Official git repository for Biopython (originally converted from CVS)

Trainable, memory-efficient, and GPU-friendly PyTorch reproduction of AlphaFold 2

Making Protein folding accessible to all!

Toolbox for molecular animations in Blender, powered by Geometry Nodes.

Foldseek enables fast and sensitive comparisons of large structure sets.

Protein Graph Library

Standardized data set for machine learning of protein structure

Working with molecular structures in pandas DataFrames

Tasks Assessing Protein Embeddings (TAPE), a set of five biologically relevant semi-supervised learning tasks spread across different domains of protein biology.

Protein-Ligand Interaction Profiler - Analyze and visualize non-covalent protein-ligand interactions in PDB files according to 📝 Schake, Bolz, et al. (2025), https://doi.org/10.1093/nar/gkaf361

:book::microscope::coffee: BioJava is an open-source project dedicated to providing a Java library for processing biological data.

Remote protein homology detection suite.

Optimizing AlphaFold Training and Inference on GPU Clusters

Saprot: Protein Language Model with Structural Alphabet (AA+3Di)

EquiBind: geometric deep learning for fast predictions of the 3D structure in which a small molecule binds to a protein

Get protein embeddings from protein sequences

Molecular simulation in Julia

An open-source platform for developing protein models beyond AlphaFold.

P2Rank: Protein-ligand binding site prediction from protein structure based on machine learning.

The Rosetta Bio-macromolecule modeling package. Available through license with the University of Washington.

Protein-protein, protein-peptide and protein-DNA docking framework based on the GSO algorithm

An all-atom protein structure dataset for machine learning.

PROPKA predicts the pKa values of ionizable groups in proteins and protein-ligand complexes based in the 3D structure.

A Python API for the RCSB Protein Data Bank (PDB)

macromolecular crystallography library and utilities

Recurrent Geometric Networks for end-to-end differentiable learning of protein structure

A collection of research papers for AI-based protein design

Jupyter Dock is a set of Jupyter Notebooks for performing molecular docking protocols interactively, as well as visualizing, converting file formats and analyzing the results.

Versatile computational pipeline for processing protein structure data for deep learning applications.

Multiple sequence and structure alignment with top benchmark scores scalable to thousands of sequences. Generates replicate alignments, enabling assessment of downstream analyses such as trees and predicted structures.