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Beyond Rotatable Bond Counts: Capturing 3D Conformational Flexibility in a Single Descriptor

[Image: see text] A new molecular descriptor, nConf(20), based on chemical connectivity, is presented which captures the accessible conformational space of a molecule. Currently the best available two-dimensional descriptors for quantifying the flexibility of a particular molecule are the rotatable...

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Detalles Bibliográficos
Autores principales: Wicker, Jerome G. P., Cooper, Richard I.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: American Chemical Society 2016
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5271572/
https://www.ncbi.nlm.nih.gov/pubmed/28024401
http://dx.doi.org/10.1021/acs.jcim.6b00565
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author Wicker, Jerome G. P.
Cooper, Richard I.
author_facet Wicker, Jerome G. P.
Cooper, Richard I.
author_sort Wicker, Jerome G. P.
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description [Image: see text] A new molecular descriptor, nConf(20), based on chemical connectivity, is presented which captures the accessible conformational space of a molecule. Currently the best available two-dimensional descriptors for quantifying the flexibility of a particular molecule are the rotatable bond count (RBC) and the Kier flexibility index. We present a descriptor which captures this information by sampling the conformational space of a molecule using the RDKit conformer generator. Flexibility has previously been identified as a key feature in determining whether a molecule is likely to crystallize or not. For this application, nConf(20) significantly outperforms previously reported single-variable classifiers and also assists rule-based analysis of black-box machine learning classification algorithms.
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spelling pubmed-52715722017-01-30 Beyond Rotatable Bond Counts: Capturing 3D Conformational Flexibility in a Single Descriptor Wicker, Jerome G. P. Cooper, Richard I. J Chem Inf Model [Image: see text] A new molecular descriptor, nConf(20), based on chemical connectivity, is presented which captures the accessible conformational space of a molecule. Currently the best available two-dimensional descriptors for quantifying the flexibility of a particular molecule are the rotatable bond count (RBC) and the Kier flexibility index. We present a descriptor which captures this information by sampling the conformational space of a molecule using the RDKit conformer generator. Flexibility has previously been identified as a key feature in determining whether a molecule is likely to crystallize or not. For this application, nConf(20) significantly outperforms previously reported single-variable classifiers and also assists rule-based analysis of black-box machine learning classification algorithms. American Chemical Society 2016-11-23 2016-12-27 /pmc/articles/PMC5271572/ /pubmed/28024401 http://dx.doi.org/10.1021/acs.jcim.6b00565 Text en Copyright © 2016 American Chemical Society This is an open access article published under a Creative Commons Attribution (CC-BY) License (http://pubs.acs.org/page/policy/authorchoice_ccby_termsofuse.html) , which permits unrestricted use, distribution and reproduction in any medium, provided the author and source are cited.
spellingShingle Wicker, Jerome G. P.
Cooper, Richard I.
Beyond Rotatable Bond Counts: Capturing 3D Conformational Flexibility in a Single Descriptor
title Beyond Rotatable Bond Counts: Capturing 3D Conformational Flexibility in a Single Descriptor
title_full Beyond Rotatable Bond Counts: Capturing 3D Conformational Flexibility in a Single Descriptor
title_fullStr Beyond Rotatable Bond Counts: Capturing 3D Conformational Flexibility in a Single Descriptor
title_full_unstemmed Beyond Rotatable Bond Counts: Capturing 3D Conformational Flexibility in a Single Descriptor
title_short Beyond Rotatable Bond Counts: Capturing 3D Conformational Flexibility in a Single Descriptor
title_sort beyond rotatable bond counts: capturing 3d conformational flexibility in a single descriptor
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5271572/
https://www.ncbi.nlm.nih.gov/pubmed/28024401
http://dx.doi.org/10.1021/acs.jcim.6b00565
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