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awesome-small-molecule-ml
A curated list of resources for machine learning for small-molecule drug discovery
https://github.com/benb111/awesome-small-molecule-ml
Last synced: 6 days ago
JSON representation
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Papers
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Hit finding and potency prediciton
- A practical guide to large-scale docking
- Ultra-large library docking for discovering new chemotypes
- EquiBind: Geometric Deep Learning for Drug Binding Structure Prediction
- A practical guide to large-scale docking
- DOCKSTRING: easy molecular docking yields better benchmarks for ligand design
- Accelerating high-throughput virtual screening through molecular pool-based active learning
- Deep Docking: A Deep Learning Platform for Augmentation of Structure Based Drug Discovery - Docking-NonAutomated)]
- Adding Stochastic Negative Examples into Machine Learning Improves Molecular Bioactivity Prediction
- Ultra-large library docking for discovering new chemotypes
- A practical guide to large-scale docking
- Ultra-large library docking for discovering new chemotypes
- A practical guide to large-scale docking
- Ultra-large library docking for discovering new chemotypes
- A practical guide to large-scale docking
- Ultra-large library docking for discovering new chemotypes
- A practical guide to large-scale docking
- Ultra-large library docking for discovering new chemotypes
- A practical guide to large-scale docking
- Ultra-large library docking for discovering new chemotypes
- A practical guide to large-scale docking
- Ultra-large library docking for discovering new chemotypes
- A practical guide to large-scale docking
- Ultra-large library docking for discovering new chemotypes
- A practical guide to large-scale docking
- Ultra-large library docking for discovering new chemotypes
- A practical guide to large-scale docking
- Ultra-large library docking for discovering new chemotypes
- A practical guide to large-scale docking
- Ultra-large library docking for discovering new chemotypes
- A practical guide to large-scale docking
- Ultra-large library docking for discovering new chemotypes
- A practical guide to large-scale docking
- Ultra-large library docking for discovering new chemotypes
- A practical guide to large-scale docking
- Ultra-large library docking for discovering new chemotypes
- A practical guide to large-scale docking
- Ultra-large library docking for discovering new chemotypes
- A practical guide to large-scale docking
- Ultra-large library docking for discovering new chemotypes
- A practical guide to large-scale docking
- Ultra-large library docking for discovering new chemotypes
- A practical guide to large-scale docking
- Ultra-large library docking for discovering new chemotypes
- A practical guide to large-scale docking
- Ultra-large library docking for discovering new chemotypes
- A practical guide to large-scale docking
- Ultra-large library docking for discovering new chemotypes
- A practical guide to large-scale docking
- Ultra-large library docking for discovering new chemotypes
- A practical guide to large-scale docking
- Ultra-large library docking for discovering new chemotypes
- A practical guide to large-scale docking
- Ultra-large library docking for discovering new chemotypes
- A practical guide to large-scale docking
- Ultra-large library docking for discovering new chemotypes
- A practical guide to large-scale docking
- Ultra-large library docking for discovering new chemotypes
- A practical guide to large-scale docking
- Ultra-large library docking for discovering new chemotypes
- A practical guide to large-scale docking
- Ultra-large library docking for discovering new chemotypes
- A practical guide to large-scale docking
- Ultra-large library docking for discovering new chemotypes
- A practical guide to large-scale docking
- Ultra-large library docking for discovering new chemotypes
- A practical guide to large-scale docking
- Ultra-large library docking for discovering new chemotypes
- A practical guide to large-scale docking
- Ultra-large library docking for discovering new chemotypes
- A practical guide to large-scale docking
- Ultra-large library docking for discovering new chemotypes
- A practical guide to large-scale docking
- Ultra-large library docking for discovering new chemotypes
- A practical guide to large-scale docking
- Ultra-large library docking for discovering new chemotypes
- A practical guide to large-scale docking
- Ultra-large library docking for discovering new chemotypes
- A practical guide to large-scale docking
- Ultra-large library docking for discovering new chemotypes
- A practical guide to large-scale docking
