mlchempapers

A semisorted, working list of ML (mostly deep learning) papers relating to chemistry, biology, and drug discovery.

Deep Learning Review Articles

1. "Deep Learning for Computational Chemistry." Goh, G. B.; Hoda, N. O.; Vishnu, A. J. Comput. Chem. 2017, DOI: 10.1002/jcc.24764

2. "A Renaissance of Neural Networks in Drug Discovery." Baskin, I. I.; Winkler, D.; Tetko, I. V. Exp. Op. Drug Disc. 2016, 11 785. DOI: 10.1080/17460441

3. "Deep Learning in Drug Discovery." Gawehn, E.; Hiss, J. A.; Schneider, G. Mol. Inf. 2016, 35, 3.

4. "Have Artificial Neural Networks Met Expectations in Drug Discovery as Implemented in QSAR framework?" Dobchev, D. & Karelson, M. Expert Opin. Drug Discov. 2016, 11, 627.

5. "The Next Era: Deep Learning in Pharmaceutical Research." Ekins, S. Pharm. Res. 2016, 33, 2594.

Machine Learning in Pharma Reviews

1. "Machine-learning approaches in drug discovery: methods and applications." Lavecchia, A. Drug Discov. Today, 2015, 20, 318.
2. "Machine learning methods in chemoinformatics." Mitchell, J. B. O. WIREs Comput. Mol. Sci. 2014, 4, 468.

Representation

1. "Convolutional Networks on Graphs for Learning Molecular Fingerprints" David Duvenaud, D.; Maclaurin, D.; Aguilera-Iparraguirre, J.; Gomez-Bombarelli, R.; Hirzel, T.; Aspuru-Guzik, A.; Adams, R. P.
2. "Automatic Chemical Design Using a Data-Driven Continuous Representation of Molecules." Gómez-Bombarelli, R.; David Duvenaud, D.; Hernandez-Lobato, J. M.; Aguilera-Iparraguirre, J.; Adams, R. P.; Aspuru-Guzik, A.
3. "Molecular graph convolutions: moving beyond fingerprints." Kearnes, S. McCloskey, K.; Berndl, M.; Pande, V.; Riley, P. J. Computer Aided Drug Design. 2016, 30, 595.
4. "Atomic Convolutional Networks for Predicting Protein-Ligand Binding Affinity." Gomes, J.; Ramsundar, B.; Feinberg, E. N.; Pande, V. S.

DFT Benchmarks

1. "ANI-1: An Extensible Neural Network Potential with DFT accuracy at force field computational cost"
2. "Fast machine learning models of electronic and energetic properties consistently reach approximation errors better than DFT accuracy."
3. "Neural Message Passing for Quantum Chemistry."

One-Shot Learning

1. "Low Data Drug Discovery with One-Shot Learning."

Multitask Networks

1. "Multi-task Neural Networks for QSAR Predictions" Dahl, G. E.; Jaitly, N.; Salakhutdinov, R.
2. "Deep Neural Nets as a Method for Quantitative Structure-Activity Relationships." Ma, J.; Sheridan, R. P.; Liaw, A.; Dahl, G. E.; Svetnik, V. J. Chem. Inf. Model. 2015, 55, 263.
3. "Toxicity Prediction using Deep Learning."
4. "Deep Learning as an Opportunity in Virtual Screening." Unterthiner, T.; Mayr, A.; Klambauer, G. NIPS 2014.
5. "Massively Multitask Networks for Drug Discovery."
6. https://arxiv.org/abs/1502.02072
7. "Deep Learning Applications for Predicting Pharamcological Properties of Drugs and Drug Repurposing Using Transcriptomic Data."
8. "Molecular Fingerprint-Baesd Artificial Neural Networks QSAR for Ligand Biological Activity Predictions."

Benchmarks

1. "MoleculeNet: A Benchmark for Molecular Machine Learning."

Generative Adversarial Networks

1. "The Cornucopia of Meaningful Leads: Applying Deep Adversarial Autoencoders for New Molecule Development in Oncology"

Molecular Properties

1. "Deep Architectures and Deep Learning in Chemoinformatics: the Prediction of Aqueous Solubility for Drug-like Molecules" Lusci, A., Pollastri, G. & Baldi, P. J. Chem. Inf. Model. 53, 1563–1575 (2013).

Metabolism

1. "Deep Learning to Predict the Formation of Quinone Species in Drug Metabolism." Hughes, T. B.; Swamidass. S. J. Chem. Res. Toxicol. 2017, 30, 642.
2. "Modeling Epoxidation of Drug-like Molecules with a Deep Learning Network." Hughes, T. B.; Miller, G. P.; Swamidass, S. J. ACS Cent. Sci, 2015, 1, 168.
3. "Modeling Reactivity to Biological Macromolecules with a Deep Multitask Network." Hughes, T. B.; Dang, N. L.; Miller, G. P.; Swamidass, S. J. ACS Cent Sci. 2016, 2, 529.
4. "Deep Learning for Drug-Induced Liver Injury." Xu, Y.; Dai, Z.; Chen, F.; Gao, S.; Pei, J.; Lai, L. J. Chem. Inf. Model. 2015, 55,2085.

Structure-Based Virtual Screening

1. "AtomNet: A Deep Convolutional Neural Network for Bioactivity Prediction in Structure-based Drug Discovery." Wallach, I.; Dzamba, M.; Heifets, A. 2015. arXiv:1510.02855v1

2. "NNScore: A Neural-Network-Based Scoring Function for the Characterization of Protein−Ligand Complexes" Durrant, J. D.; McCammon, J. A. J. Chem. Inf. Model. 2010, 50, 1865.

3. "NNScore 2.0: A Neural-Network Receptor–Ligand Scoring Function"

4. "Predicting Ligand Binding Modes from Neural Networks Trained on Protein–Ligand Interaction Fingerprints"

5. "Protein–Ligand Scoring with Convolutional Neural Networks." Matthew Ragoza, M.; Hochuli, J.; Idrobo, E.; Sunseri, J.; Koes, D. R. J. Chem. Inf. Model. 2017, 57, 942.

6. "Learning Deep Architectures for Interaction Prediction in Structure-based Virtual Screening." Gonczarek, A.; Tomczak, J. M; Zareba, S.; Kaczmar, J.; Dabrowski, P,; Walczak, M. J. arXiv:1610.07187. 2016

Organic Reaction Prediction

1. "Neural Networks for the Prediction of Organic Chemistry Reactions" Wei, J. N.; Duvenaud, Aspuru-Guzik, A. ACS Cent Sci 2016, 2, 725.
2. "Prediction of Organic Reaction Outcomes Using Machine Learning" Coley, C. W.; Barzilay, R.; Jaakola, T. S.; Green, W. H.; Jensen, K. F. ACS Cent Sci 2017.