ZINC_re_ZINC_least_6_nodes.png │ ├── G2P-4_TPSA2_512_100_ZINC_re_ZINC_least_7_nodes.png │ ├── G2P-4_TPSA2_512_100_ZINC_re_ZINC_least_8_nodes.png │ ├── G2P-4_TPSA2_512_100_ZINC_re_ZINC_least_9_nodes.png │ ├── G2P-4_logP_512_100_ZINC_re_ZINC_greatest_0_nodes.png │ ├── G2P-4_logP_512_100_ZINC_re_ZINC_greatest_10_nodes.png │ ├── G2P-4_logP_512_100_ZINC_re_ZINC_greatest_11_nodes.png │ ├── G2P-4_logP_512_100_ZINC_re_ZINC_greatest_12_nodes.png │ ├── G2P-4_logP_512_100_ZINC_re_ZINC_greatest_13_nodes.png │ ├── G2P-4_logP_512_100_ZINC_re_ZINC_greatest_14_nodes.png │ ├── G2P-4_logP_512_100_ZINC_re_ZINC_greatest_15_nodes.png │ ├── G2P-4_logP_512_100_ZINC_re_ZINC_greatest_16_nodes.png │ ├── G2P-4_logP_512_100_ZINC_re_ZINC_greatest_17_nodes.png │ ├── G2P-4_logP_512_100_ZINC_re_ZINC_greatest_18_nodes.png │ ├── G2P-4_logP_512_100_ZINC_re_ZINC_greatest_19_nodes.png │ ├── G2P-4_logP_512_100_ZINC_re_ZINC_greatest_1_nodes.png │ ├── G2P-4_logP_512_100_ZINC_re_ZINC_greatest_20_nodes.png │ ├── G2P-4_logP_512_100_ZINC_re_ZINC_greatest_21_nodes.png │ ├── G2P-4_logP_512_100_ZINC_re_ZINC_greatest_22_nodes.png │ ├── G2P-4_logP_512_100_ZINC_re_ZINC_greatest_23_nodes.png │ ├── G2P-4_logP_512_100_ZINC_re_ZINC_greatest_24_nodes.png │ ├── G2P-4_logP_512_100_ZINC_re_ZINC_greatest_25.png │ ├── G2P-4_logP_512_100_ZINC_re_ZINC_greatest_2_nodes.png │ ├── G2P-4_logP_512_100_ZINC_re_ZINC_greatest_3_nodes.png │ ├── G2P-4_logP_512_100_ZINC_re_ZINC_greatest_4_nodes.png │ ├── G2P-4_logP_512_100_ZINC_re_ZINC_greatest_5_nodes.png │ ├── G2P-4_logP_512_100_ZINC_re_ZINC_greatest_6_nodes.png │ ├── G2P-4_logP_512_100_ZINC_re_ZINC_greatest_7_nodes.png │ ├── G2P-4_logP_512_100_ZINC_re_ZINC_greatest_8_nodes.png │ ├── G2P-4_logP_512_100_ZINC_re_ZINC_greatest_9_nodes.png │ ├── G2P-4_logP_512_100_ZINC_re_ZINC_least_0_nodes.png │ ├── G2P-4_logP_512_100_ZINC_re_ZINC_least_10_nodes.png │ ├── G2P-4_logP_512_100_ZINC_re_ZINC_least_11_nodes.png │ ├── G2P-4_logP_512_100_ZINC_re_ZINC_least_12_nodes.png │ ├── G2P-4_logP_512_100_ZINC_re_ZINC_least_13_nodes.png │ ├── G2P-4_logP_512_100_ZINC_re_ZINC_least_14_nodes.png │ ├── G2P-4_logP_512_100_ZINC_re_ZINC_least_15_nodes.png │ ├── G2P-4_logP_512_100_ZINC_re_ZINC_least_16_nodes.png │ ├── G2P-4_logP_512_100_ZINC_re_ZINC_least_17_nodes.png │ ├── G2P-4_logP_512_100_ZINC_re_ZINC_least_18_nodes.png │ ├── G2P-4_logP_512_100_ZINC_re_ZINC_least_19_nodes.png │ ├── G2P-4_logP_512_100_ZINC_re_ZINC_least_1_nodes.png │ ├── G2P-4_logP_512_100_ZINC_re_ZINC_least_20_nodes.png │ ├── G2P-4_logP_512_100_ZINC_re_ZINC_least_21_nodes.png │ ├── G2P-4_logP_512_100_ZINC_re_ZINC_least_22_nodes.png │ ├── G2P-4_logP_512_100_ZINC_re_ZINC_least_23_nodes.png │ ├── G2P-4_logP_512_100_ZINC_re_ZINC_least_24_nodes.png │ ├── G2P-4_logP_512_100_ZINC_re_ZINC_least_25.png │ ├── G2P-4_logP_512_100_ZINC_re_ZINC_least_2_nodes.png │ ├── G2P-4_logP_512_100_ZINC_re_ZINC_least_3_nodes.png │ ├── G2P-4_logP_512_100_ZINC_re_ZINC_least_4_nodes.png │ ├── G2P-4_logP_512_100_ZINC_re_ZINC_least_5_nodes.png │ ├── G2P-4_logP_512_100_ZINC_re_ZINC_least_6_nodes.png │ ├── G2P-4_logP_512_100_ZINC_re_ZINC_least_7_nodes.png │ ├── G2P-4_logP_512_100_ZINC_re_ZINC_least_8_nodes.png │ └── G2P-4_logP_512_100_ZINC_re_ZINC_least_9_nodes.png └── Figure5 - Supplementary │ ├── G2P-4_cep_512_100_CEP_CEP_dissimilar_0_nodes.png │ ├── G2P-4_cep_512_100_CEP_CEP_dissimilar_10_nodes.png │ ├── G2P-4_cep_512_100_CEP_CEP_dissimilar_11_nodes.png │ ├── G2P-4_cep_512_100_CEP_CEP_dissimilar_12_nodes.png │ ├── G2P-4_cep_512_100_CEP_CEP_dissimilar_13_nodes.png │ ├── G2P-4_cep_512_100_CEP_CEP_dissimilar_14_nodes.png │ ├── G2P-4_cep_512_100_CEP_CEP_dissimilar_15_nodes.png │ ├── G2P-4_cep_512_100_CEP_CEP_dissimilar_16_nodes.png │ ├── G2P-4_cep_512_100_CEP_CEP_dissimilar_17_nodes.png │ ├── G2P-4_cep_512_100_CEP_CEP_dissimilar_18_nodes.png │ ├── G2P-4_cep_512_100_CEP_CEP_dissimilar_19_nodes.png │ ├── G2P-4_cep_512_100_CEP_CEP_dissimilar_1_nodes.png │ ├── G2P-4_cep_512_100_CEP_CEP_dissimilar_20.png │ ├── G2P-4_cep_512_100_CEP_CEP_dissimilar_2_nodes.png │ ├── G2P-4_cep_512_100_CEP_CEP_dissimilar_3_nodes.png │ ├── G2P-4_cep_512_100_CEP_CEP_dissimilar_4_nodes.png │ ├── G2P-4_cep_512_100_CEP_CEP_dissimilar_5_nodes.png │ ├── G2P-4_cep_512_100_CEP_CEP_dissimilar_6_nodes.png │ ├── G2P-4_cep_512_100_CEP_CEP_dissimilar_7_nodes.png │ ├── G2P-4_cep_512_100_CEP_CEP_dissimilar_8_nodes.png │ ├── G2P-4_cep_512_100_CEP_CEP_dissimilar_9_nodes.png │ ├── G2P-4_cep_512_100_CEP_CEP_similar_0_nodes.png │ ├── G2P-4_cep_512_100_CEP_CEP_similar_10_nodes.png │ ├── G2P-4_cep_512_100_CEP_CEP_similar_11_nodes.png │ ├── G2P-4_cep_512_100_CEP_CEP_similar_12_nodes.png │ ├── G2P-4_cep_512_100_CEP_CEP_similar_13_nodes.png │ ├── G2P-4_cep_512_100_CEP_CEP_similar_14_nodes.png │ ├── G2P-4_cep_512_100_CEP_CEP_similar_15_nodes.png │ ├── G2P-4_cep_512_100_CEP_CEP_similar_16_nodes.png │ ├── G2P-4_cep_512_100_CEP_CEP_similar_17_nodes.png │ ├── G2P-4_cep_512_100_CEP_CEP_similar_18_nodes.png │ ├── G2P-4_cep_512_100_CEP_CEP_similar_19_nodes.png │ ├── G2P-4_cep_512_100_CEP_CEP_similar_1_nodes.png │ ├── G2P-4_cep_512_100_CEP_CEP_similar_20.png │ ├── G2P-4_cep_512_100_CEP_CEP_similar_2_nodes.png │ ├── G2P-4_cep_512_100_CEP_CEP_similar_3_nodes.png │ ├── G2P-4_cep_512_100_CEP_CEP_similar_4_nodes.png │ ├── G2P-4_cep_512_100_CEP_CEP_similar_5_nodes.png │ ├── G2P-4_cep_512_100_CEP_CEP_similar_6_nodes.png │ ├── G2P-4_cep_512_100_CEP_CEP_similar_7_nodes.png │ ├── G2P-4_cep_512_100_CEP_CEP_similar_8_nodes.png │ └── G2P-4_cep_512_100_CEP_CEP_similar_9_nodes.png ├── model ├── Graph2Property.py ├── __pycache__ │ ├── Graph2Graph.cpython-36.pyc │ ├── Graph2Graph_2.cpython-36.pyc │ ├── Graph2Property.cpython-36.pyc │ ├── Graph2Smiles.cpython-36.pyc │ ├── Latent2Property.cpython-36.pyc │ ├── Smiles2Property.cpython-36.pyc │ ├── Smiles2Smiles.cpython-36.pyc │ ├── blocks.cpython-36.pyc │ ├── blocks2.cpython-36.pyc │ ├── blocks3.cpython-36.pyc │ ├── blocks4.cpython-36.pyc │ └── losses.cpython-36.pyc └── blocks2.py ├── test.py ├── train.py └── utils ├── __pycache__ ├── smilesToGraph2.cpython-36.pyc ├── smilesToGraph_explicit.cpython-36.pyc └── utils.cpython-36.pyc ├── smilesToGraph.py ├── smilesToGraph_explicit.py └── utils.py /README.md: -------------------------------------------------------------------------------- 1 | # Molecular-GAT 2 | Application of Graph Attention Network for Learning Molecular 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| 5 | class Graph2Property(): 6 | def __init__(self, FLAGS): 7 | self.FLAGS = FLAGS 8 | self.batch_size = FLAGS.batch_size 9 | self.A = tf.placeholder("float64", shape = [self.batch_size, 50, 50]) 10 | self.X = tf.placeholder("float64", shape = [self.batch_size, 50, 58]) 11 | self.P = tf.placeholder("float64", shape = [self.batch_size]) 12 | self.skip_connection = True 13 | 14 | self.create_network() 15 | 16 | def create_network(self): 17 | self.A = tf.cast(self.A, tf.float64) 18 | self.X = tf.cast(self.X, tf.float64) 19 | self.P = tf.cast(self.P, tf.float64) 20 | self.Z = None 21 | self._X = None 22 | self._P = None 23 | self._A = None 24 | latent_dim = self.FLAGS.latent_dim 25 | 26 | if( self.FLAGS.model == 'GCN' ): 27 | self.Z, self._X= blocks2.encoder_gcn_deep(self.X, self.A, self.batch_size, latent_dim, self.skip_connection) 28 | elif( self.FLAGS.model == 'GAT' ): 29 | self.Z, self._X, self._A = blocks2.encoder_gat_deep(self.X, self.A, self.batch_size, latent_dim, self.skip_connection) 30 | self._P = blocks2.predictor_mlp(self.Z) 31 | self.loss = self.calLoss(self.P, self._P) 32 | 33 | self.lr = tf.Variable(0.0, trainable = False) 34 | self.opt = self.optimizer( self.lr, self.FLAGS.optimizer ) 35 | self.sess = tf.Session() 36 | init = tf.global_variables_initializer() 37 | self.sess = tf.Session() 38 | self.sess.run(init) 39 | self.saver = tf.train.Saver() 40 | tf.train.start_queue_runners(sess=self.sess) 41 | print ("Network Ready") 42 | 43 | def calLoss(self, P, _P): 44 | batch_size = int(P.get_shape()[0]) 45 | P = tf.reshape(P, [batch_size, -1]) 46 | P = tf.cast(P, tf.float64) 47 | _P = tf.reshape(_P, [batch_size, -1]) 48 | _P = tf.cast(_P, tf.float64) 49 | loss = tf.reduce_mean(tf.pow((P-_P),2)) 50 | 51 | return loss 52 | 53 | def optimizer(self, lr, opt_type): 54 | optimizer = None 55 | if( opt_type == 'Adam' ): 56 | optimizer = tf.train.AdamOptimizer(lr) 57 | elif( opt_type == 'RMSProp' ): 58 | optimizer = tf.train.RMSPropOptimizer(lr) 59 | elif( opt_type == 'SGD' ): 60 | optimizer = tf.train.GradientDescentOptimizer(lr) 61 | 62 | return optimizer.minimize(self.loss) 63 | 64 | def get_output(self): 65 | return self._P, self.loss 66 | 67 | def train(self, A, X, P): 68 | opt, loss = self.sess.run([self.opt, self.loss], feed_dict = {self.A : A, self.X : X, self.P : P}) 69 | return loss 70 | 71 | def test(self, A, X, P): 72 | _P, loss = self.sess.run([self._P, self.loss], feed_dict = {self.A : A, self.X : X, self.P : P}) 73 | return _P, loss 74 | 75 | def predict(self, A, X): 76 | _P = self.sess.run([self._P], feed_dict = {self.A : A, self.X : X}) 77 | return _P 78 | 79 | def get_nodes(self, A, X): 80 | return self.sess.run(self._X, feed_dict = {self.A : A, self.X : X}) 81 | 82 | def get_adjacency(self, A, X): 83 | return self.sess.run(self._A, feed_dict = {self.A : A, self.X : X}) 84 | 85 | def get_latent_vector(self, A, X): 86 | return self.sess.run(self.Z, feed_dict = {self.A : A, self.X : X}) 87 | 88 | def generate_molecule(self, Z): 89 | return self.sess.run(self._P, feed_dict={self.Z : Z}) 90 | 91 | def save(self, ckpt_path, global_step): 92 | self.saver.save(self.sess, ckpt_path, global_step=global_step) 93 | print("model saved to '%s'" % (ckpt_path)) 94 | 95 | def restore(self, ckpt_path): 96 | self.saver.restore(self.sess, ckpt_path) 97 | 98 | def assign_lr(self, learning_rate): 99 | self.sess.run(tf.assign(self.lr, learning_rate)) 100 | -------------------------------------------------------------------------------- /model/__pycache__/Graph2Graph.cpython-36.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/SeongokRyu/Molecular-GAT/ce94278a708db8c9f3b290288c6ba0202d548848/model/__pycache__/Graph2Graph.cpython-36.pyc -------------------------------------------------------------------------------- /model/__pycache__/Graph2Graph_2.cpython-36.pyc: 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/model/blocks2.py: -------------------------------------------------------------------------------- 1 | import tensorflow as tf 2 | 3 | def attentionMatrix(A, X, attn_weight): 4 | 5 | # A : [batch, N, N] 6 | # X : [batch, N, F'] 7 | # weight_attn : F' 8 | num_atoms = int(X.get_shape()[1]) 9 | hidden_dim = int(X.get_shape()[2]) 10 | 11 | _X1 = tf.einsum('ij,ajk->aik', attn_weight, tf.transpose(X, [0,2,1])) 12 | _X2 = tf.matmul(X, _X1) 13 | _A = tf.multiply(A, _X2) 14 | _A = tf.nn.tanh(_A) 15 | return _A 16 | 17 | def graphConv1D(X, out_dim): 18 | _X = tf.layers.dense(X, units = out_dim, use_bias=False) 19 | return _X 20 | 21 | def getSkipConnection(_X, X): 22 | if( int(_X.get_shape()[2]) != int(X.get_shape()[2]) ): 23 | out_dim = int(_X.get_shape()[2]) 24 | _X = tf.nn.relu(_X + graphConv1D(X, out_dim)) 25 | else: 26 | _X = tf.nn.relu(_X + X) 27 | 28 | return _X 29 | 30 | def graphConv(A, X, W, b, dim, skip_connection): 31 | numAtoms = int(A.get_shape()[1]) 32 | #dim = int(W.get_shape()[1]) 33 | b = tf.reshape(tf.tile(b, [numAtoms]), [numAtoms, dim]) 34 | _X = tf.einsum('ijk,kl->ijl', X, W) + b 35 | _X = tf.matmul(A, _X) 36 | if(skip_connection == True): 37 | _X = getSkipConnection(_X, X) 38 | else: 39 | _X = tf.nn.relu(_X) 40 | 41 | return _X 42 | 43 | def graphConvAttention(A, X, W, b, attn_weight, dim, skip_connection): 44 | numAtoms = int(A.get_shape()[1]) 45 | #dim = int(W.get_shape()[1]) 46 | b = tf.reshape(tf.tile(b, [numAtoms]), [numAtoms, dim]) 47 | _X = tf.einsum('ijk,kl->ijl', X, W) + b 48 | _A = attentionMatrix(A, _X, attn_weight) 49 | _X = tf.matmul(_A, _X) 50 | 51 | if(skip_connection == True): 52 | _X = getSkipConnection(_X, X) 53 | else: 54 | _X = tf.nn.relu(_X) 55 | 56 | return _X, _A 57 | 58 | def graphConvMulti(A, X, weight, bias, attn_weight, dim, skip_connection): 59 | numAtoms = int(A.get_shape()[1]) 60 | bias_head1 = tf.reshape( tf.tile( bias['enc_head1'], [numAtoms] ), [numAtoms, dim] ) 61 | bias_head2 = tf.reshape( tf.tile( bias['enc_head2'], [numAtoms] ), [numAtoms, dim] ) 62 | bias_head3 = tf.reshape( tf.tile( bias['enc_head3'], [numAtoms] ), [numAtoms, dim] ) 63 | bias_head4 = tf.reshape( tf.tile( bias['enc_head4'], [numAtoms] ), [numAtoms, dim] ) 64 | 65 | X_head1 = tf.einsum('ijk,kl->ijl', X, weight['enc_head1']) + bias_head1 66 | X_head2 = tf.einsum('ijk,kl->ijl', X, weight['enc_head2']) + bias_head2 67 | X_head3 = tf.einsum('ijk,kl->ijl', X, weight['enc_head3']) + bias_head3 68 | X_head4 = tf.einsum('ijk,kl->ijl', X, weight['enc_head4']) + bias_head4 69 | 70 | A_head1 = attentionMatrix(A, X_head1, attn_weight['enc_attn11']) 71 | A_head2 = attentionMatrix(A, X_head2, attn_weight['enc_attn21']) 72 | A_head3 = attentionMatrix(A, X_head3, attn_weight['enc_attn31']) 73 | A_head4 = attentionMatrix(A, X_head4, attn_weight['enc_attn41']) 74 | 75 | X_head1 = tf.nn.relu(tf.matmul(A_head1, X_head1)) 76 | X_head2 = tf.nn.relu(tf.matmul(A_head2, X_head2)) 77 | X_head3 = tf.nn.relu(tf.matmul(A_head3, X_head3)) 78 | X_head4 = tf.nn.relu(tf.matmul(A_head4, X_head4)) 79 | 80 | _X = tf.concat([X_head1, X_head2, X_head3, X_head4], 2) 81 | _A = tf.reduce_mean( [A_head1, A_head2, A_head3, A_head4], 0) 82 | 83 | return _X, _A 84 | 85 | def graphConvMulti_sum(A, X, weight, bias, attn_weight, dim, skip_connection): 86 | 87 | numAtoms = int(X.get_shape()[1]) 88 | bias_head1 = tf.reshape( tf.tile( bias['enc_head1'], [numAtoms] ), [numAtoms, dim] ) 89 | bias_head2 = tf.reshape( tf.tile( bias['enc_head2'], [numAtoms] ), [numAtoms, dim] ) 90 | bias_head3 = tf.reshape( tf.tile( bias['enc_head3'], [numAtoms] ), [numAtoms, dim] ) 91 | bias_head4 = tf.reshape( tf.tile( bias['enc_head4'], [numAtoms] ), [numAtoms, dim] ) 92 | 93 | X_head1 = tf.einsum('ijk,kl->ijl', X, weight['enc_head1']) + bias_head1 94 | X_head2 = tf.einsum('ijk,kl->ijl', X, weight['enc_head2']) + bias_head2 95 | X_head3 = tf.einsum('ijk,kl->ijl', X, weight['enc_head3']) + bias_head3 96 | X_head4 = tf.einsum('ijk,kl->ijl', X, weight['enc_head4']) + bias_head4 97 | 98 | A_head1 = attentionMatrix(A, X_head1, attn_weight['enc_attn11']) 99 | A_head2 = attentionMatrix(A, X_head2, attn_weight['enc_attn21']) 100 | A_head3 = attentionMatrix(A, X_head3, attn_weight['enc_attn31']) 101 | A_head4 = attentionMatrix(A, X_head4, attn_weight['enc_attn41']) 102 | 103 | X_head1 = tf.matmul(A_head1, X_head1) 104 | X_head2 = tf.matmul(A_head2, X_head2) 105 | X_head3 = tf.matmul(A_head3, X_head3) 106 | X_head4 = tf.matmul(A_head4, X_head4) 107 | 108 | _X = tf.nn.relu( tf.reduce_mean( [X_head1, X_head2, X_head3, X_head4], 0) ) 109 | _A = tf.reduce_mean( [A_head1, A_head2, A_head3, A_head4], 0) 110 | 111 | if(skip_connection == True): 112 | _X = getSkipConnection(_X, X) 113 | else: 114 | _X = tf.nn.relu(_X) 115 | 116 | return _X, _A 117 | 118 | def encoder_gat_deep(X, A, batch_size, latent_size, skip_connection): 119 | # X : Atomic Feature, A : Adjacency Matrix 120 | hidden_dim = [32, 32, 32, 16, 16, 16] 121 | #hidden_dim = [16, 16, 16, 16, 16, 16] 122 | numAtoms = int(X.get_shape()[1]) 123 | input_dim = int(X.get_shape()[2]) 124 | 125 | weight = { 126 | 'enc_f1': tf.get_variable("efw1", initializer=tf.contrib.layers.xavier_initializer(), shape=[numAtoms*hidden_dim[5], latent_size], dtype=tf.float64), 127 | } 128 | bias = { 129 | 'enc_f1': tf.get_variable("efb1", initializer=tf.contrib.layers.xavier_initializer(), shape=[latent_size], dtype=tf.float64), 130 | } 131 | 132 | weight_c1 = { 133 | 'enc_head1': tf.get_variable("ecw1h1", initializer=tf.contrib.layers.xavier_initializer(), shape=[input_dim, hidden_dim[0]], dtype=tf.float64), 134 | 'enc_head2': tf.get_variable("ecw1h2", initializer=tf.contrib.layers.xavier_initializer(), shape=[input_dim, hidden_dim[0]], dtype=tf.float64), 135 | 'enc_head3': tf.get_variable("ecw1h3", initializer=tf.contrib.layers.xavier_initializer(), shape=[input_dim, hidden_dim[0]], dtype=tf.float64), 136 | 'enc_head4': tf.get_variable("ecw1h4", initializer=tf.contrib.layers.xavier_initializer(), shape=[input_dim, hidden_dim[0]], dtype=tf.float64), 137 | } 138 | weight_c2 = { 139 | 'enc_head1': tf.get_variable("ecw2h1", initializer=tf.contrib.layers.xavier_initializer(), shape=[hidden_dim[0], hidden_dim[1]], dtype=tf.float64), 140 | 'enc_head2': tf.get_variable("ecw2h2", initializer=tf.contrib.layers.xavier_initializer(), shape=[hidden_dim[0], hidden_dim[1]], dtype=tf.float64), 141 | 'enc_head3': tf.get_variable("ecw2h3", initializer=tf.contrib.layers.xavier_initializer(), shape=[hidden_dim[0], hidden_dim[1]], dtype=tf.float64), 142 | 'enc_head4': tf.get_variable("ecw2h4", initializer=tf.contrib.layers.xavier_initializer(), shape=[hidden_dim[0], hidden_dim[1]], dtype=tf.float64), 143 | } 144 | weight_c3 = { 145 | 'enc_head1': tf.get_variable("ecw3h1", initializer=tf.contrib.layers.xavier_initializer(), shape=[hidden_dim[1], hidden_dim[2]], dtype=tf.float64), 146 | 'enc_head2': tf.get_variable("ecw3h2", initializer=tf.contrib.layers.xavier_initializer(), shape=[hidden_dim[1], hidden_dim[2]], dtype=tf.float64), 147 | 'enc_head3': tf.get_variable("ecw3h3", initializer=tf.contrib.layers.xavier_initializer(), shape=[hidden_dim[1], hidden_dim[2]], dtype=tf.float64), 148 | 'enc_head4': tf.get_variable("ecw3h4", initializer=tf.contrib.layers.xavier_initializer(), shape=[hidden_dim[1], hidden_dim[2]], dtype=tf.float64), 149 | } 150 | weight_c4 = { 151 | 'enc_head1': tf.get_variable("ecw4h1", initializer=tf.contrib.layers.xavier_initializer(), shape=[hidden_dim[2], hidden_dim[3]], dtype=tf.float64), 152 | 'enc_head2': tf.get_variable("ecw4h2", initializer=tf.contrib.layers.xavier_initializer(), shape=[hidden_dim[2], hidden_dim[3]], dtype=tf.float64), 153 | 'enc_head3': tf.get_variable("ecw4h3", initializer=tf.contrib.layers.xavier_initializer(), shape=[hidden_dim[2], hidden_dim[3]], dtype=tf.float64), 154 | 'enc_head4': tf.get_variable("ecw4h4", initializer=tf.contrib.layers.xavier_initializer(), shape=[hidden_dim[2], hidden_dim[3]], dtype=tf.float64), 155 | } 156 | weight_c5 = { 157 | 'enc_head1': tf.get_variable("ecw5h1", initializer=tf.contrib.layers.xavier_initializer(), shape=[hidden_dim[3], hidden_dim[4]], dtype=tf.float64), 158 | 'enc_head2': tf.get_variable("ecw5h2", initializer=tf.contrib.layers.xavier_initializer(), shape=[hidden_dim[3], hidden_dim[4]], dtype=tf.float64), 159 | 'enc_head3': tf.get_variable("ecw5h3", initializer=tf.contrib.layers.xavier_initializer(), shape=[hidden_dim[3], hidden_dim[4]], dtype=tf.float64), 160 | 'enc_head4': tf.get_variable("ecw5h4", initializer=tf.contrib.layers.xavier_initializer(), shape=[hidden_dim[3], hidden_dim[4]], dtype=tf.float64), 161 | } 162 | weight_c6 = { 163 | 'enc_head1': tf.get_variable("ecw6h1", initializer=tf.contrib.layers.xavier_initializer(), shape=[hidden_dim[4], hidden_dim[5]], dtype=tf.float64), 164 | 'enc_head2': tf.get_variable("ecw6h2", initializer=tf.contrib.layers.xavier_initializer(), shape=[hidden_dim[4], hidden_dim[5]], dtype=tf.float64), 165 | 'enc_head3': tf.get_variable("ecw6h3", initializer=tf.contrib.layers.xavier_initializer(), shape=[hidden_dim[4], hidden_dim[5]], dtype=tf.float64), 166 | 'enc_head4': tf.get_variable("ecw6h4", initializer=tf.contrib.layers.xavier_initializer(), shape=[hidden_dim[4], hidden_dim[5]], dtype=tf.float64), 167 | } 168 | bias_c1 = { 169 | 'enc_head1': tf.get_variable("ecb1h1", initializer=tf.contrib.layers.xavier_initializer(), shape=[hidden_dim[0]], dtype=tf.float64), 170 | 'enc_head2': tf.get_variable("ecb1h2", initializer=tf.contrib.layers.xavier_initializer(), shape=[hidden_dim[0]], dtype=tf.float64), 171 | 'enc_head3': tf.get_variable("ecb1h3", initializer=tf.contrib.layers.xavier_initializer(), shape=[hidden_dim[0]], dtype=tf.float64), 172 | 'enc_head4': tf.get_variable("ecb1h4", initializer=tf.contrib.layers.xavier_initializer(), shape=[hidden_dim[0]], dtype=tf.float64), 173 | } 174 | bias_c2 = { 175 | 'enc_head1': tf.get_variable("ecb2h1", initializer=tf.contrib.layers.xavier_initializer(), shape=[hidden_dim[1]], dtype=tf.float64), 176 | 'enc_head2': tf.get_variable("ecb2h2", initializer=tf.contrib.layers.xavier_initializer(), shape=[hidden_dim[1]], dtype=tf.float64), 177 | 'enc_head3': tf.get_variable("ecb2h3", initializer=tf.contrib.layers.xavier_initializer(), shape=[hidden_dim[1]], dtype=tf.float64), 178 | 'enc_head4': tf.get_variable("ecb2h4", initializer=tf.contrib.layers.xavier_initializer(), shape=[hidden_dim[1]], dtype=tf.float64), 