Sidechain conditioning and modeling for full-atom protein sequence design

Plugin for folding sequences directly in PyMOL

Multiple sequence alignment visualizer

JavaScript implementation of UMAP

Library for computing dynamic non-covalent contact networks in proteins throughout MD Simulation

python tools for TCR:peptide-MHC modeling and analysis

Fraction of Common Contacts Clustering Algorithm for Protein Structures

AlphaLink2: Integrating crosslinking MS data into Uni-Fold-Multimer

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

A geometric deep learning pipeline for predicting protein interface contacts. (ICLR 2022)

List of molecules (small molecules, RNA, peptide, protein, enzymes, antibody, and PPIs) conformations and molecular dynamics (force fields) using generative artificial intelligence and deep learning

📐 Symmetry-corrected RMSD in Python

Benchmarking framework for protein representation learning. Includes a large number of pre-training and downstream task datasets, models and training/task utilities. (ICLR 2024)

MaSIF- Molecular surface interaction fingerprints. Geometric deep learning to decipher patterns in molecular surfaces.

Molecular dynamics without molecules: searching the conformational space of proteins with generative neural networks

Exploring the conformational ensembles of protein-protein complexes with transformer-based generative neural networks

Optimizing AlphaFold Training and Inference on GPU Clusters

Coarse-grained molecular dynamics for protein physics. NOTE: this has been superseded by Upside 2: https://github.com/sosnicklab/upside2-md

List of papers about Proteins Design using Deep Learning

A simple cross attention that updates both the source and target in one step

Benchmarks of approximate nearest neighbor libraries in Python

Force Fields

Predict the binding affinity of protein-protein complexes from structural data