medical-9

Drug Discovery

3260 papers • 126 benchmarks • 313 datasets

Drug discovery is the task of applying machine learning to discover new candidate drugs. ( Image credit: A Turing Test for Molecular Generators )

(Image credit: Papersgraph)

Benchmarks

These leaderboards are used to track progress in drug-discovery-9

Trend

Dataset

Best Model

Actions

Tox21

QM9

BACE

Libraries

i

Use these libraries to find drug-discovery-9 models and implementations

nrel/m2p

3 papers 22

Datasets

QM9

Tox21

BACE (β-secretase enzyme)

BBBP (Blood-Brain Barrier Penetration)

SIDER

HIV (Human Immunodeficiency Virus)

Subtasks

Drug Response Prediction Text-based de novo Molecule Generation

Most implemented papers

Semi-Supervised Classification with Graph Convolutional Networks

M. Welling, Thomas Kipf•Thu Sep 08 2016

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 which outperforms related methods by a significant margin.

33366

Content

ToxCast

MUV

HIV dataset

SIDER

BBBP

BindingDB

LIT-PCBA(MAPK1)

LIT-PCBA(ALDH1)

LIT-PCBA(KAT2A)

DAVIS-DTA

KIBA

ClinTox

LIT-PCBA(ESR1_ant)

PCBA

BindingDB IC50

egfr-inh

QED

DRD2

PDBbind

ESOL (Estimated SOLubility)

Lipophilicity (logd74)

FreeSolv (Free Solvation)

BACE (β-secretase enzyme)

BBBP (Blood-Brain Barrier Penetration)

ToxCast (Toxicity Forecaster)

divelab/DIG

2 papers 1,772

kexinhuang12345/DeepPurpose

2 papers 909

PaddlePaddle/PaddleHelix

2 papers 787

gnina/gnina

2 papers 552

mattragoza/liGAN

2 papers 204

QED

clintox

ToxCast (Toxicity Forecaster)

BindingDB

0

Paper Graph

Neural Message Passing for Quantum Chemistry

O. Vinyals, J. Gilmer, George E. Dahl, S. Schoenholz, Patrick F. Riley•Mon Apr 03 2017

Using MPNNs, state of the art results on an important molecular property prediction benchmark are demonstrated and it is believed future work should focus on datasets with larger molecules or more accurate ground truth labels.

8540 0

Paper Graph

Self-Normalizing Neural Networks

Thomas Unterthiner, Sepp Hochreiter, Andreas Mayr, G. Klambauer•Wed Jun 07 2017

Self-normalizing neural networks (SNNs) are introduced to enable high-level abstract representations and it is proved that activations close to zero mean and unit variance that are propagated through many network layers will converge towards zero meanand unit variance -- even under the presence of noise and perturbations.

2722 0

Paper Graph

Gated Graph Sequence Neural Networks

R. Zemel, Yujia Li, Marc Brockschmidt, Daniel Tarlow•Mon Nov 16 2015

This work studies feature learning techniques for graph-structured inputs and achieves state-of-the-art performance on a problem from program verification, in which subgraphs need to be matched to abstract data structures.

3533 0

Paper Graph

Junction Tree Variational Autoencoder for Molecular Graph Generation

Wengong Jin, T. Jaakkola, R. Barzilay•Sun Feb 11 2018

The junction tree variational autoencoder generates molecular graphs in two phases, by first generating a tree-structured scaffold over chemical substructures, and then combining them into a molecule with a graph message passing network, which allows for incrementally expand molecules while maintaining chemical validity at every step.

1552 0

Paper Graph

Convolutional Networks on Graphs for Learning Molecular Fingerprints

Alán Aspuru-Guzik, Ryan P. Adams, D. Duvenaud, D. Maclaurin, J. Aguilera-Iparraguirre, Rafael Gómez-Bombarelli, Timothy D. Hirzel•Tue Sep 29 2015

A convolutional neural network that operates directly on graphs that allows end-to-end learning of prediction pipelines whose inputs are graphs of arbitrary size and shape is introduced.

3557 0

Paper Graph

Molecule Attention Transformer

Stanislaw Jastrzebski, J. Tabor, Lukasz Maziarka, Tomasz Danel, Slawomir Mucha, Krzysztof Rataj•Tue Feb 18 2020

This work proposes Molecule Attention Transformer, a key innovation is to augment the attention mechanism in Transformer using inter-atomic distances and the molecular graph structure, and shows that attention weights learned are interpretable from the chemical point of view.

194 0

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Fast and Uncertainty-Aware Directional Message Passing for Non-Equilibrium Molecules

Johannes Klicpera, Stephan Gunnemann, Shankari Giri, Johannes T. Margraf•Fri Nov 27 2020

The DimeNet++ model is proposed, which is 8x faster and 10% more accurate than the original Dime net on the QM9 benchmark of equilibrium molecules, and ensembling and mean-variance estimation for uncertainty quantification are investigated with the goal of accelerating the exploration of the vast space of non-equilibrium structures.

387 0

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Generating Focussed Molecule Libraries for Drug Discovery with Recurrent Neural Networks

Marwin H. S. Segler, T. Kogej, C. Tyrchan, M. Waller•Wed Jan 04 2017

This work shows that recurrent neural networks can be trained as generative models for molecular structures, similar to statistical language models in natural language processing, and demonstrates that the properties of the generated molecules correlate very well with those of the molecules used to train the model.

98 0

Paper Graph

Adding a benchmark result helps the community track progress.