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iQSPR in XenonPy: A Bayesian Molecular Design Algorithm
Author(s) -
Wu Stephen,
Lambard Guillaume,
Liu Chang,
Yamada Hironao,
Yoshida Ryo
Publication year - 2020
Publication title -
molecular informatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.481
H-Index - 68
eISSN - 1868-1751
pISSN - 1868-1743
DOI - 10.1002/minf.201900107
Subject(s) - python (programming language) , computer science , extensibility , bayesian probability , software , algorithm , inverse , bayesian inference , inference , key (lock) , data mining , artificial intelligence , programming language , mathematics , geometry , computer security
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.