A Deep Learning Toolkit for DTI, Drug Property, PPI, DDI, Protein Function Prediction (Bioinformatics)

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

Code for ICLR 2024 (Spotlight) paper "MAPE-PPI: Towards Effective and Efficient Protein-Protein Interaction Prediction via Microenvironment-Aware Protein Embedding"

EquiDock: geometric deep learning for fast rigid 3D protein-protein docking

Predicting direct protein-protein interactions with AlphaFold deep learning neural network models.

This repository has been integrated in https://github.com/DeepRank/deeprank2

[ICLR 2022] OntoProtein: Protein Pretraining With Gene Ontology Embedding

We would like to maintain a list of resources which aim to solve molecular docking and other closely related tasks.

Dataset and package for working with protein-protein interactions in 3D

Methods for the parallel and distributed analysis and mining of the Protein Data Bank using MMTF and Apache Spark.

SaprotHub: Making Protein Modeling Accessible to All Biologists

Structural Bioinformatics Training Workshop & Hackathon 2018

Explore the cancer relevance of your gene list

An ultra-high-performance protein-protein docking for heterogeneous supercomputers

Contact map analysis for biomolecules; based on MDTraj

Code for Pre-training Protein Encoder via Siamese Sequence-Structure Diffusion Trajectory Prediction (https://arxiv.org/abs/2301.12068)

Code for ICML 2024 paper "Learning to Predict Mutational Effects of Protein-Protein Interactions by Microenvironment-aware Hierarchical Prompt Learning"

Fast AlphaFold-Multimer based pipeline for Protein-Protein Interaction (PPI) screening

Predicting mutational effects on protein-protein binding via a side-chain diffusion probabilistic model (NeurIPS 2023 Poster)