Drug Discovery

151 papers with code • 14 benchmarks • 5 datasets

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

( Image credit: A Turing Test for Molecular Generators )

Greatest papers with code

Self-Normalizing Neural Networks

bioinf-jku/SNNs NeurIPS 2017

We introduce self-normalizing neural networks (SNNs) to enable high-level abstract representations.

Drug Discovery Pulsar Prediction

Neural Message Passing for Quantum Chemistry

Microsoft/gated-graph-neural-network-samples ICML 2017

Supervised learning on molecules has incredible potential to be useful in chemistry, drug discovery, and materials science.

Drug Discovery Formation Energy +3

Gated Graph Sequence Neural Networks

Microsoft/gated-graph-neural-network-samples 17 Nov 2015

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

Drug Discovery Graph Classification +2

JAX, M.D.: A Framework for Differentiable Physics

google/jax-md 9 Dec 2019

We introduce JAX MD, a software package for performing differentiable physics simulations with a focus on molecular dynamics.

Drug Discovery

Molecular Sets (MOSES): A Benchmarking Platform for Molecular Generation Models

molecularsets/moses 29 Nov 2018

Generative models are becoming a tool of choice for exploring the molecular space.

Drug Discovery

DeepPurpose: a Deep Learning Library for Drug-Target Interaction Prediction

kexinhuang12345/DeepPurpose 19 Apr 2020

Accurate prediction of drug-target interactions (DTI) is crucial for drug discovery.

Drug Discovery

DeepDTA: Deep Drug-Target Binding Affinity Prediction

kexinhuang12345/DeepPurpose 30 Jan 2018

The results show that the proposed deep learning based model that uses the 1D representations of targets and drugs is an effective approach for drug target binding affinity prediction.

Drug Discovery

Therapeutics Data Commons: Machine Learning Datasets and Tasks for Drug Discovery and Development

mims-harvard/TDC 18 Feb 2021

Here, we introduce Therapeutics Data Commons (TDC), the first unifying platform to systematically access and evaluate machine learning across the entire range of therapeutics.

Drug Discovery

An Overview of Multi-Task Learning in Deep Neural Networks

HazyResearch/metal 15 Jun 2017

Multi-task learning (MTL) has led to successes in many applications of machine learning, from natural language processing and speech recognition to computer vision and drug discovery.

Drug Discovery Multi-Task Learning +1