A curated list of awesome papers, data sets, frameworks, packages, blogs, and other resources related to machine learning for small-molecule drug discovery. Please contribute!

• Papers
  • Survey papers and books
  • Representation, transfer, and few-shot learning
  • Generative algorithms
  • Hit finding and potency prediciton
  • ADME and toxicity prediction
  • Synthetic accessability and retrosynthetic planning
  • DNA-encoded libraries (DELs)
  • Visualization and interpretability
  • MS/MS prediction
• Data sets
• Frameworks, Libraries, and Software Tools
• Blogs
• Twitter
• Related lists

• Walters and Barzilay, 2021. Critical assessment of AI in drug discovery.
• White, 2021. Deep Learning for Molecules and Materials.
• Coley, 2020. Defining and Exploring Chemical Spaces.
• Chuang et al, 2020. Learning Molecular Representations for Medicinal Chemistry.
• Walters and Barzilay, 2020. Applications of Deep Learning in Molecule Generation and Molecular Property Prediction.
• Cai et al, 2020. Transfer Learning for Drug Discovery.

• Krenn et al, 2022. SELFIES and the future of molecular string representations.
• Wang et al, 2022. Molecular Contrastive Learning of Representations via Graph Neural Networks. [Code]
• Ahmad et al, 2021. ChemBERTa-2: Towards Chemical Foundation Models. [Code]
• Satorras et al, 2021. E(n) Equivariant Graph Neural Networks. [Code]
• Stanley et al, 2021. FS-Mol: A Few-Shot Learning Dataset of Molecules. [Code]
• Townshend et al, 2021. ATOM3D: Tasks On Molecules in Three Dimensions.
• Xue et al, 2021. X-MOL: large-scale pre-training for molecular understanding and diverse molecular analysis. [Code]
• Ying et al, 2021. Do Transformers Really Perform Bad for Graph Representation? (Graphormer paper). [Code]
• Chuang and Keiser, 2020. Attention-Based Learning on Molecular Ensembles.
• Li and Fourches, 2020. Inductive transfer learning for molecular activity prediction: Next-Gen QSAR Models with MolPMoFiT. [Code]
• Maziarka et al, 2020. Molecule Attention Transformer. [Code]
• Nguyen et al., 2020. Meta-Learning GNN Initializations for Low-Resource Molecular Property Prediction [Code]
• Rong et al., 2020. Self-Supervised Graph Transformer on Large-Scale Molecular Data (GROVER paper). [Code]
• Hu et al, 2019. Strategies for Pre-training Graph Neural Networks. [Code]
• Yang et al, 2019. Analyzing Learned Molecular Representations for Property Prediction (Chemprop). [Code]
• Feinberg et al, 2018. PotentialNet for Molecular Property Prediction.
• Altae-Tran et al, 2017. Low Data Drug Discovery with One-Shot Learning.

• Bengio et al, 2021. Flow Network based Generative Models for Non-Iterative Diverse Candidate Generation. [Code]
• Berenger and Tsuda, 2021. Molecular generation by Fast Assembly of (Deep)SMILES fragments. [Code]
• Gao et al, 2021. Amortized Tree Generation for Bottom-up Synthesis Planning and Synthesizable Molecular Design. [Code]
• Takeuchi et al, 2021. R-group replacement database for medicinal chemistry.
• Imrie et al, 2020. Deep Generative Models for 3D Linker Design. [Code]
• Jin et al, 2020. Hierarchical Generation of Molecular Graphs using Structural Motifs. [Code]
• Polishchuk, 2020. CReM: chemically reasonable mutations framework for structure generation. [Code]
• Brown, 2019. GuacaMol: Benchmarking Models for de Novo Molecular Design. [Code]
• Popova et al, 2019. MolecularRNN: Generating realistic molecular graphs with optimized properties .
• You et al, 2019. Graph Convolutional Policy Network for Goal-Directed Molecular Graph Generation. [Code]
• Zhou et al, 2019. Optimization of Molecules via Deep Reinforcement Learning. [Code (official version)] [PyTorch implementation]
• Jin et al, 2018. Junction Tree Variational Autoencoder for Molecular Graph Generation. [Code]
• Merk et al, 2018. De Novo Design of Bioactive Small Molecules by Artificial Intelligence.