- Ultra-large library docking for discovering new chemotypes
- A practical guide to large-scale docking
- Ultra-large library docking for discovering new chemotypes
- A practical guide to large-scale docking
- Ultra-large library docking for discovering new chemotypes
- A practical guide to large-scale docking
- Ultra-large library docking for discovering new chemotypes
- A practical guide to large-scale docking
- Ultra-large library docking for discovering new chemotypes
- A practical guide to large-scale docking
- Ultra-large library docking for discovering new chemotypes
- A practical guide to large-scale docking
- Ultra-large library docking for discovering new chemotypes
- A practical guide to large-scale docking
- Ultra-large library docking for discovering new chemotypes
- A practical guide to large-scale docking
- Ultra-large library docking for discovering new chemotypes
- A practical guide to large-scale docking
- Ultra-large library docking for discovering new chemotypes
- A practical guide to large-scale docking
- Ultra-large library docking for discovering new chemotypes
- A practical guide to large-scale docking
- Ultra-large library docking for discovering new chemotypes
- A practical guide to large-scale docking
- Ultra-large library docking for discovering new chemotypes
- A practical guide to large-scale docking
- Ultra-large library docking for discovering new chemotypes
- A practical guide to large-scale docking
- Ultra-large library docking for discovering new chemotypes
- A practical guide to large-scale docking
- A practical guide to large-scale docking
- Ultra-large library docking for discovering new chemotypes
- Ultra-large library docking for discovering new chemotypes
- A practical guide to large-scale docking
- Ultra-large library docking for discovering new chemotypes
- A practical guide to large-scale docking
- Ultra-large library docking for discovering new chemotypes
- A practical guide to large-scale docking
- Ultra-large library docking for discovering new chemotypes
- A practical guide to large-scale docking
- Ultra-large library docking for discovering new chemotypes
- A practical guide to large-scale docking
- Ultra-large library docking for discovering new chemotypes
- A practical guide to large-scale docking
- Ultra-large library docking for discovering new chemotypes
- A practical guide to large-scale docking
- Ultra-large library docking for discovering new chemotypes
- A practical guide to large-scale docking
- Ultra-large library docking for discovering new chemotypes
- A practical guide to large-scale docking
- Ultra-large library docking for discovering new chemotypes
- A practical guide to large-scale docking
- Ultra-large library docking for discovering new chemotypes
- A practical guide to large-scale docking
- Ultra-large library docking for discovering new chemotypes
- A practical guide to large-scale docking
- Ultra-large library docking for discovering new chemotypes
- A practical guide to large-scale docking
- Ultra-large library docking for discovering new chemotypes
- A practical guide to large-scale docking
- Ultra-large library docking for discovering new chemotypes
- A practical guide to large-scale docking
- Ultra-large library docking for discovering new chemotypes
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ADME and toxicity prediction
- Support Vector Machine model for hERG inhibitory activities based on the integrated hERG database using descriptor selection by NSGA-II
- A Graph Neural Network Approach to Molecule Carcinogenicity Prediction
- CardioTox net: a robust predictor for hERG channel blockade based on deep learning meta-feature ensembles
- Validating ADME QSAR Models Using Marketed Drugs
- Bayer’s in silico ADMET platform: a journey of machine learning over the past two decades
- DeepHIT: a deep learning framework for prediction of hERG-induced cardiotoxicity
- Deep Learning-Based Prediction of Drug-Induced Cardiotoxicity - Lab/deephERG)]
- Support Vector Machine model for hERG inhibitory activities based on the integrated hERG database using descriptor selection by NSGA-II
- In Silico Absorption, Distribution, Metabolism, Excretion, and Pharmacokinetics (ADME-PK): Utility and Best Practices
- Support Vector Machine model for hERG inhibitory activities based on the integrated hERG database using descriptor selection by NSGA-II
- Support Vector Machine model for hERG inhibitory activities based on the integrated hERG database using descriptor selection by NSGA-II
- Support Vector Machine model for hERG inhibitory activities based on the integrated hERG database using descriptor selection by NSGA-II
- Support Vector Machine model for hERG inhibitory activities based on the integrated hERG database using descriptor selection by NSGA-II