179 | } 180 | bias_c3 = { 181 | 'enc_head1': tf.get_variable("ecb3h1", initializer=tf.contrib.layers.xavier_initializer(), shape=[hidden_dim[2]], dtype=tf.float64), 182 | 'enc_head2': tf.get_variable("ecb3h2", initializer=tf.contrib.layers.xavier_initializer(), shape=[hidden_dim[2]], dtype=tf.float64), 183 | 'enc_head3': tf.get_variable("ecb3h3", initializer=tf.contrib.layers.xavier_initializer(), shape=[hidden_dim[2]], dtype=tf.float64), 184 | 'enc_head4': tf.get_variable("ecb3h4", initializer=tf.contrib.layers.xavier_initializer(), shape=[hidden_dim[2]], dtype=tf.float64), 185 | } 186 | bias_c4 = { 187 | 'enc_head1': tf.get_variable("ecb4h1", initializer=tf.contrib.layers.xavier_initializer(), shape=[hidden_dim[3]], dtype=tf.float64), 188 | 'enc_head2': tf.get_variable("ecb4h2", initializer=tf.contrib.layers.xavier_initializer(), shape=[hidden_dim[3]], dtype=tf.float64), 189 | 'enc_head3': tf.get_variable("ecb4h3", initializer=tf.contrib.layers.xavier_initializer(), shape=[hidden_dim[3]], dtype=tf.float64), 190 | 'enc_head4': tf.get_variable("ecb4h4", initializer=tf.contrib.layers.xavier_initializer(), shape=[hidden_dim[3]], dtype=tf.float64), 191 | } 192 | bias_c5 = { 193 | 'enc_head1': tf.get_variable("ecb5h1", initializer=tf.contrib.layers.xavier_initializer(), shape=[hidden_dim[4]], dtype=tf.float64), 194 | 'enc_head2': tf.get_variable("ecb5h2", initializer=tf.contrib.layers.xavier_initializer(), shape=[hidden_dim[4]], dtype=tf.float64), 195 | 'enc_head3': tf.get_variable("ecb5h3", initializer=tf.contrib.layers.xavier_initializer(), shape=[hidden_dim[4]], dtype=tf.float64), 196 | 'enc_head4': tf.get_variable("ecb5h4", initializer=tf.contrib.layers.xavier_initializer(), shape=[hidden_dim[4]], dtype=tf.float64), 197 | } 198 | bias_c6 = { 199 | 'enc_head1': tf.get_variable("ecb6h1", initializer=tf.contrib.layers.xavier_initializer(), shape=[hidden_dim[5]], dtype=tf.float64), 200 | 'enc_head2': tf.get_variable("ecb6h2", initializer=tf.contrib.layers.xavier_initializer(), shape=[hidden_dim[5]], dtype=tf.float64), 201 | 'enc_head3': tf.get_variable("ecb6h3", initializer=tf.contrib.layers.xavier_initializer(), shape=[hidden_dim[5]], dtype=tf.float64), 202 | 'enc_head4': tf.get_variable("ecb6h4", initializer=tf.contrib.layers.xavier_initializer(), shape=[hidden_dim[5]], dtype=tf.float64), 203 | } 204 | atten_weight_c1 = { 205 | 'enc_attn11' : tf.get_variable("eattn1h11", initializer = tf.contrib.layers.xavier_initializer(), shape = [hidden_dim[0], hidden_dim[0]], dtype= tf.float64), 206 | 'enc_attn21' : tf.get_variable("eattn1h21", initializer = tf.contrib.layers.xavier_initializer(), shape = [hidden_dim[0], hidden_dim[0]], dtype= tf.float64), 207 | 'enc_attn31' : tf.get_variable("eattn1h31", initializer = tf.contrib.layers.xavier_initializer(), shape = [hidden_dim[0], hidden_dim[0]], dtype= tf.float64), 208 | 'enc_attn41' : tf.get_variable("eattn1h41", initializer = tf.contrib.layers.xavier_initializer(), shape = [hidden_dim[0], hidden_dim[0]], dtype= tf.float64), 209 | } 210 | atten_weight_c2 = { 211 | 'enc_attn11' : tf.get_variable("eattn2h11", initializer = tf.contrib.layers.xavier_initializer(), shape = [hidden_dim[1], hidden_dim[1]], dtype= tf.float64), 212 | 'enc_attn21' : tf.get_variable("eattn2h21", initializer = tf.contrib.layers.xavier_initializer(), shape = [hidden_dim[1], hidden_dim[1]], dtype= tf.float64), 213 | 'enc_attn31' : tf.get_variable("eattn2h31", initializer = tf.contrib.layers.xavier_initializer(), shape = [hidden_dim[1], hidden_dim[1]], dtype= tf.float64), 214 | 'enc_attn41' : tf.get_variable("eattn2h41", initializer = tf.contrib.layers.xavier_initializer(), shape = [hidden_dim[1], hidden_dim[1]], dtype= tf.float64), 215 | } 216 | atten_weight_c3 = { 217 | 'enc_attn11' : tf.get_variable("eattn3h11", initializer = tf.contrib.layers.xavier_initializer(), shape = [hidden_dim[2], hidden_dim[2]], dtype= tf.float64), 218 | 'enc_attn21' : tf.get_variable("eattn3h21", initializer = tf.contrib.layers.xavier_initializer(), shape = [hidden_dim[2], hidden_dim[2]], dtype= tf.float64), 219 | 'enc_attn31' : tf.get_variable("eattn3h31", initializer = tf.contrib.layers.xavier_initializer(), shape = [hidden_dim[2], hidden_dim[2]], dtype= tf.float64), 220 | 'enc_attn41' : tf.get_variable("eattn3h41", initializer = tf.contrib.layers.xavier_initializer(), shape = [hidden_dim[2], hidden_dim[2]], dtype= tf.float64), 221 | } 222 | atten_weight_c4 = { 223 | 'enc_attn11' : tf.get_variable("eattn4h11", initializer = tf.contrib.layers.xavier_initializer(), shape = [hidden_dim[3], hidden_dim[3]], dtype= tf.float64), 224 | 'enc_attn21' : tf.get_variable("eattn4h21", initializer = tf.contrib.layers.xavier_initializer(), shape = [hidden_dim[3], hidden_dim[3]], dtype= tf.float64), 225 | 'enc_attn31' : tf.get_variable("eattn4h31", initializer = tf.contrib.layers.xavier_initializer(), shape = [hidden_dim[3], hidden_dim[3]], dtype= tf.float64), 226 | 'enc_attn41' : tf.get_variable("eattn4h41", initializer = tf.contrib.layers.xavier_initializer(), shape = [hidden_dim[3], hidden_dim[3]], dtype= tf.float64), 227 | } 228 | atten_weight_c5 = { 229 | 'enc_attn11' : tf.get_variable("eattn5h11", initializer = tf.contrib.layers.xavier_initializer(), shape = [hidden_dim[4], hidden_dim[4]], dtype= tf.float64), 230 | 'enc_attn21' : tf.get_variable("eattn5h21", initializer = tf.contrib.layers.xavier_initializer(), shape = [hidden_dim[4], hidden_dim[4]], dtype= tf.float64), 231 | 'enc_attn31' : tf.get_variable("eattn5h31", initializer = tf.contrib.layers.xavier_initializer(), shape = [hidden_dim[4], hidden_dim[4]], dtype= tf.float64), 232 | 'enc_attn41' : tf.get_variable("eattn5h41", initializer = tf.contrib.layers.xavier_initializer(), shape = [hidden_dim[4], hidden_dim[4]], dtype= tf.float64), 233 | } 234 | atten_weight_c6 = { 235 | 'enc_attn11' : tf.get_variable("eattn6h11", initializer = tf.contrib.layers.xavier_initializer(), shape = [hidden_dim[5], hidden_dim[5]], dtype= tf.float64), 236 | 'enc_attn21' : tf.get_variable("eattn6h21", initializer = tf.contrib.layers.xavier_initializer(), shape = [hidden_dim[5], hidden_dim[5]], dtype= tf.float64), 237 | 'enc_attn31' : tf.get_variable("eattn6h31", initializer = tf.contrib.layers.xavier_initializer(), shape = [hidden_dim[5], hidden_dim[5]], dtype= tf.float64), 238 | 'enc_attn41' : tf.get_variable("eattn6h41", initializer = tf.contrib.layers.xavier_initializer(), shape = [hidden_dim[5], hidden_dim[5]], dtype= tf.float64), 239 | } 240 | 241 | _X1, _A1 = graphConvMulti_sum( A, X, weight_c1, bias_c1, atten_weight_c1, hidden_dim[0], False ) 242 | _X2, _A = graphConvMulti_sum( A, _X1, weight_c2, bias_c2, atten_weight_c2, hidden_dim[1], skip_connection ) 243 | _X3, _A = graphConvMulti_sum( A, _X2, weight_c3, bias_c3, atten_weight_c3, hidden_dim[2], skip_connection ) 244 | _X4, _A = graphConvMulti_sum( A, _X3, weight_c4, bias_c4, atten_weight_c4, hidden_dim[3], skip_connection ) 245 | _X5, _A = graphConvMulti_sum( A, _X4, weight_c5, bias_c5, atten_weight_c5, hidden_dim[4], skip_connection ) 246 | _X6, _A = graphConvMulti_sum( A, _X5, weight_c6, bias_c6, atten_weight_c6, hidden_dim[5], skip_connection ) 247 | _Z = tf.reshape(_X6, [batch_size, -1]) 248 | latent = tf.nn.sigmoid(tf.nn.xw_plus_b(_Z, weight['enc_f1'], bias['enc_f1'])) 249 | 250 | return latent, _X6, _A1 251 | 252 | def encoder_gcn_deep(X, A, batch_size, latent_size, skip_connection): 253 | # X : Atomic Feature, A : Adjacency Matrix 254 | hidden_dim = [32, 32, 32, 16, 16, 16] 255 | numAtoms = int(X.get_shape()[1]) 256 | input_dim = int(X.get_shape()[2]) 257 | 258 | weight = { 259 | 'enc_f1': tf.get_variable("efw1", initializer=tf.contrib.layers.xavier_initializer(), shape=[numAtoms*hidden_dim[5], latent_size], dtype=tf.float64), 260 | 'enc_c1': tf.get_variable("ecw1", initializer=tf.contrib.layers.xavier_initializer(), shape=[input_dim, hidden_dim[0]], dtype=tf.float64), 261 | 'enc_c2': tf.get_variable("ecw2", initializer=tf.contrib.layers.xavier_initializer(), shape=[hidden_dim[0], hidden_dim[1]], dtype=tf.float64), 262 | 'enc_c3': tf.get_variable("ecw3", initializer=tf.contrib.layers.xavier_initializer(), shape=[hidden_dim[1], hidden_dim[2]], dtype=tf.float64), 263 | 'enc_c4': tf.get_variable("ecw4", initializer=tf.contrib.layers.xavier_initializer(), shape=[hidden_dim[2], hidden_dim[3]], dtype=tf.float64), 264 | 'enc_c5': tf.get_variable("ecw5", initializer=tf.contrib.layers.xavier_initializer(), shape=[hidden_dim[3], hidden_dim[4]], dtype=tf.float64), 265 | 'enc_c6': tf.get_variable("ecw6", initializer=tf.contrib.layers.xavier_initializer(), shape=[hidden_dim[4], hidden_dim[5]], dtype=tf.float64), 266 | } 267 | bias = { 268 | 'enc_f1': tf.get_variable("efb1", initializer=tf.contrib.layers.xavier_initializer(), shape=[latent_size], dtype=tf.float64), 269 | 'enc_c1': tf.get_variable("ecb1", initializer=tf.contrib.layers.xavier_initializer(), shape=[hidden_dim[0]], dtype=tf.float64), 270 | 'enc_c2': tf.get_variable("ecb2", initializer=tf.contrib.layers.xavier_initializer(), shape=[hidden_dim[1]], dtype=tf.float64), 271 | 'enc_c3': tf.get_variable("ecb3", initializer=tf.contrib.layers.xavier_initializer(), shape=[hidden_dim[2]], dtype=tf.float64), 272 | 'enc_c4': tf.get_variable("ecb4", initializer=tf.contrib.layers.xavier_initializer(), shape=[hidden_dim[3]], dtype=tf.float64), 273 | 'enc_c5': tf.get_variable("ecb5", initializer=tf.contrib.layers.xavier_initializer(), shape=[hidden_dim[4]], dtype=tf.float64), 274 | 'enc_c6': tf.get_variable("ecb6", initializer=tf.contrib.layers.xavier_initializer(), shape=[hidden_dim[5]], dtype=tf.float64), 275 | } 276 | 277 | _X1 = graphConv( A, X, weight['enc_c1'], bias['enc_c1'], hidden_dim[0], False ) 278 | _X2 = graphConv( A, _X1, weight['enc_c2'], bias['enc_c2'], hidden_dim[1], skip_connection ) 279 | _X3 = graphConv( A, _X2, weight['enc_c3'], bias['enc_c3'], hidden_dim[2], skip_connection ) 280 | _X4 = graphConv( A, _X3, weight['enc_c4'], bias['enc_c4'], hidden_dim[3], skip_connection ) 281 | _X5 = graphConv( A, _X4, weight['enc_c5'], bias['enc_c5'], hidden_dim[4], skip_connection ) 282 | _X6 = graphConv( A, _X5, weight['enc_c6'], bias['enc_c6'], hidden_dim[5], skip_connection ) 283 | _Z = tf.reshape(_X6, [batch_size, -1]) 284 | latent = tf.nn.sigmoid(tf.nn.xw_plus_b(_Z, weight['enc_f1'], bias['enc_f1'])) 285 | 286 | return latent, _X6 287 | 288 | def predictor_mlp(Z): 289 | 290 | Z = tf.cast(Z, tf.float64) 291 | latent_size = int(Z.get_shape()[1]) 292 | hidden_dim = [latent_size, latent_size, 1] 293 | weight = { 294 | 'mlp_f1': tf.get_variable("fw1", initializer=tf.contrib.layers.xavier_initializer(), shape=[latent_size, hidden_dim[0]], dtype=tf.float64), 295 | 'mlp_f2': tf.get_variable("fw2", initializer=tf.contrib.layers.xavier_initializer(), shape=[hidden_dim[0], hidden_dim[1]], dtype=tf.float64), 296 | 'mlp_f3': tf.get_variable("fw3", initializer=tf.contrib.layers.xavier_initializer(), shape=[hidden_dim[1], hidden_dim[2]], dtype=tf.float64), 297 | } 298 | bias = { 299 | 'mlp_f1': tf.get_variable("fb1", initializer=tf.contrib.layers.xavier_initializer(), shape=[hidden_dim[0]], dtype=tf.float64), 300 | 'mlp_f2': tf.get_variable("fb2", initializer=tf.contrib.layers.xavier_initializer(), shape=[hidden_dim[1]], dtype=tf.float64), 301 | 'mlp_f3': tf.get_variable("fb3", initializer=tf.contrib.layers.xavier_initializer(), shape=[hidden_dim[2]], dtype=tf.float64), 302 | } 303 | _Y = tf.nn.relu(tf.nn.xw_plus_b(Z, weight['mlp_f1'], bias['mlp_f1'])) 304 | _Y = tf.nn.tanh(tf.nn.xw_plus_b(_Y, weight['mlp_f2'], bias['mlp_f2'])) 305 | _Y = tf.nn.xw_plus_b(_Y, weight['mlp_f3'], bias['mlp_f3']) 306 | 307 | return _Y 308 | -------------------------------------------------------------------------------- /test.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | import os 3 | import time 4 | import sys 5 | sys.path.insert(0, './utils') 6 | sys.path.insert(0, './model') 7 | from Graph2Property import Graph2Property 8 | from utils import * 9 | import tensorflow as tf 10 | from rdkit.Chem import Draw 11 | import smilesToGraph2 12 | import smilesToGraph_explicit 13 | np.set_printoptions(precision=3) 14 | 15 | def loadInputs(FLAGS, idx, modelName, unitLen): 16 | if(FLAGS.validation_database == 'QM9'): 17 | adj1 = np.load('./Data/'+FLAGS.validation_database+'/adj_explicit/'+str(idx)+'.npy') 18 | features = np.load('./Data/'+FLAGS.validation_database+'/features_explicit/'+str(idx)+'.npy') 19 | else: 20 | adj1 = np.load('./Data/'+FLAGS.validation_database+'/adj/'+str(idx)+'.npy') 21 | features = np.load('./Data/'+FLAGS.validation_database+'/features/'+str(idx)+'.npy') 22 | retInput = (adj1, features) 23 | retOutput = (np.load('./Data/'+FLAGS.validation_database+'/'+FLAGS.output+'.npy')[idx*unitLen:(idx+1)*unitLen]) 24 | 25 | return retInput, retOutput 26 | 27 | def predict(model, FLAGS, modelName): 28 | batch_size = FLAGS.batch_size 29 | total_st = time.time() 30 | model.restore("./save/"+modelName+".ckpt-"+str(FLAGS.num_model)) 31 | 32 | smiles_f = open(FLAGS.validation_database+'.txt', 'r') 33 | smiles_list = smiles_f.readlines() 34 | num_valid = len(smiles_list) 35 | #A, X = smilesToGraph_explicit.convertToGraph(smiles_list, 1) 36 | A, X = smilesToGraph2.convertToGraph(smiles_list, 1) 37 | A = list(A) 38 | X = list(X) 39 | 40 | A_zero = np.zeros((50,50)) 41 | X_zero = np.zeros((50,58)) 42 | 43 | batch_size = FLAGS.batch_size 44 | emptyNum = num_valid%batch_size 45 | print ("Start Feature Extraction XD") 46 | for i in range(0,batch_size-emptyNum): 47 | A.append(A_zero) 48 | X.append(X_zero) 49 | 50 | A_batch = np.asarray(A) 51 | X_batch = np.asarray(X) 52 | num_batch = int(A_batch.shape[0]/batch_size) 53 | 54 | prediction = [] 55 | for i in range(num_batch): 56 | idx = np.arange(batch_size) + i*batch_size 57 | _P = model.predict(A_batch[idx], X_batch[idx]) 58 | print (_P[0][:num_valid]) 59 | prediction.append(_P) 60 | np.save('./prediction/'+modelName+'_'+FLAGS.validation_database+'.npy', prediction) 61 | 62 | total_et = time.time() 63 | print ("Finish feature extraction! Total required time for extraction : ", (total_et-total_st)) 64 | 65 | def getLatent_DB(model, FLAGS, modelName): 66 | ### Save Node vectors and Latent vectors ### 67 | batch_size = FLAGS.batch_size 68 | total_st = time.time() 69 | unitLen = FLAGS.unitLen 70 | print ("Start Training XD") 71 | 72 | model.restore("./save/"+modelName+".ckpt-"+str(FLAGS.num_model)) 73 | 74 | total_st = time.time() 75 | if( (FLAGS.model) == 'GCN') ): 76 | for i in range(FLAGS.start_DB, FLAGS.start_DB + FLAGS.num_DB): 77 | _graph, _property = loadInputs(FLAGS, i, modelName, FLAGS.unitLen) 78 | num_batches = int(_graph[0].shape[0]/batch_size) 79 | 80 | st = time.time() 81 | latent = [] 82 | adjacency = [] 83 | nodes = [] 84 | for _iter in range(num_batches): 85 | A_batch = _graph[0][_iter*FLAGS.batch_size:(_iter+1)*FLAGS.batch_size] 86 | X_batch = _graph[1][_iter*FLAGS.batch_size:(_iter+1)*FLAGS.batch_size] 87 | Z = model.get_latent_vector(A_batch, X_batch) 88 | X = model.get_nodes(A_batch, X_batch) 89 | for z in Z: 90 | latent.append(z) 91 | for x in X: 92 | nodes.append(x) 93 | latent = np.asarray(latent) 94 | np.save('./latent/'+modelName+'_'+FLAGS.validation_database+'_'+str(i)+'.npy', latent) 95 | np.save('./nodes/'+modelName+'_'+FLAGS.validation_database+'_'+str(i)+'.npy', nodes) 96 | 97 | 98 | elif( (FLAGS.model == 'GAT') ): 99 | for i in range(FLAGS.start_DB, FLAGS.start_DB + FLAGS.num_DB): 100 | _graph, _property = loadInputs(FLAGS, i, modelName, FLAGS.unitLen) 101 | num_batches = int(_graph[0].shape[0]/batch_size) 102 | 103 | st = time.time() 104 | latent = [] 105 | adjacency = [] 106 | nodes = [] 107 | for _iter in range(num_batches): 108 | A_batch = _graph[0][_iter*FLAGS.batch_size:(_iter+1)*FLAGS.batch_size] 109 | X_batch = _graph[1][_iter*FLAGS.batch_size:(_iter+1)*FLAGS.batch_size] 110 | Z = model.get_latent_vector(A_batch, X_batch) 111 | _A = model.get_adjacency(A_batch, X_batch) 112 | X = model.get_nodes(A_batch, X_batch) 113 | for z in Z: 114 | latent.append(z) 115 | for _a in _A: 116 | adjacency.append(_a) 117 | for x in X: 118 | nodes.append(x) 119 | latent = np.asarray(latent) 120 | adjacency = np.asarray(adjacency) 121 | np.save('./latent/'+modelName+'_'+FLAGS.validation_database+'_'+str(i)+'.npy', latent) 122 | np.save('./adjacency/'+modelName+'_'+FLAGS.validation_database+'_'+str(i)+'.npy', adjacency) 123 | np.save('./nodes/'+modelName+'_'+FLAGS.validation_database+'_'+str(i)+'.npy', nodes) 124 | 125 | et = time.time() 126 | print ("Time for feature extraction of", i, "th batch : ", et-st) 127 | st = time.time() 128 | 129 | total_et = time.time() 130 | print ("Finish feature extraction! Total required time for extraction : ", (total_et-total_st)) 131 | 132 | def getLatent(model, FLAGS, modelName): 133 | ### Save Node vectors and Latent vectors ### 134 | batch_size = FLAGS.batch_size 135 | total_st = time.time() 136 | model.restore("./save/"+modelName+".ckpt-450000") 137 | 138 | S = np.load("./Data/"+FLAGS.validation_database+'/smiles.txt') 139 | num_valid = S.shape[0] 140 | 141 | S = list(S) 142 | A = list(A) 143 | X = list(X) 144 | 145 | S_zero = np.zeros(120) 146 | A_zero = np.zeros((50,50)) 147 | X_zero = np.zeros((50,28)) 148 | 149 | batch_size = FLAGS.batch_size 150 | emptyNum = len(S)%batch_size 151 | print ("Start Feature Extraction XD") 152 | for i in range(0,batch_size-emptyNum): 153 | A.append(A_zero) 154 | X.append(X_zero) 155 | S.append(S_zero) 156 | 157 | A_batch = np.asarray(A) 158 | X_batch = np.asarray(X) 159 | S_batch = np.asarray(S) 160 | num_batch = int(S_batch.shape[0]/batch_size) 161 | 162 | latent = [] 163 | adjacency = [] 164 | for i in range(num_batch): 165 | idx = np.arange(batch_size) + i*batch_size 166 | Z = model.get_latent_vector(A_batch[idx], X_batch[idx]) 167 | _A = model.get_adjacency(A_batch[idx], X_batch[idx]) 168 | for z in Z: 169 | latent.append(z) 170 | for _a in _A: 171 | adjacency.append(_a) 172 | latent = np.asarray(latent) 173 | adjacency = np.asarray(adjacency) 174 | np.save('./latent/'+modelName+'_'+FLAGS.validation_database+'.npy', latent) 175 | np.save('./adjacency/'+modelName+'_'+FLAGS.validation_database+'.npy', adjacency) 176 | 177 | total_et = time.time() 178 | print ("Finish feature extraction! Total required time for extraction : ", (total_et-total_st)) 179 | 180 | def test(model, FLAGS, modelName): 181 | ### For test property ### 182 | batch_size = FLAGS.batch_size 183 | total_st = time.time() 184 | idx_start = FLAGS.start_DB 185 | _dim = FLAGS.num_DB 186 | unitLen = FLAGS.unitLen 187 | print ("Start Training XD") 188 | 189 | P_batch_total = [] 190 | P_pred_total = [] 191 | model.restore("./save/"+modelName+".ckpt-"+str(FLAGS.num_model)) 192 | 193 | for i in range(FLAGS.start_DB,FLAGS.start_DB+_dim): 194 | _graph, _property = loadInputs(FLAGS, i, modelName, unitLen) 195 | num_batches = int(_graph[0].shape[0]/batch_size) 196 | 197 | st = time.time() 198 | for _iter in range(num_batches): 199 | A_batch = _graph[0][_iter*FLAGS.batch_size:(_iter+1)*FLAGS.batch_size] 200 | X_batch = _graph[1][_iter*FLAGS.batch_size:(_iter+1)*FLAGS.batch_size] 201 | P_batch = _property[_iter*FLAGS.batch_size:(_iter+1)*FLAGS.batch_size] 202 | 203 | if( A_batch.shape[0] == FLAGS.batch_size): 204 | # Test accuracy 205 | Y, cost = model.test(A_batch, X_batch, P_batch) 206 | P_pred_total.append(Y.flatten()) 207 | P_batch_total.append((P_batch)) 208 | et = time.time() 209 | print ("time : ", et-st) 210 | 211 | st = time.time() 212 | 213 | P_batch_total = np.asarray(P_batch_total).flatten() 214 | P_pred_total = np.asarray(P_pred_total).flatten() 215 | mae_total = np.mean(np.abs(P_batch_total - P_pred_total)) 216 | print ("MAE :", mae_total) 217 | 218 | total_et = time.time() 219 | print ("Finish property validation! Total required time for validation :", (total_et-total_st)) 220 | 221 | flags = tf.app.flags 222 | FLAGS = flags.FLAGS 223 | flags.DEFINE_string('model', 'GAT', 'Options : GAT, GCN') 224 | flags.DEFINE_string('output', 'AE', 'Options : logP, TPSA, ...') 