Application of machine learning for herbicide characterization (CROSBI ID 676104)
Prilog sa skupa u zborniku | sažetak izlaganja sa skupa | domaća recenzija
Podaci o odgovornosti
Pehar, Vesna ; Oršolić, Davor ; Stepanić, Višnja
engleski
Application of machine learning for herbicide characterization
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.
herbicides ; machine learning ; ADME ; toxicity
nije evidentirano
nije evidentirano
nije evidentirano
nije evidentirano
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Podaci o prilogu
33-33.
2019.
objavljeno
Podaci o matičnoj publikaciji
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
978-953-6076-51-2
Podaci o skupu
Computational Chemistry Day 2019
poster
11.05.2019-11.05.2019
Zagreb, Hrvatska
Povezanost rada
Interdisciplinarne biotehničke znanosti, Kemija, Poljoprivreda (agronomija)