- Support Vector Machine model for hERG inhibitory activities based on the integrated hERG database using descriptor selection by NSGA-II
- Support Vector Machine model for hERG inhibitory activities based on the integrated hERG database using descriptor selection by NSGA-II
- Support Vector Machine model for hERG inhibitory activities based on the integrated hERG database using descriptor selection by NSGA-II
- Support Vector Machine model for hERG inhibitory activities based on the integrated hERG database using descriptor selection by NSGA-II
- Support Vector Machine model for hERG inhibitory activities based on the integrated hERG database using descriptor selection by NSGA-II
- Support Vector Machine model for hERG inhibitory activities based on the integrated hERG database using descriptor selection by NSGA-II
- Support Vector Machine model for hERG inhibitory activities based on the integrated hERG database using descriptor selection by NSGA-II
- Support Vector Machine model for hERG inhibitory activities based on the integrated hERG database using descriptor selection by NSGA-II
- Support Vector Machine model for hERG inhibitory activities based on the integrated hERG database using descriptor selection by NSGA-II
- Support Vector Machine model for hERG inhibitory activities based on the integrated hERG database using descriptor selection by NSGA-II
- Support Vector Machine model for hERG inhibitory activities based on the integrated hERG database using descriptor selection by NSGA-II
- Support Vector Machine model for hERG inhibitory activities based on the integrated hERG database using descriptor selection by NSGA-II
- Support Vector Machine model for hERG inhibitory activities based on the integrated hERG database using descriptor selection by NSGA-II
- Support Vector Machine model for hERG inhibitory activities based on the integrated hERG database using descriptor selection by NSGA-II
- Support Vector Machine model for hERG inhibitory activities based on the integrated hERG database using descriptor selection by NSGA-II
- Support Vector Machine model for hERG inhibitory activities based on the integrated hERG database using descriptor selection by NSGA-II
- Support Vector Machine model for hERG inhibitory activities based on the integrated hERG database using descriptor selection by NSGA-II
- Support Vector Machine model for hERG inhibitory activities based on the integrated hERG database using descriptor selection by NSGA-II
- Support Vector Machine model for hERG inhibitory activities based on the integrated hERG database using descriptor selection by NSGA-II
- Support Vector Machine model for hERG inhibitory activities based on the integrated hERG database using descriptor selection by NSGA-II
- DeepHIT: a deep learning framework for prediction of hERG-induced cardiotoxicity
- Support Vector Machine model for hERG inhibitory activities based on the integrated hERG database using descriptor selection by NSGA-II
- Support Vector Machine model for hERG inhibitory activities based on the integrated hERG database using descriptor selection by NSGA-II
- Support Vector Machine model for hERG inhibitory activities based on the integrated hERG database using descriptor selection by NSGA-II
- Support Vector Machine model for hERG inhibitory activities based on the integrated hERG database using descriptor selection by NSGA-II
- Support Vector Machine model for hERG inhibitory activities based on the integrated hERG database using descriptor selection by NSGA-II
- Support Vector Machine model for hERG inhibitory activities based on the integrated hERG database using descriptor selection by NSGA-II
- Support Vector Machine model for hERG inhibitory activities based on the integrated hERG database using descriptor selection by NSGA-II
- Support Vector Machine model for hERG inhibitory activities based on the integrated hERG database using descriptor selection by NSGA-II
- Support Vector Machine model for hERG inhibitory activities based on the integrated hERG database using descriptor selection by NSGA-II
- Support Vector Machine model for hERG inhibitory activities based on the integrated hERG database using descriptor selection by NSGA-II
- Support Vector Machine model for hERG inhibitory activities based on the integrated hERG database using descriptor selection by NSGA-II
- Support Vector Machine model for hERG inhibitory activities based on the integrated hERG database using descriptor selection by NSGA-II
- Support Vector Machine model for hERG inhibitory activities based on the integrated hERG database using descriptor selection by NSGA-II
- Support Vector Machine model for hERG inhibitory activities based on the integrated hERG database using descriptor selection by NSGA-II
