Generative Models for Biomolecular Prediction, Dynamics, and Design

Abstract: We lay out the three avenues in which we think generative models are especially valuable for modeling biomolecules. 1) Hard prediction tasks can be better addressed with generative models that can suggest and rank multiple solutions (e.g. docking). 2) The dynamics and conformations of biomolecules can be captured with generative models (e.g. protein conformational ensembles and MD trajectories). 3) Designing new biomolecules can be accelerated, informed by samples or likelihoods from generative models (e.g. protein binder or regulatory DNA design).