Drug Discovery
375 papers with code • 28 benchmarks • 24 datasets
Drug discovery is the task of applying machine learning to discover new candidate drugs.
( Image credit: A Turing Test for Molecular Generators )
Benchmarks
These leaderboards are used to track progress in Drug Discovery
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Libraries
Use these libraries to find Drug Discovery models and implementationsDatasets
QM9
Tox21
BACE (β-secretase enzyme)
SIDER
QED
BindingDB
Most implemented papers
Semi-Supervised Classification with Graph Convolutional Networks
tkipf/pygcn
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We present a scalable approach for semi-supervised learning on graph-structured data that is based on an efficient variant of convolutional neural networks which operate directly on graphs.
Neural Message Passing for Quantum Chemistry
brain-research/mpnn
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ICML 2017
Supervised learning on molecules has incredible potential to be useful in chemistry, drug discovery, and materials science.
Gated Graph Sequence Neural Networks
dmlc/dgl
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Graph-structured data appears frequently in domains including chemistry, natural language semantics, social networks, and knowledge bases.
Self-Normalizing Neural Networks
bioinf-jku/SNNs
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NeurIPS 2017
We introduce self-normalizing neural networks (SNNs) to enable high-level abstract representations.
Junction Tree Variational Autoencoder for Molecular Graph Generation
wengong-jin/icml18-jtnn
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ICML 2018
We evaluate our model on multiple tasks ranging from molecular generation to optimization.
Convolutional Networks on Graphs for Learning Molecular Fingerprints
HIPS/neural-fingerprint
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NeurIPS 2015
We introduce a convolutional neural network that operates directly on graphs.
PhysNet: A Neural Network for Predicting Energies, Forces, Dipole Moments and Partial Charges
MeuwlyGroup/PhysNet
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J. Chem. Theory Comput. 2019
Further, two new datasets are generated in order to probe the performance of ML models for describing chemical reactions, long-range interactions, and condensed phase systems.
Molecule Attention Transformer
gmum/MAT
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Designing a single neural network architecture that performs competitively across a range of molecule property prediction tasks remains largely an open challenge, and its solution may unlock a widespread use of deep learning in the drug discovery industry.
Fast and Uncertainty-Aware Directional Message Passing for Non-Equilibrium Molecules
Many important tasks in chemistry revolve around molecules during reactions.
Generating Focussed Molecule Libraries for Drug Discovery with Recurrent Neural Networks
benevolentAI/guacamol_baselines
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In de novo drug design, computational strategies are used to generate novel molecules with good affinity to the desired biological target.
