249 papers with code • 27 benchmarks • 20 datasets
Drug discovery is the task of applying machine learning to discover new candidate drugs.
( Image credit: A Turing Test for Molecular Generators )
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Most implemented papers
Semi-Supervised Classification with Graph Convolutional Networks
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
Supervised learning on molecules has incredible potential to be useful in chemistry, drug discovery, and materials science.
Gated Graph Sequence Neural Networks
Graph-structured data appears frequently in domains including chemistry, natural language semantics, social networks, and knowledge bases.
Junction Tree Variational Autoencoder for Molecular Graph Generation
We evaluate our model on multiple tasks ranging from molecular generation to optimization.
Self-Normalizing Neural Networks
We introduce self-normalizing neural networks (SNNs) to enable high-level abstract representations.
Convolutional Networks on Graphs for Learning Molecular Fingerprints
We introduce a convolutional neural network that operates directly on graphs.
PhysNet: A Neural Network for Predicting Energies, Forces, Dipole Moments and Partial Charges
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
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.
Generating Focussed Molecule Libraries for Drug Discovery with Recurrent Neural Networks
In de novo drug design, computational strategies are used to generate novel molecules with good affinity to the desired biological target.
Directional Message Passing for Molecular Graphs
Each message is associated with a direction in coordinate space.