About

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

( Image credit: A Turing Test for Molecular Generators )

Benchmarks

TREND DATASET BEST METHOD PAPER TITLE PAPER CODE COMPARE

Datasets

Greatest papers with code

Self-Normalizing Neural Networks

NeurIPS 2017 bioinf-jku/SNNs

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

DRUG DISCOVERY PULSAR PREDICTION

Gated Graph Sequence Neural Networks

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

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

DRUG DISCOVERY GRAPH CLASSIFICATION NODE CLASSIFICATION SQL-TO-TEXT

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

29 Nov 2018molecularsets/moses

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

DRUG DISCOVERY

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

9 Dec 2019google/jax-md

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

DRUG DISCOVERY

An Overview of Multi-Task Learning in Deep Neural Networks

15 Jun 2017HazyResearch/metal

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 SPEECH RECOGNITION

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

19 Apr 2020kexinhuang12345/DeepPurpose

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

DRUG DISCOVERY

DeepDTA: Deep Drug-Target Binding Affinity Prediction

30 Jan 2018kexinhuang12345/DeepPurpose

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