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Alignment-independent technique for 3D QSAR analysis

Molecular biochemistry is controlled by 3D phenomena but structure–activity models based on 3D descriptors are infrequently used for large data sets because of the computational overhead for determining molecular conformations. A diverse dataset of 146 androgen receptor binders was used to investiga...

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Autores principales: Wilkes, Jon G., Stoyanova-Slavova, Iva B., Buzatu, Dan A.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer International Publishing 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4833814/
https://www.ncbi.nlm.nih.gov/pubmed/27026022
http://dx.doi.org/10.1007/s10822-016-9909-0
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author Wilkes, Jon G.
Stoyanova-Slavova, Iva B.
Buzatu, Dan A.
author_facet Wilkes, Jon G.
Stoyanova-Slavova, Iva B.
Buzatu, Dan A.
author_sort Wilkes, Jon G.
collection PubMed
description Molecular biochemistry is controlled by 3D phenomena but structure–activity models based on 3D descriptors are infrequently used for large data sets because of the computational overhead for determining molecular conformations. A diverse dataset of 146 androgen receptor binders was used to investigate how different methods for defining molecular conformations affect the performance of 3D-quantitative spectral data activity relationship models. Molecular conformations tested: (1) global minimum of molecules’ potential energy surface; (2) alignment-to-templates using equal electronic and steric force field contributions; (3) alignment using contributions “Best-for-Each” template; (4) non-energy optimized, non-aligned (2D > 3D). Aggregate predictions from models were compared. Highest average coefficients of determination ranged from R(Test)(2) = 0.56 to 0.61. The best model using 2D > 3D (imported directly from ChemSpider) produced R(Test)(2) = 0.61. It was superior to energy-minimized and conformation-aligned models and was achieved in only 3–7 % of the time required using the other conformation strategies. Predictions averaged from models built on different conformations achieved a consensus R(Test)(2) = 0.65. The best 2D > 3D model was analyzed for underlying structure–activity relationships. For the compound strongest binding to the androgen receptor, 10 substructural features contributing to binding were flagged. Utility of 2D > 3D was compared for two other activity endpoints, each modeling a medium sized data set. Results suggested that large scale, accurate predictions using 2D > 3D SDAR descriptors may be produced for interactions involving endocrine system nuclear receptors and other data sets in which strongest activities are produced by fairly inflexible substrates. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s10822-016-9909-0) contains supplementary material, which is available to authorized users.
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spelling pubmed-48338142016-04-25 Alignment-independent technique for 3D QSAR analysis Wilkes, Jon G. Stoyanova-Slavova, Iva B. Buzatu, Dan A. J Comput Aided Mol Des Article Molecular biochemistry is controlled by 3D phenomena but structure–activity models based on 3D descriptors are infrequently used for large data sets because of the computational overhead for determining molecular conformations. A diverse dataset of 146 androgen receptor binders was used to investigate how different methods for defining molecular conformations affect the performance of 3D-quantitative spectral data activity relationship models. Molecular conformations tested: (1) global minimum of molecules’ potential energy surface; (2) alignment-to-templates using equal electronic and steric force field contributions; (3) alignment using contributions “Best-for-Each” template; (4) non-energy optimized, non-aligned (2D > 3D). Aggregate predictions from models were compared. Highest average coefficients of determination ranged from R(Test)(2) = 0.56 to 0.61. The best model using 2D > 3D (imported directly from ChemSpider) produced R(Test)(2) = 0.61. It was superior to energy-minimized and conformation-aligned models and was achieved in only 3–7 % of the time required using the other conformation strategies. Predictions averaged from models built on different conformations achieved a consensus R(Test)(2) = 0.65. The best 2D > 3D model was analyzed for underlying structure–activity relationships. For the compound strongest binding to the androgen receptor, 10 substructural features contributing to binding were flagged. Utility of 2D > 3D was compared for two other activity endpoints, each modeling a medium sized data set. Results suggested that large scale, accurate predictions using 2D > 3D SDAR descriptors may be produced for interactions involving endocrine system nuclear receptors and other data sets in which strongest activities are produced by fairly inflexible substrates. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s10822-016-9909-0) contains supplementary material, which is available to authorized users. Springer International Publishing 2016-03-30 2016 /pmc/articles/PMC4833814/ /pubmed/27026022 http://dx.doi.org/10.1007/s10822-016-9909-0 Text en © The Author(s) 2016 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
spellingShingle Article
Wilkes, Jon G.
Stoyanova-Slavova, Iva B.
Buzatu, Dan A.
Alignment-independent technique for 3D QSAR analysis
title Alignment-independent technique for 3D QSAR analysis
title_full Alignment-independent technique for 3D QSAR analysis
title_fullStr Alignment-independent technique for 3D QSAR analysis
title_full_unstemmed Alignment-independent technique for 3D QSAR analysis
title_short Alignment-independent technique for 3D QSAR analysis
title_sort alignment-independent technique for 3d qsar analysis
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4833814/
https://www.ncbi.nlm.nih.gov/pubmed/27026022
http://dx.doi.org/10.1007/s10822-016-9909-0
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