Samuel Murail

This paper evaluates limitations of deep learning in biomolecular structure prediction, highlighting the benefits of explicit ion modeling for improving conformational accuracy.

This paper introduces SST2, a new enhanced sampling method for molecular dynamics simulations that improves efficiency for large biomolecular systems by selectively scaling interactions within and around the molecule.

The paper explores cyclic peptide conformations using multiple molecular dynamics protocols and force fields, finding that combining implicit solvent simulations improves native structure prediction.

The paper introduces a framework for rational head-to-tail peptide cyclization using protein structure data, demonstrating improved conformation prediction and successful application to Nrf2 and urotensin II peptides.