Comprehensive machine learning based study of the chemical space of herbicides

Oršolić, Davor; Pehar, Vesna; Šmuc, Tomislav; Stepanić, Višnja (2021) Comprehensive machine learning based study of the chemical space of herbicides. Scientific reports, 11 . ISSN 2045-2322

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Official URL: https://www.nature.com/articles/s41598-021-90690-w

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 (SAR) underlying herbicide classifications according to their MoAs and weed selectivity. By combining the predictive models with herbicide-like rules defined by selected molecular features (numbers of H-bond acceptors and donors, logP, topological (TPSA) and relative (RelPSA) 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 sites of action and translocation profiles as compared with the synthetic compounds.

Item Type:

Article

Uncontrolled Keywords:

herbicides ; HRAC ; virtual screening ; natural products ; machine learning ; Random Forest ; weed selectivity

Subjects:

NATURAL SCIENCES > Biology

Divisions:

Division of Electronics

Projects:

Project title	Project leader	Project code	Project type
Bioprospecting Jadranskog mora	Čož-Rakovac, Rozelinda; Dragović-Uzelac, Verica; Šantek, Božidar; Jokić, Stela; Jerković, Igor; Kraljević Pavelić, Sandra	KK.01.1.1.01.0002	EK

Depositing User:

Višnja StepaniÄ�

Date Deposited:

03 Aug 2021 07:20

URI:

http://fulir.irb.hr/id/eprint/6500

DOI:

10.1038/s41598-021-90690-w