Area of research
Molecular Biology · Computational Theory and Mathematics
Research interest
Research interests include Protein Structure and Dynamics, Computational Drug Discovery Methods, Enzyme Structure and Function, and vaccines and immunoinformatics approaches.
DeepRank-GNN: a graph neural network framework to learn patterns in protein–protein interfaces
PANDORA: A Fast, Anchor-Restrained Modelling Protocol for Peptide: MHC Complexes
DeepRank: a deep learning framework for data mining 3D protein-protein interfaces
Blind prediction of homo‐ and hetero‐protein complexes: The CASP13‐CAPRI experiment
Finding the ΔΔ<i>G</i> spot: Are predictors of binding affinity changes upon mutations in protein–protein interactions ready for it?
iScore: a novel graph kernel-based function for scoring protein–protein docking models
Large-scale prediction of binding affinity in protein–small ligand complexes: the PRODIGY-LIG web server
iSEE: Interface structure, evolution, and energy‐based machine learning predictor of binding affinity changes upon mutations
Protein–ligand pose and affinity prediction: Lessons from D3R Grand Challenge 3
Performance of HADDOCK and a simple contact-based protein–ligand binding affinity predictor in the D3R Grand Challenge 2
Sense and Simplicity in HADDOCK Scoring: Lessons from CASP‐CAPRI (page 418)
PRODIGY: a web server for predicting the binding affinity of protein–protein complexes
Prediction of homoprotein and heteroprotein complexes by protein docking and template‐based modeling: A CASP‐CAPRI experiment
Template-based protein–protein docking exploiting pairwise interfacial residue restraints
Computational prediction of protein interfaces: A review of data driven methods
RNABindRPlus: A Predictor that Combines Machine Learning and Sequence Homology-Based Methods to Improve the Reliability of Predicted RNA-Binding Residues in Proteins