Cunliang Geng

SwiftMHC: A High-Speed Attention Network for MHC-Bound Peptide Identification and 3D Modeling

SwiftMHC rapidly predicts peptide-MHC binding and generates 3D structures, outperforming sequence-based methods in speed while maintaining accuracy.

Integrative modeling in the age of machine learning: a summary of HADDOCK strategies in CAPRI rounds 47-55

The paper summarizes HADDOCK's performance in CAPRI rounds 47-55, highlighting the effectiveness of integrating HADDOCK with AI-driven methods like AlphaFold2 for protein complex structure prediction.

MetaScore: A novel machine-learning based approach to improve traditional scoring functions for scoring protein-protein docking conformations

MetaScore improves traditional protein-protein docking scoring functions using machine learning, outperforming existing methods in success and hit rates.

DeepRank: A deep learning framework for data mining 3D protein-protein interfaces

DeepRank uses 3D CNNs to mine protein-protein interfaces, achieving competitive or better performance in classification and docking model ranking.

matchms - processing and similarity evaluation of mass spectrometry data

The paper introduces matchms, a tool for processing and evaluating similarity in mass spectrometry data, addressing challenges in dataset consistency and metadata quality.

iScore: an MPI supported software for ranking protein-protein docking models based on a random walk graph kernel and support vector machines.

The paper introduces iScore, a software tool that ranks protein-protein docking models using a random walk graph kernel and support vector machines, with MPI and GPU support for efficient computation.

An overview of data-driven HADDOCK strategies in CAPRI rounds 38-45.

The paper evaluates HADDOCK's performance in CAPRI rounds 38-45, showing it produces acceptable or better models for most targets using data-driven strategies.

iScore: A novel graph kernel-based function for scoring protein-protein docking models

iScore improves protein-protein docking model scoring by combining energy terms, graph representation, and evolutionary conservation, achieving top performance on benchmark datasets.

iSEE: Interface Structure, Evolution and Energy-based machine learning predictor of binding affinity changes upon mutations

iSEE predicts binding affinity changes upon mutations using a random forest model with 31 features, achieving high performance on diverse datasets.