Geometric deep learning for molecular property predictions with chemical accuracy across chemical space.

ABSTRACT: Chemical engineers heavily rely on precise knowledge of physicochemical properties to model chemical processes. Despite the growing popularity of deep learning, it is only rarely applied for property prediction due to data scarcity and limited accuracy for compounds in industrially-relevant areas of the chemical space. Herein, we present a geometric deep learning framework for predicting gas- and liquid-phase properties based on novel quantum chemical datasets comprising 124,000 molecules. Our findings reveal that the necessity for quantum-chemical information in deep learning models varies significantly depending on the modeled physicochemical property. Specifically, our top-performing geometric model meets the most stringent criteria for "chemically accurate" thermochemistry predictions.

SUBMITTER: Dobbelaere MR 

PROVIDER: S-EPMC11323398 | biostudies-literature | 2024 Aug

REPOSITORIES: biostudies-literature