A focus on simulation and machine learning as complementary tools for chemical space navigation

Computer-aided molecular design benefits from the integration of two complementary approaches: machine learning and first-principles simulation. Mohr et al. (B. Mohr, K. Shmilovich, I. S. Kleinwächter, D. Schneider, A. L. Ferguson and T. Bereau, Chem. Sci., 2022, 13, 4498–4511, https://pubs.rsc.org/...

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Detalles Bibliográficos
Autores principales: Aldeghi, Matteo, Coley, Connor W.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: The Royal Society of Chemistry 2022
Materias:
Chemistry
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9297700/
https://www.ncbi.nlm.nih.gov/pubmed/35919730
http://dx.doi.org/10.1039/d2sc90130g
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author Aldeghi, Matteo
Coley, Connor W.
author_facet Aldeghi, Matteo
Coley, Connor W.
author_sort Aldeghi, Matteo
collection PubMed
description Computer-aided molecular design benefits from the integration of two complementary approaches: machine learning and first-principles simulation. Mohr et al. (B. Mohr, K. Shmilovich, I. S. Kleinwächter, D. Schneider, A. L. Ferguson and T. Bereau, Chem. Sci., 2022, 13, 4498–4511, https://pubs.rsc.org/en/content/articlelanding/2022/sc/d2sc00116k) demonstrated the discovery of a cardiolipin-selective molecule via the combination of coarse-grained molecular dynamics, alchemical free energy calculations, Bayesian optimization and interpretable regression to reveal design principles.
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spelling pubmed-92977002022-08-01 A focus on simulation and machine learning as complementary tools for chemical space navigation Aldeghi, Matteo Coley, Connor W. Chem Sci Chemistry Computer-aided molecular design benefits from the integration of two complementary approaches: machine learning and first-principles simulation. Mohr et al. (B. Mohr, K. Shmilovich, I. S. Kleinwächter, D. Schneider, A. L. Ferguson and T. Bereau, Chem. Sci., 2022, 13, 4498–4511, https://pubs.rsc.org/en/content/articlelanding/2022/sc/d2sc00116k) demonstrated the discovery of a cardiolipin-selective molecule via the combination of coarse-grained molecular dynamics, alchemical free energy calculations, Bayesian optimization and interpretable regression to reveal design principles. The Royal Society of Chemistry 2022-07-11 /pmc/articles/PMC9297700/ /pubmed/35919730 http://dx.doi.org/10.1039/d2sc90130g Text en This journal is © The Royal Society of Chemistry https://creativecommons.org/licenses/by/3.0/
spellingShingle Chemistry
Aldeghi, Matteo
Coley, Connor W.
A focus on simulation and machine learning as complementary tools for chemical space navigation
title A focus on simulation and machine learning as complementary tools for chemical space navigation
title_full A focus on simulation and machine learning as complementary tools for chemical space navigation
title_fullStr A focus on simulation and machine learning as complementary tools for chemical space navigation
title_full_unstemmed A focus on simulation and machine learning as complementary tools for chemical space navigation
title_short A focus on simulation and machine learning as complementary tools for chemical space navigation
title_sort focus on simulation and machine learning as complementary tools for chemical space navigation
topic Chemistry
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9297700/
https://www.ncbi.nlm.nih.gov/pubmed/35919730
http://dx.doi.org/10.1039/d2sc90130g
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