Comprehensive machine learning based study of the chemical space of herbicides

Abstract Widespread use of herbicides results in the global increase in weed resistance. The rotational use of herbicides according to their modes of action (MoAs) and discovery of novel phytotoxic molecules are the two strategies used against the weed resistance. Herein, Random Forest modeling was...

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Autores principales:	Davor Oršolić, Vesna Pehar, Tomislav Šmuc, Višnja Stepanić
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/3ce6d67198584709bdb4bef84fa4059e
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spelling	oai:doaj.org-article:3ce6d67198584709bdb4bef84fa4059e2021-12-02T18:25:04ZComprehensive machine learning based study of the chemical space of herbicides10.1038/s41598-021-90690-w2045-2322https://doaj.org/article/3ce6d67198584709bdb4bef84fa4059e2021-06-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-90690-whttps://doaj.org/toc/2045-2322Abstract Widespread use of herbicides results in the global increase in weed resistance. The rotational use of herbicides according to their modes of action (MoAs) and discovery of novel phytotoxic molecules are the two strategies used against the weed resistance. Herein, Random Forest modeling was used to build predictive models and establish comprehensive characterization of structure–activity relationships underlying herbicide classifications according to their MoAs and weed selectivity. By combining the predictive models with herbicide-likeness rules defined by selected molecular features (numbers of H-bond acceptors and donors, logP, topological and relative polar surface area, and net charge), the virtual stepwise screening platform is proposed for characterization of small weight molecules for their phytotoxic properties. The screening cascade was applied on the data set of phytotoxic natural products. The obtained results may be valuable for refinement of herbicide rotational program as well as for discovery of novel herbicides primarily among natural products as a source for molecules of novel structures and novel modes of action and translocation profiles as compared with the synthetic compounds.Davor OršolićVesna PeharTomislav ŠmucVišnja StepanićNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-12 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Davor Oršolić Vesna Pehar Tomislav Šmuc Višnja Stepanić Comprehensive machine learning based study of the chemical space of herbicides
description	Abstract Widespread use of herbicides results in the global increase in weed resistance. The rotational use of herbicides according to their modes of action (MoAs) and discovery of novel phytotoxic molecules are the two strategies used against the weed resistance. Herein, Random Forest modeling was used to build predictive models and establish comprehensive characterization of structure–activity relationships underlying herbicide classifications according to their MoAs and weed selectivity. By combining the predictive models with herbicide-likeness rules defined by selected molecular features (numbers of H-bond acceptors and donors, logP, topological and relative polar surface area, and net charge), the virtual stepwise screening platform is proposed for characterization of small weight molecules for their phytotoxic properties. The screening cascade was applied on the data set of phytotoxic natural products. The obtained results may be valuable for refinement of herbicide rotational program as well as for discovery of novel herbicides primarily among natural products as a source for molecules of novel structures and novel modes of action and translocation profiles as compared with the synthetic compounds.
format	article
author	Davor Oršolić Vesna Pehar Tomislav Šmuc Višnja Stepanić
author_facet	Davor Oršolić Vesna Pehar Tomislav Šmuc Višnja Stepanić
author_sort	Davor Oršolić
title	Comprehensive machine learning based study of the chemical space of herbicides
title_short	Comprehensive machine learning based study of the chemical space of herbicides
title_full	Comprehensive machine learning based study of the chemical space of herbicides
title_fullStr	Comprehensive machine learning based study of the chemical space of herbicides
title_full_unstemmed	Comprehensive machine learning based study of the chemical space of herbicides
title_sort	comprehensive machine learning based study of the chemical space of herbicides
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/3ce6d67198584709bdb4bef84fa4059e
work_keys_str_mv	AT davororsolic comprehensivemachinelearningbasedstudyofthechemicalspaceofherbicides AT vesnapehar comprehensivemachinelearningbasedstudyofthechemicalspaceofherbicides AT tomislavsmuc comprehensivemachinelearningbasedstudyofthechemicalspaceofherbicides AT visnjastepanic comprehensivemachinelearningbasedstudyofthechemicalspaceofherbicides
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Comprehensive machine learning based study of the chemical space of herbicides

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