Simulating how atoms and molecules move over time is a central challenge in computational chemistry and materials science. Classical machine learning approaches to molecular dynamics (MD) encode ...

Five novel inhibitors of the MenT3 toxin were identified through a machine learning and simulations pipeline, offering new ...

An international team led by Einstein Professor Cecilia Clementi in the Department of Physics at Freie Universität Berlin has introduced CGSchNet, a machine-learned coarse-grained (CG) model that can ...

Researchers from BIFOLD and Google DeepMind have developed MD-ET, a transformer-based molecular dynamics model that achieves state-of-the-art results without encoding traditional physical constraints ...