Microsoft Researchers Propose DiG: Transforming Molecular Modeling with Deep Learning for Equilibrium Distribution Prediction

DiG is a deep learning framework that predicts equilibrium distributions of molecular systems, enabling diverse molecular sampling and accelerating the discovery of molecular structures. It can be applied to various molecular modeling and design challenges, such as protein conformation sampling, ligand structure sampling, and catalyst-adsorbate sampling.