Pretražite po imenu i prezimenu autora, mentora, urednika, prevoditelja

Napredna pretraga

Pregled bibliografske jedinice broj: 1134292

Comprehensive machine learning based study of the chemical space of herbicides


Oršolić, Davor; Pehar, Vesna; Šmuc, Tomislav; Stepanić, Višnja
Comprehensive machine learning based study of the chemical space of herbicides // Scientific reports, 11 (2021), 11479, 12 doi:10.1038/s41598-021-90690-w (međunarodna recenzija, članak, znanstveni)


CROSBI ID: 1134292 Za ispravke kontaktirajte CROSBI podršku putem web obrasca

Naslov
Comprehensive machine learning based study of the chemical space of herbicides

Autori
Oršolić, Davor ; Pehar, Vesna ; Šmuc, Tomislav ; Stepanić, Višnja

Izvornik
Scientific reports (2045-2322) 11 (2021); 11479, 12

Vrsta, podvrsta i kategorija rada
Radovi u časopisima, članak, znanstveni

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

Sažetak
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.

Izvorni jezik
Engleski

Znanstvena područja
Kemija, Biologija, Interdisciplinarne prirodne znanosti, Računarstvo, Poljoprivreda (agronomija)



POVEZANOST RADA


Projekti:
EK-KF-KK.01.1.1.01.0002 - Bioprospecting Jadranskog mora (Čož-Rakovac, Rozelinda; Dragović-Uzelac, Verica; Šantek, Božidar; Jokić, Stela; Jerković, Igor; Kraljević Pavelić, Sandra, EK - KK.01.1.1.01) ( POIROT)

Ustanove:
Institut "Ruđer Bošković", Zagreb

Profili:

Avatar Url Vesna Pehar (autor)

Avatar Url Davor Oršolić (autor)

Avatar Url Tomislav Šmuc (autor)

Avatar Url Višnja Stepanić (autor)

Citiraj ovu publikaciju

Oršolić, Davor; Pehar, Vesna; Šmuc, Tomislav; Stepanić, Višnja
Comprehensive machine learning based study of the chemical space of herbicides // Scientific reports, 11 (2021), 11479, 12 doi:10.1038/s41598-021-90690-w (međunarodna recenzija, članak, znanstveni)
Oršolić, D., Pehar, V., Šmuc, T. & Stepanić, V. (2021) Comprehensive machine learning based study of the chemical space of herbicides. Scientific reports, 11, 11479, 12 doi:10.1038/s41598-021-90690-w.
@article{article, year = {2021}, pages = {12}, DOI = {10.1038/s41598-021-90690-w}, chapter = {11479}, keywords = {herbicides, HRAC, virtual screening, natural products, machine learning, Random Forest, weed selectivity}, journal = {Scientific reports}, doi = {10.1038/s41598-021-90690-w}, volume = {11}, issn = {2045-2322}, title = {Comprehensive machine learning based study of the chemical space of herbicides}, keyword = {herbicides, HRAC, virtual screening, natural products, machine learning, Random Forest, weed selectivity}, chapternumber = {11479} }
@article{article, year = {2021}, pages = {12}, DOI = {10.1038/s41598-021-90690-w}, chapter = {11479}, keywords = {herbicides, HRAC, virtual screening, natural products, machine learning, Random Forest, weed selectivity}, journal = {Scientific reports}, doi = {10.1038/s41598-021-90690-w}, volume = {11}, issn = {2045-2322}, title = {Comprehensive machine learning based study of the chemical space of herbicides}, keyword = {herbicides, HRAC, virtual screening, natural products, machine learning, Random Forest, weed selectivity}, chapternumber = {11479} }

Časopis indeksira:


  • Current Contents Connect (CCC)
  • Web of Science Core Collection (WoSCC)
    • Science Citation Index Expanded (SCI-EXP)
    • Social Science Citation Index (SSCI)
    • SCI-EXP, SSCI i/ili A&HCI
  • Scopus
  • MEDLINE


Citati





    Contrast
    Increase Font
    Decrease Font
    Dyslexic Font