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Quantitative estimation of pesticide-likeness for agrochemical discovery

BACKGROUND: The design of chemical libraries, an early step in agrochemical discovery programs, is frequently addressed by means of qualitative physicochemical and/or topological rule-based methods. The aim of this study is to develop quantitative estimates of herbicide- (QEH), insecticide- (QEI), f...

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Autores principales: Avram, Sorin, Funar-Timofei, Simona, Borota, Ana, Chennamaneni, Sridhar Rao, Manchala, Anil Kumar, Muresan, Sorel
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer International Publishing 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4173135/
https://www.ncbi.nlm.nih.gov/pubmed/25264458
http://dx.doi.org/10.1186/s13321-014-0042-6
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author Avram, Sorin
Funar-Timofei, Simona
Borota, Ana
Chennamaneni, Sridhar Rao
Manchala, Anil Kumar
Muresan, Sorel
author_facet Avram, Sorin
Funar-Timofei, Simona
Borota, Ana
Chennamaneni, Sridhar Rao
Manchala, Anil Kumar
Muresan, Sorel
author_sort Avram, Sorin
collection PubMed
description BACKGROUND: The design of chemical libraries, an early step in agrochemical discovery programs, is frequently addressed by means of qualitative physicochemical and/or topological rule-based methods. The aim of this study is to develop quantitative estimates of herbicide- (QEH), insecticide- (QEI), fungicide- (QEF), and, finally, pesticide-likeness (QEP). In the assessment of these definitions, we relied on the concept of desirability functions. RESULTS: We found a simple function, shared by the three classes of pesticides, parameterized particularly, for six, easy to compute, independent and interpretable, molecular properties: molecular weight, logP, number of hydrogen bond acceptors, number of hydrogen bond donors, number of rotatable bounds and number of aromatic rings. Subsequently, we describe the scoring of each pesticide class by the corresponding quantitative estimate. In a comparative study, we assessed the performance of the scoring functions using extensive datasets of patented pesticides. CONCLUSIONS: The hereby-established quantitative assessment has the ability to rank compounds whether they fail well-established pesticide-likeness rules or not, and offer an efficient way to prioritize (class-specific) pesticides. These findings are valuable for the efficient estimation of pesticide-likeness of vast chemical libraries in the field of agrochemical discovery. [Image: see text] ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s13321-014-0042-6) contains supplementary material, which is available to authorized users.
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spelling pubmed-41731352014-09-26 Quantitative estimation of pesticide-likeness for agrochemical discovery Avram, Sorin Funar-Timofei, Simona Borota, Ana Chennamaneni, Sridhar Rao Manchala, Anil Kumar Muresan, Sorel J Cheminform Research Article BACKGROUND: The design of chemical libraries, an early step in agrochemical discovery programs, is frequently addressed by means of qualitative physicochemical and/or topological rule-based methods. The aim of this study is to develop quantitative estimates of herbicide- (QEH), insecticide- (QEI), fungicide- (QEF), and, finally, pesticide-likeness (QEP). In the assessment of these definitions, we relied on the concept of desirability functions. RESULTS: We found a simple function, shared by the three classes of pesticides, parameterized particularly, for six, easy to compute, independent and interpretable, molecular properties: molecular weight, logP, number of hydrogen bond acceptors, number of hydrogen bond donors, number of rotatable bounds and number of aromatic rings. Subsequently, we describe the scoring of each pesticide class by the corresponding quantitative estimate. In a comparative study, we assessed the performance of the scoring functions using extensive datasets of patented pesticides. CONCLUSIONS: The hereby-established quantitative assessment has the ability to rank compounds whether they fail well-established pesticide-likeness rules or not, and offer an efficient way to prioritize (class-specific) pesticides. These findings are valuable for the efficient estimation of pesticide-likeness of vast chemical libraries in the field of agrochemical discovery. [Image: see text] ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s13321-014-0042-6) contains supplementary material, which is available to authorized users. Springer International Publishing 2014-09-12 /pmc/articles/PMC4173135/ /pubmed/25264458 http://dx.doi.org/10.1186/s13321-014-0042-6 Text en © Avram et al.; licensee Chemistry Central Ltd. 2014 This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research Article
Avram, Sorin
Funar-Timofei, Simona
Borota, Ana
Chennamaneni, Sridhar Rao
Manchala, Anil Kumar
Muresan, Sorel
Quantitative estimation of pesticide-likeness for agrochemical discovery
title Quantitative estimation of pesticide-likeness for agrochemical discovery
title_full Quantitative estimation of pesticide-likeness for agrochemical discovery
title_fullStr Quantitative estimation of pesticide-likeness for agrochemical discovery
title_full_unstemmed Quantitative estimation of pesticide-likeness for agrochemical discovery
title_short Quantitative estimation of pesticide-likeness for agrochemical discovery
title_sort quantitative estimation of pesticide-likeness for agrochemical discovery
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4173135/
https://www.ncbi.nlm.nih.gov/pubmed/25264458
http://dx.doi.org/10.1186/s13321-014-0042-6
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