225 | flags.DEFINE_string('loss_type', 'MSE', 'Options : MSE') ### Using MSE or Hinge for predictor 226 | flags.DEFINE_string('database', 'qm9', 'Options : ZINC, QM9, CEP') 227 | flags.DEFINE_string('validation_database', 'AE_lowest', 'Options : ZINC, QM9, CEP') ### Using MSE or Hinge for predictor 228 | flags.DEFINE_string('optimizer', 'Adam', 'Options : ') ### Using MSE or Hinge for predictor 229 | flags.DEFINE_integer('latent_dim', 512, 'Dimension of a latent vector for autoencoder') 230 | flags.DEFINE_integer('epoch_size', 100, 'Epoch size') 231 | flags.DEFINE_integer('batch_size', 100, 'Batch size') 232 | flags.DEFINE_integer('save_every', 1000, 'Save every') 233 | flags.DEFINE_float('learning_rate', 0.0001, 'Batch size') 234 | flags.DEFINE_float('decay_rate', 0.95, 'Batch size') 235 | flags.DEFINE_integer('num_DB', 1, '') 236 | flags.DEFINE_integer('start_DB', 0, '') 237 | flags.DEFINE_integer('unitLen', 10000, '') 238 | flags.DEFINE_integer('num_model', 120000, '') 239 | modelName = FLAGS.model + '_' + FLAGS.output + '_' + str(FLAGS.latent_dim) + '_' + str(FLAGS.batch_size) + '_' + FLAGS.database 240 | 241 | print ("Summary of this training & testing") 242 | print ("Model name is", modelName) 243 | print ("A Latent vector dimension is", str(FLAGS.latent_dim)) 244 | print ("A learning rate is", str(FLAGS.learning_rate), "with a decay rate", str(FLAGS.decay_rate)) 245 | print ("Using", FLAGS.loss_type, "for loss function in an optimization") 246 | 247 | model = Graph2Property(FLAGS) 248 | predict(model, FLAGS, modelName) 249 | -------------------------------------------------------------------------------- /train.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | import os 3 | import time 4 | import sys 5 | sys.path.insert(0, './utils') 6 | sys.path.insert(0, './model') 7 | from Graph2Property import Graph2Property 8 | from utils import * 9 | import tensorflow as tf 10 | np.set_printoptions(precision=3) 11 | 12 | def loadInputs(FLAGS, idx, modelName, unitLen): 13 | if(FLAGS.validation_database == 'QM9'): 14 | adj1 = np.load('./Data/'+FLAGS.validation_database+'/adj_explicit/'+str(idx)+'.npy') 15 | features = np.load('./Data/'+FLAGS.validation_database+'/features_explicit/'+str(idx)+'.npy') 16 | else: 17 | adj1 = np.load('./Data/'+FLAGS.validation_database+'/adj/'+str(idx)+'.npy') 18 | features = np.load('./Data/'+FLAGS.validation_database+'/features/'+str(idx)+'.npy') 19 | retInput = (adj1, features) 20 | retOutput = (np.load('./Data/'+FLAGS.validation_database+'/'+FLAGS.output+'.npy')[idx*unitLen:(idx+1)*unitLen]) 21 | 22 | return retInput, retOutput 23 | 24 | def training(model, FLAGS, modelName): 25 | num_epochs = FLAGS.epoch_size 26 | batch_size = FLAGS.batch_size 27 | decay_rate = FLAGS.decay_rate 28 | save_every = FLAGS.save_every 29 | learning_rate = FLAGS.learning_rate 30 | num_DB = FLAGS.num_DB 31 | unitLen = FLAGS.unitLen 32 | total_iter = 0 33 | total_st = time.time() 34 | print ("Start Training XD") 35 | for epoch in range(num_epochs): 36 | # Learning rate scheduling 37 | model.assign_lr(learning_rate * (decay_rate ** epoch)) 38 | 39 | for i in range(0,num_DB): 40 | _graph, _property = loadInputs(FLAGS, i, modelName, unitLen) 41 | num_batches = int(_graph[0].shape[0]/batch_size) 42 | 43 | st = time.time() 44 | for _iter in range(num_batches): 45 | total_iter += 1 46 | A_batch = _graph[0][_iter*FLAGS.batch_size:(_iter+1)*FLAGS.batch_size] 47 | X_batch = _graph[1][_iter*FLAGS.batch_size:(_iter+1)*FLAGS.batch_size] 48 | P_batch = _property[_iter*FLAGS.batch_size:(_iter+1)*FLAGS.batch_size] 49 | if total_iter % 5 != 0: 50 | # Training 51 | cost = model.train(A_batch, X_batch, P_batch) 52 | print ("train_iter : ", total_iter, ", epoch : ", epoch, ", cost : ", cost) 53 | 54 | elif total_iter % 5 == 0: 55 | # Test accuracy 56 | Y, cost = model.test(A_batch, X_batch, P_batch) 57 | print ("test_iter : ", total_iter, ", epoch : ", epoch, ", cost : ", cost) 58 | if( total_iter % 100 == 0 ): 59 | print (Y.flatten()) 60 | print (P_batch) 61 | print (Y.flatten() - P_batch) 62 | mse = (np.mean(np.power((Y.flatten() - P_batch),2))) 63 | mae = (np.mean(np.abs(Y.flatten() - P_batch))) 64 | print ("MSE : ", mse, "\t MAE : ", mae) 65 | 66 | if total_iter % save_every == 0: 67 | # Save network! 68 | ckpt_path = 'save/'+modelName+'.ckpt' 69 | model.save(ckpt_path, total_iter) 70 | 71 | et = time.time() 72 | print ("time : ", et-st) 73 | st = time.time() 74 | 75 | total_et = time.time() 76 | print ("Finish training! Total required time for training : ", (total_et-total_st)) 77 | return 78 | 79 | #os.environ["CUDA_VISIBLE_DEVICES"] = "0" 80 | # Set FLAGS for environment setting 81 | flags = tf.app.flags 82 | FLAGS = flags.FLAGS 83 | flags.DEFINE_string('model', 'GAT', 'Options : GAT, GCN') 84 | flags.DEFINE_string('output', 'logP', '') 85 | flags.DEFINE_string('loss_type', 'MSE', 'Options : MSE') ### Using MSE or Hinge for predictor 86 | flags.DEFINE_string('database', 'ZINC', 'Options : ZINC, QM9, ZINC') 87 | flags.DEFINE_string('optimizer', 'Adam', 'Options : Adam, SGD, RMSProp') 88 | flags.DEFINE_integer('latent_dim', 512, 'Dimension of a latent vector for autoencoder') 89 | flags.DEFINE_integer('epoch_size', 100, 'Epoch size') 90 | flags.DEFINE_integer('batch_size', 100, 'Batch size') 91 | flags.DEFINE_integer('save_every', 1000, 'Save every') 92 | flags.DEFINE_float('learning_rate', 0.001, 'Batch size') 93 | flags.DEFINE_float('decay_rate', 0.95, 'Batch size') 94 | flags.DEFINE_integer('num_DB', 45, '') 95 | flags.DEFINE_integer('unitLen', 10000, '') 96 | 97 | modelName = FLAGS.model + '_' + FLAGS.output + '_' + str(FLAGS.latent_dim) + '_' + str(FLAGS.batch_size) + '_' + FLAGS.database 98 | 99 | print ("Summary of this training & testing") 100 | print ("Model name is", modelName) 101 | print ("A Latent vector dimension is", str(FLAGS.latent_dim)) 102 | print ("A learning rate is", str(FLAGS.learning_rate), "with a decay rate", str(FLAGS.decay_rate)) 103 | print ("Using", FLAGS.loss_type, "for loss function in an optimization") 104 | 105 | model = Graph2Property(FLAGS) 106 | training(model, FLAGS, modelName) 107 | -------------------------------------------------------------------------------- /utils/__pycache__/smilesToGraph2.cpython-36.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/SeongokRyu/Molecular-GAT/ce94278a708db8c9f3b290288c6ba0202d548848/utils/__pycache__/smilesToGraph2.cpython-36.pyc -------------------------------------------------------------------------------- /utils/__pycache__/smilesToGraph_explicit.cpython-36.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/SeongokRyu/Molecular-GAT/ce94278a708db8c9f3b290288c6ba0202d548848/utils/__pycache__/smilesToGraph_explicit.cpython-36.pyc -------------------------------------------------------------------------------- /utils/__pycache__/utils.cpython-36.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/SeongokRyu/Molecular-GAT/ce94278a708db8c9f3b290288c6ba0202d548848/utils/__pycache__/utils.cpython-36.pyc -------------------------------------------------------------------------------- /utils/smilesToGraph.