Vijay S. Pande

The paper introduces a method to model IDP dynamics using MSMs with per-residue secondary structure features, demonstrating its application to p53-CTD and revealing its conformational landscape as a network of transient structures.

The paper uses kinetic machine learning to analyze molecular dynamics simulations of the μ opioid receptor, identifying new metastable states and ligand-directed conformational changes.

This paper introduces a unifying method called landmark kernel tICA (lktICA) for analyzing conformational dynamics, showing improved projections in three test cases.

The paper challenges the assumption that energy conservation is a reliable measure of molecular simulation accuracy, showing that energy drift is not linear in simulation length, temperature, or system size.

The paper identifies sixteen allosteric lead ligands of c-Src kinase using MD simulations, Markov State Models, and ligand screening from the ZINC database.

The paper identifies significantly mutated regions (SMRs) across cancer genomes to uncover functional molecular alterations, revealing diverse mutation patterns and their associations with gene expression and protein domains.

The paper introduces two optimized matrix multiply routines for calculating protein RMSD, achieving up to 3X speed improvement over existing libraries.