- DeepHIT: a deep learning framework for prediction of hERG-induced cardiotoxicity
- Support Vector Machine model for hERG inhibitory activities based on the integrated hERG database using descriptor selection by NSGA-II
- Support Vector Machine model for hERG inhibitory activities based on the integrated hERG database using descriptor selection by NSGA-II
- Support Vector Machine model for hERG inhibitory activities based on the integrated hERG database using descriptor selection by NSGA-II
- Support Vector Machine model for hERG inhibitory activities based on the integrated hERG database using descriptor selection by NSGA-II
- Support Vector Machine model for hERG inhibitory activities based on the integrated hERG database using descriptor selection by NSGA-II
- Support Vector Machine model for hERG inhibitory activities based on the integrated hERG database using descriptor selection by NSGA-II
- Support Vector Machine model for hERG inhibitory activities based on the integrated hERG database using descriptor selection by NSGA-II
- Support Vector Machine model for hERG inhibitory activities based on the integrated hERG database using descriptor selection by NSGA-II
- Support Vector Machine model for hERG inhibitory activities based on the integrated hERG database using descriptor selection by NSGA-II
- Support Vector Machine model for hERG inhibitory activities based on the integrated hERG database using descriptor selection by NSGA-II
- Support Vector Machine model for hERG inhibitory activities based on the integrated hERG database using descriptor selection by NSGA-II
- Support Vector Machine model for hERG inhibitory activities based on the integrated hERG database using descriptor selection by NSGA-II
- Support Vector Machine model for hERG inhibitory activities based on the integrated hERG database using descriptor selection by NSGA-II
- Support Vector Machine model for hERG inhibitory activities based on the integrated hERG database using descriptor selection by NSGA-II
- Support Vector Machine model for hERG inhibitory activities based on the integrated hERG database using descriptor selection by NSGA-II
- Support Vector Machine model for hERG inhibitory activities based on the integrated hERG database using descriptor selection by NSGA-II
- Support Vector Machine model for hERG inhibitory activities based on the integrated hERG database using descriptor selection by NSGA-II
- Support Vector Machine model for hERG inhibitory activities based on the integrated hERG database using descriptor selection by NSGA-II
- Support Vector Machine model for hERG inhibitory activities based on the integrated hERG database using descriptor selection by NSGA-II
- Support Vector Machine model for hERG inhibitory activities based on the integrated hERG database using descriptor selection by NSGA-II
- Support Vector Machine model for hERG inhibitory activities based on the integrated hERG database using descriptor selection by NSGA-II
- Support Vector Machine model for hERG inhibitory activities based on the integrated hERG database using descriptor selection by NSGA-II
- Support Vector Machine model for hERG inhibitory activities based on the integrated hERG database using descriptor selection by NSGA-II
- Support Vector Machine model for hERG inhibitory activities based on the integrated hERG database using descriptor selection by NSGA-II
- Support Vector Machine model for hERG inhibitory activities based on the integrated hERG database using descriptor selection by NSGA-II
- Support Vector Machine model for hERG inhibitory activities based on the integrated hERG database using descriptor selection by NSGA-II
- Support Vector Machine model for hERG inhibitory activities based on the integrated hERG database using descriptor selection by NSGA-II
- Support Vector Machine model for hERG inhibitory activities based on the integrated hERG database using descriptor selection by NSGA-II
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Survey papers and books
- Critical assessment of AI in drug discovery
- Deep Learning for Molecules and Materials
- Defining and Exploring Chemical Spaces
- Learning Molecular Representations for Medicinal Chemistry
- Applications of Deep Learning in Molecule Generation and Molecular Property Prediction
- Transfer Learning for Drug Discovery
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Representation, transfer learning, and few-shot learning
- SELFIES and the future of molecular string representations
- Molecular Contrastive Learning of Representations via Graph Neural Networks
- ChemBERTa-2: Towards Chemical Foundation Models - loves-chemistry)]
- E(n) Equivariant Graph Neural Networks
- FS-Mol: A Few-Shot Learning Dataset of Molecules - Mol)]