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | from rdkit import Chem 3 | 4 | def adj_k(adj, k): 5 | 6 | ret = adj 7 | for i in range(0, k-1): 8 | ret = np.dot(ret, adj) 9 | 10 | return convertAdj(ret) 11 | 12 | def convertAdj(adj): 13 | 14 | dim = len(adj) 15 | a = adj.flatten() 16 | b = np.zeros(dim*dim) 17 | c = (np.ones(dim*dim)-np.equal(a,b)).astype('float64') 18 | d = c.reshape((dim, dim)) 19 | 20 | return d 21 | 22 | def convertToGraph(smiles_list, k): 23 | adj = [] 24 | adj_norm = [] 25 | features = [] 26 | maxNumAtoms = 50 27 | for i in smiles_list: 28 | # Mol 29 | iMol = Chem.MolFromSmiles(i.strip()) 30 | #Adj 31 | iAdjTmp = Chem.rdmolops.GetAdjacencyMatrix(iMol) 32 | # Feature 33 | if( iAdjTmp.shape[0] <= maxNumAtoms): 34 | # Feature-preprocessing 35 | iFeature = np.zeros((maxNumAtoms, 58)) 36 | iFeatureTmp = [] 37 | for atom in iMol.GetAtoms(): 38 | iFeatureTmp.append( atom_feature(atom) ) ### atom features only 39 | iFeature[0:len(iFeatureTmp), 0:58] = iFeatureTmp ### 0 padding for feature-set 40 | features.append(iFeature) 41 | 42 | # Adj-preprocessing 43 | iAdj = np.zeros((maxNumAtoms, maxNumAtoms)) 44 | iAdj[0:len(iFeatureTmp), 0:len(iFeatureTmp)] = iAdjTmp + np.eye(len(iFeatureTmp)) 45 | adj.append(adj_k(np.asarray(iAdj), k)) 46 | features = np.asarray(features) 47 | 48 | return adj, features 49 | 50 | def atom_feature(atom): 51 | return np.array(one_of_k_encoding_unk(atom.GetSymbol(), 52 | ['C', 'N', 'O', 'S', 'F', 'H', 'Si', 'P', 'Cl', 'Br', 53 | 'Li', 'Na', 'K', 'Mg', 'Ca', 'Fe', 'As', 'Al', 'I', 'B', 54 | 'V', 'Tl', 'Sb', 'Sn', 'Ag', 'Pd', 'Co', 'Se', 'Ti', 'Zn', 55 | 'Ge', 'Cu', 'Au', 'Ni', 'Cd', 'Mn', 'Cr', 'Pt', 'Hg', 'Pb']) + 56 | one_of_k_encoding(atom.GetDegree(), [0, 1, 2, 3, 4, 5]) + 57 | one_of_k_encoding_unk(atom.GetTotalNumHs(), [0, 1, 2, 3, 4]) + 58 | one_of_k_encoding_unk(atom.GetImplicitValence(), [0, 1, 2, 3, 4, 5]) + 59 | [atom.GetIsAromatic()]) # (40, 6, 5, 6, 1) 60 | 61 | def one_of_k_encoding(x, allowable_set): 62 | if x not in allowable_set: 63 | raise Exception("input {0} not in allowable set{1}:".format(x, allowable_set)) 64 | #print list((map(lambda s: x == s, allowable_set))) 65 | return list(map(lambda s: x == s, allowable_set)) 66 | 67 | def one_of_k_encoding_unk(x, allowable_set): 68 | """Maps inputs not in the allowable set to the last element.""" 69 | if x not in allowable_set: 70 | x = allowable_set[-1] 71 | return list(map(lambda s: x == s, allowable_set)) 72 | -------------------------------------------------------------------------------- /utils/smilesToGraph_explicit.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | from rdkit import Chem 3 | 4 | ### Convert SmilesToGraph explicitly to generate input for training atomization energy ### 5 | 6 | def adj_k(adj, k): 7 | 8 | ret = adj 9 | for i in range(0, k-1): 10 | ret = np.dot(ret, adj) 11 | 12 | return convertAdj(ret) 13 | 14 | def convertAdj(adj): 15 | 16 | dim = len(adj) 17 | a = adj.flatten() 18 | b = np.zeros(dim*dim) 19 | c = (np.ones(dim*dim)-np.equal(a,b)).astype('float64') 20 | d = c.reshape((dim, dim)) 21 | 22 | return d 23 | 24 | def convertToGraph(smiles_list, k): 25 | adj = [] 26 | adj_norm = [] 27 | features = [] 28 | maxNumAtoms = 50 29 | for i in smiles_list: 30 | # Mol 31 | iMol = Chem.MolFromSmiles(i.strip()) 32 | iMol = Chem.AddHs(iMol) 33 | #print (iMol.GetNumAtoms()) 34 | #Adj 35 | iAdjTmp = Chem.rdmolops.GetAdjacencyMatrix(iMol) 36 | # Feature 37 | if( iAdjTmp.shape[0] <= maxNumAtoms): 38 | # Feature-preprocessing 39 | iFeature = np.zeros((maxNumAtoms, 58)) 40 | iFeatureTmp = [] 41 | for atom in iMol.GetAtoms(): 42 | iFeatureTmp.append( atom_feature(atom) ) ### atom features only 43 | iFeature[0:len(iFeatureTmp), 0:58] = iFeatureTmp ### 0 padding for feature-set 44 | features.append(iFeature) 45 | 46 | # Adj-preprocessing 47 | iAdj = np.zeros((maxNumAtoms, maxNumAtoms)) 48 | iAdj[0:len(iFeatureTmp), 0:len(iFeatureTmp)] = iAdjTmp + np.eye(len(iFeatureTmp)) 49 | adj.append(adj_k(np.asarray(iAdj), k)) 50 | features = np.asarray(features) 51 | 52 | return adj, features 53 | 54 | def atom_feature(atom): 55 | return np.array(one_of_k_encoding_unk(atom.GetSymbol(), 56 | ['C', 'N', 'O', 'S', 'F', 'H', 'Si', 'P', 'Cl', 'Br', 57 | 'Li', 'Na', 'K', 'Mg', 'Ca', 'Fe', 'As', 'Al', 'I', 'B', 58 | 'V', 'Tl', 'Sb', 'Sn', 'Ag', 'Pd', 'Co', 'Se', 'Ti', 'Zn', 59 | 'Ge', 'Cu', 'Au', 'Ni', 'Cd', 'Mn', 'Cr', 'Pt', 'Hg', 'Pb']) + 60 | one_of_k_encoding(atom.GetDegree(), [0, 1, 2, 3, 4, 5]) + 61 | one_of_k_encoding_unk(atom.GetTotalNumHs(), [0, 1, 2, 3, 4]) + 62 | one_of_k_encoding_unk(atom.GetImplicitValence(), [0, 1, 2, 3, 4, 5]) + 63 | [atom.GetIsAromatic()]) # (40, 6, 5, 6, 1) 64 | 65 | def one_of_k_encoding(x, allowable_set): 66 | if x not in allowable_set: 67 | raise Exception("input {0} not in allowable set{1}:".format(x, allowable_set)) 68 | #print list((map(lambda s: x == s, allowable_set))) 69 | return list(map(lambda s: x == s, allowable_set)) 70 | 71 | def one_of_k_encoding_unk(x, allowable_set): 72 | """Maps inputs not in the allowable set to the last element.""" 73 | if x not in allowable_set: 74 | x = allowable_set[-1] 75 | return list(map(lambda s: x == s, allowable_set)) 76 | -------------------------------------------------------------------------------- /utils/utils.py: -------------------------------------------------------------------------------- 1 | import h5py 2 | import numpy as np 3 | from rdkit import Chem 4 | 5 | def convert_to_smiles(vector, char): 6 | list_char = char.tolist() 7 | vector = vector.astype(int) 8 | return "".join(map(lambda x: list_char[x], vector)).strip() 9 | 10 | def stochastic_convert_to_smiles(vector, char): 11 | list_char = char.tolist() 12 | s = "" 13 | for i in range(len(vector)): 14 | prob = vector[i].tolist() 15 | norm0 = sum(prob) 16 | prob = [i/norm0 for i in prob] 17 | index = np.random.choice(len(list_char), 1, p=prob) 18 | s+=list_char[index[0]] 19 | return s 20 | 21 | def one_hot_array(i, n): 22 | return list(map(int, [ix == i for ix in range(n)])) 23 | 24 | def one_hot_index(vec, charset): 25 | return list(map(charset.index, vec)) 26 | 27 | def from_one_hot_array(vec): 28 | oh = np.where(vec == 1) 29 | if oh[0].shape == (0, ): 30 | return None 31 | return int(oh[0][0]) 32 | 33 | def decode_smiles_from_indexes(vec, charset): 34 | return "".join(map(lambda x: charset[x], vec)).strip() 35 | 36 | def load_dataset(filename, split = True): 37 | h5f = h5py.File(filename, 'r') 38 | if split: 39 | data_train = h5f['data_train'][:] 40 | else: 41 | data_train = None 42 | data_test = h5f['data_test'][:] 43 | charset = h5f['charset'][:] 44 | h5f.close() 45 | if split: 46 | return data_train, data_test, charset 47 | else: 48 | return data_test, charset 49 | 50 | def encode_smiles(smiles, model, charset): 51 | cropped = list(smiles.ljust(120)) 52 | preprocessed = np.array([list(map(lambda x: one_hot_array(x, len(charset)), one_hot_index(cropped, charset)))]) 53 | latent = model.encoder.predict(preprocessed) 54 | return latent 55 | 56 | def smiles_to_onehot(smiles, charset): 57 | cropped = list(smiles.ljust(120)) 58 | preprocessed = np.array([list(map(lambda x: one_hot_array(x, len(charset)), one_hot_index(cropped, charset)))]) 59 | return preprocessed 60 | 61 | def smiles_to_vector(smiles, vocab, max_length): 62 | while len(smiles)