awesome-small-molecule-ml
by benb111
A curated list of resources for machine learning for small-molecule drug discovery
AI summary
Machine Learning Resources
A curated collection of resources and papers related to machine learning in small-molecule drug discovery
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What's in the list
110 links in 14 sections, with live GitHub stats.activeno commit in 2y
Papers / Survey papers and books
- Critical assessment of AI in drug discovery
Walters and Barzilay, 2021.
- Deep Learning for Molecules and Materials
White, 2021.
- Defining and Exploring Chemical Spaces
Coley, 2020.
- Learning Molecular Representations for Medicinal Chemistry
Chuang et al, 2020.
- Applications of Deep Learning in Molecule Generation and Molecular Property Prediction
Walters and Barzilay, 2020.
- Transfer Learning for Drug Discovery
Cai et al, 2020.
Papers / Representation, transfer learning, and few-shot learning
- SELFIES and the future of molecular string representations
Krenn et al, 2022.
- Molecular Contrastive Learning of Representations via Graph Neural Networks
Wang et al, 2022. . [ ]
- ChemBERTa-2: Towards Chemical Foundation Models
Ahmad et al, 2021. . [ ]
- E(n) Equivariant Graph Neural Networks
Satorras et al, 2021. . [ ]
- FS-Mol: A Few-Shot Learning Dataset of Molecules
Stanley et al, 2021. . [ ]
- ATOM3D: Tasks On Molecules in Three Dimensions
Townshend et al, 2021.
- Do Transformers Really Perform Bad for Graph Representation? (Graphormer paper)
Ying et al, 2021. . [ ]
- Attention-Based Learning on Molecular Ensembles
Chuang and Keiser, 2020.
- Inductive transfer learning for molecular activity prediction: Next-Gen QSAR Models with MolPMoFiT
Li and Fourches, 2020. . [ ]
- Molecule Attention Transformer
Maziarka et al, 2020. . [ ]
- Meta-Learning GNN Initializations for Low-Resource Molecular Property Prediction
Nguyen et al., 2020. [ ]
- Self-Supervised Graph Transformer on Large-Scale Molecular Data (GROVER paper)
Rong et al., 2020. . [ ]
- Strategies for Pre-training Graph Neural Networks
Hu et al, 2019. . [ ]
- Analyzing Learned Molecular Representations for Property Prediction (Chemprop)
Yang et al, 2019. . [ ]
- PotentialNet for Molecular Property Prediction
Feinberg et al, 2018.
- Low Data Drug Discovery with One-Shot Learning
Altae-Tran et al, 2017.
Papers / Generative algorithms
- Flow Network based Generative Models for Non-Iterative Diverse Candidate Generation
Bengio et al, 2021. . [ ]
- Molecular generation by Fast Assembly of (Deep)SMILES fragments
Berenger and Tsuda, 2021. . [ ]
- R-group replacement database for medicinal chemistry
Takeuchi et al, 2021.
- Deep Generative Models for 3D Linker Design
Imrie et al, 2020. . [ ]
- Hierarchical Generation of Molecular Graphs using Structural Motifs
Jin et al, 2020. . [ ]
- CReM: chemically reasonable mutations framework for structure generation
Polishchuk, 2020. . [ ]
- GuacaMol: Benchmarking Models for de Novo Molecular Design
Brown, 2019. . [ ]
- Graph Convolutional Policy Network for Goal-Directed Molecular Graph Generation
You et al, 2019. . [ ]
- Optimization of Molecules via Deep Reinforcement Learning
Zhou et al, 2019. . [ ] [ ]
- Junction Tree Variational Autoencoder for Molecular Graph Generation
Jin et al, 2018. . [ ]
Papers / Hit finding and potency prediciton
- EquiBind: Geometric Deep Learning for Drug Binding Structure Prediction
Stärk et al, 2022. . [ ]
- A practical guide to large-scale docking
Bender et al, 2021.
- DOCKSTRING: easy molecular docking yields better benchmarks for ligand design
García-Ortegón et al, 2021. . [ ] [ ]
- Accelerating high-throughput virtual screening through molecular pool-based active learning
Graff et al, 2021. . [ ]
- Deep Docking: A Deep Learning Platform for Augmentation of Structure Based Drug Discovery
Gentile et al, 2020. . [ ]
- Ultra-large library docking for discovering new chemotypes
Lin et al, 2019.
Papers / ADME and toxicity prediction
- A Graph Neural Network Approach to Molecule Carcinogenicity Prediction
Fradkin et al, 2022.
- Validating ADME QSAR Models Using Marketed Drugs
Siramshetty et al, 2021.
- DeepHIT: a deep learning framework for prediction of hERG-induced cardiotoxicity
Ryu et al, 2020. . [ ]
- Deep Learning-Based Prediction of Drug-Induced Cardiotoxicity
Cai et al, 2019. . [ ]
Papers / Synthetic accessability and retrosynthetic planning
- Reinforcement Learning for Bioretrosynthesis
Koch et al, 2020.
- Learning Graph Models for Retrosynthesis Prediction
Somnath et al, 2020.
- Retrosynthesis Prediction with Conditional Graph Logic Network
Dai et al, 2019. . [ ]
- SCScore: Synthetic Complexity Learned from a Reaction Corpus
Coley et al, 2018. . [ ] [ ]
Papers / DNA-encoded libraries (DELs)
- Machine Learning on DNA-Encoded Libraries: A New Paradigm for Hit Finding
McCloskey et al, 2020.
Papers / Visualization and interpretability
- ChemInformatics Model Explorer (CIME): Exploratory analysis of chemical model explanations
Humer et al, 2021. . [ ]
- Benchmarks for interpretation of QSAR models
Matveieva and Polishchuk, 2021. . [ ]
- Finding Constellations in Chemical Space Through Core Analysis
Naveja and Medina-Franco, 2019.
Papers / MS/MS prediction
- MassFormer: Tandem Mass Spectrum Prediction for Small Molecules using Graph Transformers
Young et al, 2023. . [ ]
- Prefix-Tree Decoding for Predicting Mass Spectra from Molecules
Goldman el al, 2023. . [ ]
- 3DMolMS: prediction of tandem mass spectra from 3D molecular conformations
Hong et al, 2023. . [ ]
- Rapid Prediction of Electron–Ionization Mass Spectrometry Using Neural Networks
Wei et al, 2019. . [ ]
Data sets
Frameworks, Libraries, and Software Tools
- DeepChem
[ ]
Chemprop
rd_filtersBlogs
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