└── README.md /README.md: -------------------------------------------------------------------------------- 1 | # mlchempapers 2 | A semisorted, working list of ML (mostly deep learning) papers relating to chemistry, biology, and drug discovery. 3 | 4 | ### Deep Learning Review Articles 5 | 1. "Deep Learning for Computational Chemistry." 6 | Goh, G. B.; Hoda, N. O.; Vishnu, A. *J. Comput. Chem.* **2017**, DOI: 10.1002/jcc.24764 7 | 8 | 2. "A Renaissance of Neural Networks in Drug Discovery." 9 | Baskin, I. I.; Winkler, D.; Tetko, I. V. *Exp. Op. Drug Disc.* **2016**, *11* 785. DOI: 10.1080/17460441 10 | 11 | 3. "Deep Learning in Drug Discovery." 12 | Gawehn, E.; Hiss, J. A.; Schneider, G. *Mol. Inf.* **2016**, *35*, 3. 13 | 14 | 4. "Have Artificial Neural Networks Met Expectations in Drug Discovery as Implemented in QSAR framework?" 15 | Dobchev, D. & Karelson, M. *Expert Opin. Drug Discov.* **2016**, *11*, 627. 16 | 17 | 5. "The Next Era: Deep Learning in Pharmaceutical Research." 18 | Ekins, S. *Pharm. Res.* **2016**, *33*, 2594. 19 | 20 | 21 | ### Machine Learning in Pharma Reviews 22 | 1. "Machine-learning approaches in drug discovery: methods and applications." 23 | Lavecchia, A. *Drug Discov. Today*, **2015**, *20*, 318. 24 | 2. "Machine learning methods in chemoinformatics." 25 | Mitchell, J. B. O. WIREs Comput. *Mol. Sci.* **2014**, *4*, 468. 26 | 27 | ### Representation 28 | 1. "Convolutional Networks on Graphs for Learning Molecular Fingerprints" 29 | David Duvenaud, D.; Maclaurin, D.; Aguilera-Iparraguirre, J.; Gomez-Bombarelli, R.; Hirzel, T.; Aspuru-Guzik, A.; Adams, R. P. 30 | 2. "Automatic Chemical Design Using a Data-Driven Continuous Representation of Molecules." 31 | Gómez-Bombarelli, R.; David Duvenaud, D.; Hernandez-Lobato, J. M.; Aguilera-Iparraguirre, J.; Adams, R. P.; Aspuru-Guzik, A. 32 | 3. "Molecular graph convolutions: moving beyond fingerprints." 33 | Kearnes, S. McCloskey, K.; Berndl, M.; Pande, V.; Riley, P. J. Computer Aided Drug Design. **2016**, *30*, 595. 34 | 4. "Atomic Convolutional Networks for Predicting Protein-Ligand Binding Affinity." 35 | Gomes, J.; Ramsundar, B.; Feinberg, E. N.; Pande, V. S. 36 | 37 | ### DFT Benchmarks 38 | 1. "ANI-1: An Extensible Neural Network Potential with DFT accuracy at force field computational cost" 39 | 2. "Fast machine learning models of electronic and energetic properties consistently reach approximation errors better than DFT accuracy." 40 | 3. "Neural Message Passing for Quantum Chemistry." 41 | 42 | ### One-Shot Learning 43 | 1. "Low Data Drug Discovery with One-Shot Learning." 44 | 45 | ### Multitask Networks 46 | 1. "Multi-task Neural Networks for QSAR Predictions" 47 | Dahl, G. E.; Jaitly, N.; Salakhutdinov, R. 48 | 2. "Deep Neural Nets as a Method for Quantitative Structure-Activity Relationships." 49 | Ma, J.; Sheridan, R. P.; Liaw, A.; Dahl, G. E.; Svetnik, V. J. Chem. Inf. Model. 2015, 55, 263. 50 | 3. "Toxicity Prediction using Deep Learning." 51 | 4. "Deep Learning as an Opportunity in Virtual Screening." 52 | Unterthiner, T.; Mayr, A.; Klambauer, G. NIPS 2014. 