Drug Discovery

386 papers with code • 28 benchmarks • 25 datasets

Drug discovery is the task of applying machine learning to discover new candidate drugs.

( Image credit: A Turing Test for Molecular Generators )


Use these libraries to find Drug Discovery models and implementations
3 papers
2 papers
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Most implemented papers

Semi-Supervised Classification with Graph Convolutional Networks

tkipf/pygcn 9 Sep 2016

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 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 17 Nov 2015

Graph-structured data appears frequently in domains including chemistry, natural language semantics, social networks, and knowledge bases.

Self-Normalizing Neural Networks

bioinf-jku/SNNs 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 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 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 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 19 Feb 2020

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

klicperajo/dimenet 28 Nov 2020

Many important tasks in chemistry revolve around molecules during reactions.

Generating Focussed Molecule Libraries for Drug Discovery with Recurrent Neural Networks

benevolentAI/guacamol_baselines 5 Jan 2017

In de novo drug design, computational strategies are used to generate novel molecules with good affinity to the desired biological target.