Construction of a Deep Neural Network Energy Function for Protein Physics.

ABSTRACT: The traditional approach of computational biology consists of calculating molecule properties by using approximate classical potentials. Interactions between atoms are described by an energy function derived from physical principles or fitted to experimental data. Their functional form is usually limited to pairwise interactions between atoms and does not consider complex multibody effects. More recently, neural networks have emerged as an alternative way of describing the interactions between biomolecules. In this approach, the energy function does not have an explicit functional form and is learned bottom-up from simulations at the atomistic or quantum level. In this study, we attempt a top-down approach and use deep learning methods to obtain an energy function by exploiting the large a

SUBMITTER: Yang H 

PROVIDER: S-EPMC9476656 | biostudies-literature | 2022 Sep

REPOSITORIES: biostudies-literature