iQSPR in XenonPy: A Bayesian Molecular Design Algorithm

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,...

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Detalles Bibliográficos
Autores principales: Wu, Stephen, Lambard, Guillaume, Liu, Chang, Yamada, Hironao, Yoshida, Ryo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2019
Materias:
Full Papers
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7050509/
https://www.ncbi.nlm.nih.gov/pubmed/31841276
http://dx.doi.org/10.1002/minf.201900107
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author Wu, Stephen
Lambard, Guillaume
Liu, Chang
Yamada, Hironao
Yoshida, Ryo
author_facet Wu, Stephen
Lambard, Guillaume
Liu, Chang
Yamada, Hironao
Yoshida, Ryo
author_sort Wu, Stephen
collection PubMed
description 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.
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spelling pubmed-70505092020-03-09 iQSPR in XenonPy: A Bayesian Molecular Design Algorithm Wu, Stephen Lambard, Guillaume Liu, Chang Yamada, Hironao Yoshida, Ryo Mol Inform Full Papers 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. John Wiley and Sons Inc. 2019-11-05 2020-01 /pmc/articles/PMC7050509/ /pubmed/31841276 http://dx.doi.org/10.1002/minf.201900107 Text en © 2019 The Authors. Published by Wiley-VCH Verlag GmbH & Co. KGaA. This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.
spellingShingle Full Papers
Wu, Stephen
Lambard, Guillaume
Liu, Chang
Yamada, Hironao
Yoshida, Ryo
iQSPR in XenonPy: A Bayesian Molecular Design Algorithm
title iQSPR in XenonPy: A Bayesian Molecular Design Algorithm
title_full iQSPR in XenonPy: A Bayesian Molecular Design Algorithm
title_fullStr iQSPR in XenonPy: A Bayesian Molecular Design Algorithm
title_full_unstemmed iQSPR in XenonPy: A Bayesian Molecular Design Algorithm
title_short iQSPR in XenonPy: A Bayesian Molecular Design Algorithm
title_sort iqspr in xenonpy: a bayesian molecular design algorithm
topic Full Papers
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7050509/
https://www.ncbi.nlm.nih.gov/pubmed/31841276
http://dx.doi.org/10.1002/minf.201900107
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