Molecular Geometry Prediction using a Deep Generative Graph Neural Network.

ABSTRACT: A molecule's geometry, also known as conformation, is one of a molecule's most important properties, determining the reactions it participates in, the bonds it forms, and the interactions it has with other molecules. Conventional conformation generation methods minimize hand-designed molecular force field energy functions that are often not well correlated with the true energy function of a molecule observed in nature. They generate geometrically diverse sets of conformations, some of which are very similar to the lowest-energy conformations and others of which are very different. In this paper, we propose a conditional deep generative graph neural network that learns an energy function by directly learning to generate molecular conformations that are energetically favorable and more likel

SUBMITTER: Mansimov E 

PROVIDER: S-EPMC6938476 | biostudies-literature | 2019 Dec

REPOSITORIES: biostudies-literature