- ATOM3D: Tasks On Molecules in Three Dimensions
- X-MOL: large-scale pre-training for molecular understanding and diverse molecular analysis - lab/X-MOL)]
- Do Transformers Really Perform Bad for Graph Representation? (Graphormer paper)
- Attention-Based Learning on Molecular Ensembles
- Inductive transfer learning for molecular activity prediction: Next-Gen QSAR Models with MolPMoFiT
- Molecule Attention Transformer
- - AI/meta-learning-qsar)]
- Self-Supervised Graph Transformer on Large-Scale Molecular Data (GROVER paper) - ailab/grover)]
- Strategies for Pre-training Graph Neural Networks - stanford/pretrain-gnns)]
- Analyzing Learned Molecular Representations for Property Prediction (Chemprop)
- PotentialNet for Molecular Property Prediction
- Low Data Drug Discovery with One-Shot Learning
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Generative algorithms
- Flow Network based Generative Models for Non-Iterative Diverse Candidate Generation
- Molecular generation by Fast Assembly of (Deep)SMILES fragments
- Amortized Tree Generation for Bottom-up Synthesis Planning and Synthesizable Molecular Design - gao/SynNet)]
- R-group replacement database for medicinal chemistry
- Deep Generative Models for 3D Linker Design
- Hierarchical Generation of Molecular Graphs using Structural Motifs - jin/hgraph2graph)]
- CReM: chemically reasonable mutations framework for structure generation
- GuacaMol: Benchmarking Models for de Novo Molecular Design
- Graph Convolutional Policy Network for Goal-Directed Molecular Graph Generation
- Optimization of Molecules via Deep Reinforcement Learning - research/google-research/tree/master/mol_dqn)] [[PyTorch implementation](https://github.com/aksub99/MolDQN-pytorch)]
- Junction Tree Variational Autoencoder for Molecular Graph Generation - jin/icml18-jtnn)]
- De Novo Design of Bioactive Small Molecules by Artificial Intelligence
- R-group replacement database for medicinal chemistry
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Synthetic accessability and retrosynthetic planning
- Data augmentation and pretraining for template-based retrosynthetic prediction in computer-aided synthesis planning
- Reinforcement Learning for Bioretrosynthesis
- Learning Graph Models for Retrosynthesis Prediction
- Retrosynthesis Prediction with Conditional Graph Logic Network - Dai/GLN)]
- SCScore: Synthetic Complexity Learned from a Reaction Corpus
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DNA-encoded libraries (DELs)
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Visualization and interpretability
- ChemInformatics Model Explorer (CIME): Exploratory analysis of chemical model explanations - vds-lab/cime)]
- Benchmarks for interpretation of QSAR models - lab-cz/ibenchmark)]
- Finding Constellations in Chemical Space Through Core Analysis
- Integrating the Structure–Activity Relationship Matrix Method with Molecular Grid Maps and Activity Landscape Models for Medicinal Chemistry Applications
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MS/MS prediction
- MassFormer: Tandem Mass Spectrum Prediction for Small Molecules using Graph Transformers
- Prefix-Tree Decoding for Predicting Mass Spectra from Molecules - pred)]
- 3DMolMS: prediction of tandem mass spectra from 3D molecular conformations
- CFM-ID 4.0: More Accurate ESI-MS/MS Spectral Prediction and Compound Identification
- Rapid Prediction of Electron–Ionization Mass Spectrometry Using Neural Networks - research/deep-molecular-massspec)]
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Data sets
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Frameworks, Libraries, and Software Tools
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Blogs
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MS/MS prediction
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Twitter
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Related lists
Programming Languages
Categories
Sub Categories
Hit finding and potency prediciton
143
ADME and toxicity prediction
77
MS/MS prediction
49
Representation, transfer learning, and few-shot learning
17
Generative algorithms
13
Survey papers and books
6
Synthetic accessability and retrosynthetic planning
5
Visualization and interpretability
4
DNA-encoded libraries (DELs)
2
Keywords
chemistry
3
drug-discovery
3
awesome-list
2
bioinformatics
2
cheminformatics
2
computational-chemistry
2
deep-learning
2
protein-structure
1
pdb-files
1
pdb
1
pandas-dataframe
1
molecules
1
molecule
1
molecular-structures
1
mol2
1
computational-biology
1
awesome
1
machine-learning
1
neural-networks
1
rdkit
1
recommender-system
1
explainable-ai
1
explainable-ml
1
graph
1
graph-algorithms
1
graphml
1
atomistic-simulations
1
molecular-dynamics
1
python-chemistry
1
quantum-chemistry
1
simulation
1