Nicolas Renaud

This paper improves MHC-I binding peptide prediction generalizability using geometric deep learning, outperforming sequence-based methods without binding affinity data.

DeepRank-GNN uses graph neural networks to score protein-protein docking models, outperforming existing methods and improving speed and storage efficiency.

PANDORA is a fast pMHC structure modelling pipeline that uses anchor restraints to generate accurate models with high success rate and speed.

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

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.

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