MACHINE LEARNING FOR SIMULATIONS IN BIOLOGY AND CHEMISTRY

Machine learning has revolutionised biology and chemistry, yet many models lack physical rigour, relying on static structures that miss essential dynamics. First-principles methods like molecular dynamics offer accuracy and physical grounding, but remain too costly for large or complex systems.

Simbiochem seeks to bridge that divide by bringing together researchers across machine learning, computational chemistry, biophysics, life and materials science. Our goal is to integrate scalable ML with rigorous physical simulation — making methods not only faster, but also more accurate, reliable, and grounded in science.