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3dinfomax

Making self-supervised learning work on molecules by using their 3D geometry to pre-train GNNs. Implemented in DGL and Pytorch Geometric.

abt-mpnn

An atom-bond transformer-based message passing neural network for molecular property prediction.

annotated_deep_learning_paper_implementations

🧑‍🏫 59 Implementations/tutorials of deep learning papers with side-by-side notes 📝; including transformers (original, xl, switch, feedback, vit, ...), optimizers (adam, adabelief, ...), gans(cyclegan, stylegan2, ...), 🎮 reinforcement learning (ppo, dqn), capsnet, distillation, ... 🧠

awesome-graph-level-learning

Awesome graph-level learning methods. Collections of commonly used datasets, papers as well as implementations are listed in this github repository. We also invite researchers interested in graph representation learning, graph regression and graph classification to join this project as contribut…

chemplot

A python package for chemical space visualization.

chemprop

Message Passing Neural Networks for Molecule Property Prediction

chemprop-ir

deepmol

DeepMol: a python-based machine and deep learning framework for drug discovery

drugai

Graph Neural Networks for Drug Efficacy Prediction

egat

a pytorch implementation of Exploiting Edge Features in Graph Neural Networks.

exmol

Explainer for black box models that predict molecule properties

gnn-project

A Graph Neural Network project on HIV data

graph-neural-networks-for-drug-discovery

graph-representations

Comparing graph representations for molecular features prediction

graph_nets

PyTorch Implementation and Explanation of Graph Representation Learning papers: DeepWalk, GCN, GraphSAGE, ChebNet & GAT.

graphormer

Graphormer is a deep learning package that allows researchers and developers to train custom models for molecule modeling tasks. It aims to accelerate the research and application in AI for molecule science, such as material design, drug discovery, etc.

hignn

Code for "HiGNN: A Hierarchical Informative Graph Neural Network for Molecular Property Prediction Equipped with Feature-Wise Attention"

machine-learning-for-proteins

Listing of papers about machine learning for proteins.

mpnn

Message-Passing Neural Network for Chemistry

nmp_qc

Our own implementation of Neural Message Passing for Computer Vision paper

olfactoryspace

This repository will houses data and code used to build odorous/odorless classification models, draw borders of olfactory space, and estimate the number of possible odorants.

probing-graph-transformers

Code for our paper "Attending to Graph Transformers"

proteinmpnn

Code for the ProteinMPNN paper

pygat

Pytorch implementation of the Graph Attention Network model by Veličković et. al (2017, https://arxiv.org/abs/1710.10903)

pygcn

Graph Convolutional Networks in PyTorch

receptor2odorant

Implementation of paper "Matching receptor to odorant with protein language and graph neural networks"

sampn

san

scent-gnn

Classifying and understanding molecule scent with GNN.

specformer

Transformer-based Spectral Graph Neural Networks