Graph Network for protein-protein interface

A geometric deep learning framework (Geometric Transformers) for predicting protein interface contacts. (ICLR 2022)

Learning to design protein-protein interactions with enhanced generalization (ICLR 2024)

Code repository for the SPOC tool for scoring binary AF-M predicitons.

E(3) equivariant graph neural network for PPI site prediction

Evolved25 AIxBio Global Sprint Hackathon (Fastfold AI Team)

🧬 Integrates AlphaFold2/3 model confidence with ⚡ pyDock energy scoring to enhance protein–protein complex prediction accuracy. 📦 Includes workflows for generating diverse AF2-Multimer models, 🔍 computing pyDock energies, and 🔗 combining both via z-score normalization to produce robust, prioritized complex structures.

Interactive visualizations for protein complexes from hu.MAP 1.0 and hu.MAP 2.0 by Super.Complex and CORUM

Community-Maintained Version of mordred (Quantitative Estimate Index for Early-Stage Screening of Compounds Targeting Protein-Protein Interactions)

Open hotspot-guided de novo protein binder design pipeline integrating OpenMM, BindCraft, ProteinMPNN, and AlphaFold2.

Structural Interactome pipeline

Python package to analyse binding kinetics data with simple models

Convert PPIs in HIPPIE to BEL

Interactive visualizations for protein complexes from hu.MAP 1.0 and hu.MAP 2.0 with RL

A computational biology project analyzing Protein-Protein Interaction (PPI) networks using Graph Theory and NetworkX. Features centrality analysis, shortest path discovery, and network visualization to identify key biological hubs.

Analyzing biological networks using statistical testing to uncover significant differences in protein distributions based on functional relationships.

Incorporating Triplet Error for Predicting PPIs using Deep Learning

Tools for processing and analysing PPI datasets