IQSPR in XenonPy: A Bayesian Molecular Design Algorithm. Issue 1 (5th November 2019)

• Record Type: Journal Article
• Authors: Wu, Stephen; Lambard, Guillaume; Liu, Chang; Yamada, Hironao; Yoshida, Ryo
• Is Part Of: Molecular informatics. Volume 39:Issue 1/2(2020)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2019-11-05
• Subjects: molecular design -- machine learning -- Bayesian inference -- open source -- polymer; Cheminformatics -- Periodicals; QSAR (Biochemistry) -- Periodicals; Structure-activity relationships (Biochemistry) -- Periodicals; Drugs -- Structure-activity relationships -- Periodicals; 615.19
• DOI: 10.1002/minf.201900107
• Languages: English
• ISSNs: 1868-1743

Abstract

Abstract: iQSPR is an inverse molecular design algorithm based on Bayesian inference that was developed in our previous study. Here, the algorithm is integrated in Python as a new module called iQSPR‐X in the all‐in‐one materials informatics platform XenonPy. Our new software provides a flexible, easy‐to‐use, and extensible platform for users to build customized molecular design algorithms using pre‐set modules and a pre‐trained model library in XenonPy. In this paper, we describe key features of iQSPR‐X and provide guidance on its use, illustrated by an application to a polymer design that targets a specific range of bandgap and dielectric constant. Abstract :