I present an overview of the different ways machine learning is making an impact in molecular science. In particular I focus on theoretical and computational biophysics at the molecular scale, and how machine learning is revolutionizing molecular simulation techniques. I present some of the methods developed in the last few years, the results that have been obtained and the challenges ahead. I describe in some detail the application of machine learning to the development of molecular models for biological macromolecules at resolutions coarser than atomistic, that can accurately reproduce the behavior of the system as described by atomistic models or experimental measurements.