• Stärk et al, 2022. EquiBind: Geometric Deep Learning for Drug Binding Structure Prediction. [Code]
• Bender et al, 2021. A practical guide to large-scale docking.
• García-Ortegón et al, 2021. DOCKSTRING: easy molecular docking yields better benchmarks for ligand design. [Code] [Data]
• Graff et al, 2021. Accelerating high-throughput virtual screening through molecular pool-based active learning. [Code]
• Gentile et al, 2020. Deep Docking: A Deep Learning Platform for Augmentation of Structure Based Drug Discovery. [Code]
• Cáceres et al, 2020. Adding Stochastic Negative Examples into Machine Learning Improves Molecular Bioactivity Prediction.
• Lin et al, 2019. Ultra-large library docking for discovering new chemotypes.

• Fradkin et al, 2022. A Graph Neural Network Approach to Molecule Carcinogenicity Prediction.
• Karim et al, 2021. CardioTox net: a robust predictor for hERG channel blockade based on deep learning meta-feature ensembles. [Code]
• Siramshetty et al, 2021. Validating ADME QSAR Models Using Marketed Drugs.
• Göller et al, 2020. Bayer’s in silico ADMET platform: a journey of machine learning over the past two decades.
• Ryu et al, 2020. DeepHIT: a deep learning framework for prediction of hERG-induced cardiotoxicity. [Code]
• Cai et al, 2019. Deep Learning-Based Prediction of Drug-Induced Cardiotoxicity. [Code]
• Ogura et al, 2019. Support Vector Machine model for hERG inhibitory activities based on the integrated hERG database using descriptor selection by NSGA-II. [Data]
• Lombardo et al, 2018. In Silico Absorption, Distribution, Metabolism, Excretion, and Pharmacokinetics (ADME-PK): Utility and Best Practices.

• Fortunato et al, 2020. Data augmentation and pretraining for template-based retrosynthetic prediction in computer-aided synthesis planning.
• Koch et al, 2020. Reinforcement Learning for Bioretrosynthesis.
• Somnath et al, 2020. Learning Graph Models for Retrosynthesis Prediction.
• Dai et al, 2019. Retrosynthesis Prediction with Conditional Graph Logic Network. [Code]
• Coley et al, 2018. SCScore: Synthetic Complexity Learned from a Reaction Corpus. [Code] [DeepChem implementation]

• Lim et al, 2022. Machine Learning on DNA-Encoded Library Count Data Using an Uncertainty-Aware Probabilistic Loss Function. [Code]
• McCloskey et al, 2020. Machine Learning on DNA-Encoded Libraries: A New Paradigm for Hit Finding.

• Humer et al, 2021. ChemInformatics Model Explorer (CIME): Exploratory analysis of chemical model explanations. [Code]
• Matveieva and Polishchuk, 2021. Benchmarks for interpretation of QSAR models. [Code]
• Atsushi et al, 2019. Integrating the Structure–Activity Relationship Matrix Method with Molecular Grid Maps and Activity Landscape Models for Medicinal Chemistry Applications.
• Naveja and Medina-Franco, 2019. Finding Constellations in Chemical Space Through Core Analysis.

• Young et al, 2023. MassFormer: Tandem Mass Spectrum Prediction for Small Molecules using Graph Transformers. [Code]
• Goldman el al, 2023. Prefix-Tree Decoding for Predicting Mass Spectra from Molecules. [Code]
• Hong et al, 2023. 3DMolMS: prediction of tandem mass spectra from 3D molecular conformations. [Code]
• Wang et al, 2021. CFM-ID 4.0: More Accurate ESI-MS/MS Spectral Prediction and Compound Identification.
• Wei et al, 2019. Rapid Prediction of Electron–Ionization Mass Spectrometry Using Neural Networks. [Code]

• ADME@NCATS
• AMED Cardiotoxicity Database
• BindingDB
• ChEMBL
• DrugBank
• DrugMatrix
• Enamine Real database
• hERG Central
• MoleculeNet
• MONA: DB of Mass spec + other readouts
• NPASS database of natural products
• PubChem
• The Open Reaction Database
• Therapeutic Data Commons
• Zinc

• AutoDock Vina
• BioPandas
• Chemprop
• DeepChem [Tutorials]
• Open Babel
• pdb-tools
• PyTorch Geometric
• rd_filters
• Small-World Search
• TorchDrug

• Hyperparameter Space
• Is Life Worth Living
• Practical Cheminformatics
• RDKit Blog

• Regina Barzilay
• Bob the Grumpy Med Chemist
• John Chodera
• Connor W. Coley
• Greg Landrum
• pen(Taka)
• Bharath Ramsundar
• Marwin Segler
• Patrick Walters

• Awesome Cheminformatics
• Awesome Drug Discovery
• Awesome Explainable Graph Reasoning
• Awesome Python Chemistry
• deeplearning-biology