Machine Learning for Biomolecular Modeling

The staggering complexity of biological systems has made them one of the earlier fields of applications of data-driven modeling techniques. From bioinformatics, to the automatic construction of collective variables to describe rare events, machine learning has been part of biomolecular modeling for a longer time than in other fields of chemical physics. Yet, more exciting examples of the potential of artificial intelligence for the study of biomolecules appear every day - from the prediction of protein folding based on sequence information, to the screening of drug candidates for emerging diseases.

This special issue welcomes contributions that highlight the accomplishments, the promise and the challenges ahead for the use of machine-learning techniques in the field of biomolecular simulations, covering both the development of novel methodologies and the application to problems of fundamental and practical significance.

Guest Editors: Pratyush Tiwary, Francesca Grisoni, Pilar Cossio, with JCP Editor Michele Ceriotti.