53 | 5. "Massively Multitask Networks for Drug Discovery." 54 | 2015. https://arxiv.org/abs/1502.02072 55 | 6. "Deep Learning Applications for Predicting Pharamcological Properties of Drugs and Drug Repurposing Using Transcriptomic Data." 56 | 7. "Molecular Fingerprint-Baesd Artificial Neural Networks QSAR for Ligand Biological Activity Predictions." 57 | 58 | ### Benchmarks 59 | 1. "MoleculeNet: A Benchmark for Molecular Machine Learning." 60 | 61 | ### Generative Adversarial Networks 62 | 1. "The Cornucopia of Meaningful Leads: Applying Deep Adversarial Autoencoders for New Molecule Development in Oncology" 63 | 64 | ### Molecular Properties 65 | 1. "Deep Architectures and Deep Learning in Chemoinformatics: the Prediction of Aqueous Solubility for Drug-like Molecules" 66 | Lusci, A., Pollastri, G. & Baldi, P. J. Chem. Inf. Model. 53, 1563–1575 (2013). 67 | 68 | ### Metabolism 69 | 1. "Deep Learning to Predict the Formation of Quinone Species in Drug Metabolism." 70 | Hughes, T. B.; Swamidass. S. J. *Chem. Res. Toxicol.* **2017**, *30*, 642. 71 | 2. "Modeling Epoxidation of Drug-like Molecules with a Deep Learning Network." 72 | Hughes, T. B.; Miller, G. P.; Swamidass, S. J. *ACS Cent. Sci*, **2015**, *1*, 168. 73 | 3. "Modeling Reactivity to Biological Macromolecules with a Deep Multitask Network." 74 | Hughes, T. B.; Dang, N. L.; Miller, G. P.; Swamidass, S. J. *ACS Cent Sci.* **2016**, *2*, 529. 75 | 4. "Deep Learning for Drug-Induced Liver Injury." 76 | Xu, Y.; Dai, Z.; Chen, F.; Gao, S.; Pei, J.; Lai, L. *J. Chem. Inf. Model.* **2015**, *55*,2085. 77 | 78 | ### Structure-Based Virtual Screening 79 | 1. "AtomNet: A Deep Convolutional Neural Network for Bioactivity Prediction in Structure-based Drug Discovery." 80 | Wallach, I.; Dzamba, M.; Heifets, A. **2015**. arXiv:1510.02855v1 81 | 2. "NNScore: A Neural-Network-Based Scoring Function for the Characterization of Protein−Ligand Complexes" 82 | Durrant, J. D.; McCammon, J. A. *J. Chem. Inf. Model.* **2010**, *50*, 1865. 83 | 3. "NNScore 2.0: A Neural-Network Receptor–Ligand Scoring Function" 84 | 4. "Predicting Ligand Binding Modes from Neural Networks Trained on Protein–Ligand Interaction Fingerprints" 85 | 86 | 5. "Protein–Ligand Scoring with Convolutional Neural Networks." 87 | Matthew Ragoza, M.; Hochuli, J.; Idrobo, E.; Sunseri, J.; Koes, D. R. *J. Chem. Inf. Model.* **2017**, *57*, 942. 88 | 6. "Learning Deep Architectures for Interaction Prediction in Structure-based Virtual Screening." 89 | Gonczarek, A.; Tomczak, J. M; Zareba, S.; Kaczmar, J.; Dabrowski, P,; Walczak, M. J. arXiv:1610.07187. **2016** 90 | 91 | ### Organic Reaction Prediction 92 | 1. "Neural Networks for the Prediction of Organic Chemistry Reactions" 93 | Wei, J. N.; Duvenaud, Aspuru-Guzik, A. ACS Cent Sci **2016**, 2, 725. 94 | 2. "Prediction of Organic Reaction Outcomes Using Machine Learning" 95 | Coley, C. W.; Barzilay, R.; Jaakola, T. S.; Green, W. H.; Jensen, K. F. *ACS Cent Sci* **2017**. 96 | --------------------------------------------------------------------------------