Organic crystal structure prediction via coupled generative adversarial networks and graph convolutional networks.

ABSTRACT: Organic crystal structures exert a profound impact on the physicochemical properties and biological effects of organic compounds. Quantum mechanics (QM)-based crystal structure predictions (CSPs) have somewhat alleviated the dilemma that experimental crystal structure investigations struggle to conduct complete polymorphism studies, but the high computing cost poses a challenge to its widespread application. The present study aims to construct DeepCSP, a feasible pure machine learning framework for minute-scale rapid organic CSP. Initially, based on 177,746 data entries from the Cambridge Crystal Structure Database, a generative adversarial network was built to conditionally generate trial crystal structures under selected feature constraints for the given molecule. Simultaneously, a graph

SUBMITTER: Ye Z 

PROVIDER: S-EPMC10878116 | biostudies-literature | 2024 Mar

REPOSITORIES: biostudies-literature