Naslov
Application of machine learning for herbicide characterization

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

Vrsta, podvrsta i kategorija rada
Sažeci sa skupova, sažetak, znanstveni

Izvornik
Book Of Abstracts / Darko, Babić ; Danijela, Barić ; Marko, Cvitaš ; Ines, Despotović ; Nađa, Došlić ; Marko, Hanževački ; Tomica, Hrenar ; Borislav, Kovačević ; Ivan, Ljubić ; Zlatko, Mihalić ; Davor, Šakić ; Tana, Tandarić ; Mario, Vazdar ; Robert, Vianello ; Valerije, Vrček ; Tin, Weitner - Zagreb : Prirodoslovno-matematički fakultet Sveučilišta u Zagrebu, 2019, 33-33

ISBN
978-953-6076-51-2

Skup
Computational Chemistry Day 2019

Mjesto i datum
Zagreb, Hrvatska, 11.05.2019

Vrsta sudjelovanja
Poster

Vrsta recenzije
Domaća recenzija

Ključne riječi
herbicides ; machine learning ; ADME ; toxicity

Sažetak
Herbicides are chemical molecules used for destruction of weeds. Massive usage of herbicides has resulted in two global problems: increase in weed resistance and harmful impact of human health [1, 2]. In order to facilitate development of novel, more specific herbicides and of strategies for impeding the weed resistance, we have carried out extensive in silico analysis of the set of herbicides. Herein, we present results revealing links between structural, physicochemical, ADME (Absorption, Distribution, Metabolism, Excretion) and toxic features for herbicides (Figure 1). The analysis has been done by using proper machine learning approaches. References: [1] A. Forouzesh, E. Zand, S. Soufizadeh, S. S. Foroushani, Weed Res. 55 (2015) 334-358. [2] V. I. Lushchak, T. M. Matviishyn, V. V. Husak, J. M. Storey, K. B. Storey, EXCLI J. 17 (2018) 1101-1136.

Izvorni jezik
Engleski

Znanstvena područja
Kemija, Poljoprivreda (agronomija), Interdisciplinarne biotehničke znanosti

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