rociomer

PlotNeuralNet

Latex code for making neural networks diagrams

deepmind-research

This repository contains implementations and illustrative code to accompany DeepMind publications

NN-SVG

Publication-ready NN-architecture schematics.

awesome-network-embedding

A curated list of network embedding techniques.

maml

Code for "Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks"

Graphormer

Graphormer is a general-purpose deep learning backbone for molecular modeling.

awesome-explainable-graph-reasoning

A collection of research papers and software related to explainability in graph machine learning.

noah-research

Noah Research

maml_rl

Code for RL experiments in "Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks"

leo

Implementation of Meta-Learning with Latent Embedding Optimization

hypergrad

Exploring differentiation with respect to hyperparameters

exmol

Explainer for black box models that predict molecule properties

jax2torch

Use Jax functions in Pytorch

awesome-drug-discovery-knowledge-graphs

A collection of research papers, datasets and software related to knowledge graphs for drug discovery. Accompanies the paper "A review of biomedical datasets relating to drug discovery: a knowledge graph perspective" (Briefings in Bioinformatics, 2022)

MatDeepLearn

MatDeepLearn, package for graph neural networks in materials chemistry

FS-Mol

FS-Mol is A Few-Shot Learning Dataset of Molecules, containing molecular compounds with measurements of activity against a variety of protein targets. The dataset is presented with a model evaluation benchmark which aims to drive few-shot learning research in the domain of molecules and graph-structured data.

pyg_autoscale

Implementation of "GNNAutoScale: Scalable and Expressive Graph Neural Networks via Historical Embeddings" in PyTorch

dockstring

A Python package for molecular docking with an extensive, highly-curated dataset and a set of realistic benchmark tasks for drug discovery.

3DInfomax

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

GraphiT

Official Pytorch Implementation of GraphiT

RL-GraphINVENT

RL-GraphINVENT is a platform for graph-based targeted molecular generation using graph neural networks and reinforcement learning. RL-GraphINVENT uses a Gated Graph Neural Network -based model fine-tuned using reinforcement learning to probabilistically generate new molecules with desired property profiles.

ChIRo

Public Implementation of ChIRo from "Learning 3D Representations of Molecular Chirality with Invariance to Bond Rotations"

caviar

// PROJECT PAUSED FOR NOW (lack of capacity) // Protein cavity identification and automatic subpocket decomposition

molfunc

fast functionalisation of molecules

Autobahn

Repository for Autobahn: Automorphism Based Graph Neural Networks

Supramolecular_VAE

route-distances

Tools and routines to calculate distances between synthesis routes and to cluster them.

reptile-torch

PyTorch Implementation of Reptile

MMAML-rl

surge

A Fast Chemical Graph Generator