QuADMET-Former: Pre-training on Quantum Mechanical Properties Improves ADMET Prediction

Sort & Slice: A Simple and Superior Alternative to Hash-Based Folding for Extended-Connectivity Fingerprints (ECFPs)

PyTorch Lightning + Hydra. A very user-friendly template for ML experimentation. ⚡🔥⚡

Molecular Hypergraph Neural Network

[TPAMI 2022] "Bag of Tricks for Training Deeper Graph Neural Networks A Comprehensive Benchmark Study" by Tianlong Chen*, Kaixiong Zhou*, Keyu Duan, Wenqing Zheng, Peihao Wang, Xia Hu, Zhangyang Wang

DeepErwin is a python 3.8+ package that implements and optimizes JAX 2.x wave function models for numerical solutions to the multi-electron Schrödinger equation. DeepErwin supports weight-sharing w…

Repository associated to the paper: "Explaining the Explainers in Graph Neural Networks: a Comparative Study"

Pretrained universal neural network potential for charge-informed atomistic modeling https://chgnet.lbl.gov

Graph neural networks for molecular design.

Implementation of Geometric Vector Perceptron, a simple circuit for 3d rotation equivariance for learning over large biomolecules, in Pytorch. Idea proposed and accepted at ICLR 2021

Training neural network potentials

Graph Transformer Architecture. Source code for "A Generalization of Transformer Networks to Graphs", DLG-AAAI'21.

Recipe for a General, Powerful, Scalable Graph Transformer

Graphium: Scaling molecular GNNs to infinity.

Artificial Intelligence Research for Science (AIRS)

Fast calculation of hydrogen-bond strengths and free energy of hydration of small molecules.

Papers about graph transformers.

Molecular optimization by capturing chemist’s intuition using the Seq2Seq with attention and the Transformer

Course to get into Large Language Models (LLMs) with roadmaps and Colab notebooks.

A collection of papers studying/improving the expressiveness of graph neural networks (GNNs)

Topological Deep Learning

Capturing Graphs with Hypo-Elliptic Diffusions

gRNAde is a Generative AI framework for inverse design of 3D RNA structure and function

Official repository for the paper "Improving Graph Neural Network Expressivity via Subgraph Isomorphism Counting" (TPAMI'22) https://arxiv.org/abs/2006.09252

Code to analyze SAR datasets for Nonadditivity

NequIP is a code for building E(3)-equivariant interatomic potentials

Must-read papers on graph neural networks (GNN)