OpenMM is a toolkit for molecular simulation using high performance GPU code.

Leveraging Structural Priors and Constraints for Cryo-EM Heterogeneous Reconstruction

AlphaFold 3 inference pipeline.

A trainable PyTorch reproduction of AlphaFold 3.

Code for the paper "Topological Autoencoders" by Michael Moor, Max Horn, Bastian Rieck, and Karsten Borgwardt.

A curated list of topological deep learning (TDL) resources and links.

Code for the NeurIPS 2021 paper "Higher Order Kernel Mean Embeddings to Capture Filtrations of Stochastic Processes".

Comprehensive benchmarking of protein-ligand structure generation methods (ICML 2024 AI4Science)

🏡 GitHub Pages template for personal academic homepage

Message Passing Neural Networks for Simplicial and Cell Complexes

Topological Neural Networks go Persistent, Equivariant and Continuous

Simplicial neural networks (SNNs), a generalization of graph neural networks to data that live on a class of topological spaces called simplicial complexes.

Ripser: efficient computation of Vietoris–Rips persistence barcodes

This repo includes Claude prompt curation to use Claude better.

Code for "Log Neural Controlled Differential Equations: The Lie Brackets Make a Difference" (ICML 2024)

RevIN: Reversible Instance Normalization For Accurate Time-series Forecasting Against Distribution Shift

An open-source remote desktop application designed for self-hosting, as an alternative to TeamViewer.

The AI developer platform. Use Weights & Biases to train and fine-tune models, and manage models from experimentation to production.

MD trajectory analysis using protein-ligand Interaction Fingerprints

From Proteins to Ligands: Decoding Deep Learning Methods for Binding Affinity Prediction

Masif seed paper repository

Codebase for PINNacle: A Comprehensive Benchmark of Physics-Informed Neural Networks for Solving PDEs.

This project aim to reproduce Sora (Open AI T2V model), we wish the open source community contribute to this project.

a molecular descriptor calculator

A PyTorch implementation of "Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks" (KDD 2019).