Browse SoTA > Medical > Drug Discovery

Drug Discovery

85 papers with code · Medical

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

( Image credit: Neural Graph Fingerprints )

Benchmarks

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 the tools of choice for the discovery of new molecules and materials.

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

JAX, M.D.: End-to-End Differentiable, Hardware Accelerated, Molecular Dynamics in Pure Python

9 Dec 2019google/jax-md

Finally, since all of the simulation code is written in Python, researchers can have unprecedented flexibility in setting up experiments without having to edit any low-level C++ or CUDA code.

DRUG DISCOVERY

Graph Networks as a Universal Machine Learning Framework for Molecules and Crystals

Chem. Mater. 2018 materialsvirtuallab/megnet

Similarly, we show that MEGNet models trained on $\sim 60, 000$ crystals in the Materials Project substantially outperform prior ML models in the prediction of the formation energies, band gaps and elastic moduli of crystals, achieving better than DFT accuracy over a much larger data set.

DRUG DISCOVERY FORMATION ENERGY

DeepPurpose: a Deep Learning Library for Drug-Target Interaction Prediction and Applications to Repurposing and Screening

19 Apr 2020kexinhuang12345/DeepPurpose

The unique feature of DeepPurpose is that it enables non-computational drug development scientists to identify drug candidates based on five pre-trained DL models with only a few lines of codes.

DRUG DISCOVERY