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 (CSP) 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 current study aims to construct DeepCSP, a feasible pure machine learning framework for minute-scale rapid organic crystal structure prediction. Initially, based on 177,746 data entries from the Cambridge Crystal Structure Database (CSD), a generative adversarial network was built to conditionally generate trial crystal structures under selected feature constraints for the given molecule. Simultaneously, a graph convolutional attention network was employed to predict the density of stable crystal structures for the input molecule. Subsequently, the distances between the predicted density and the definition-based calculated density would be considered as the crystal structure screening and ranking basis, and finally, the density-based crystal structure ranking would be output. Such two distinct algorithms, performing the generation and ranking functionalities respectively, collectively constitute the DeepCSP, which has demonstrated compelling performance in marketed drug validations, achieving an accuracy rate exceeding 80% and hit rate surpassing 85%. Inspiringly, the computing speed of the pure machine learning methodology demonstrates the potential of artificial intelligence in advancing CSP research.