Pregled bibliografske jedinice broj: 990906
Application of machine learning approaches for design of more selective herbicides
Application of machine learning approaches for design of more selective herbicides // Book of Abstracts, The 3rd COST-sponsored ARBRE- MOBIEU plenary meeting / Ivošević DeNardis, Nadica ; Campos-Olivas, Ramon ; Miele, Adriana E. ; England, Patrick ; Vuletić, Tomislav (ur.).
Zagreb: Institut Ruđer Bošković ; Hrvatsko biofizičko društvo, 2019. str. 113-114 (poster, međunarodna recenzija, sažetak, znanstveni)
CROSBI ID: 990906 Za ispravke kontaktirajte CROSBI podršku putem web obrasca
Naslov
Application of machine learning approaches for design of more selective herbicides
Autori
Pehar, Vesna ; Oršolić, Davor ; Jadrijević-Mladar Takač, Milena ; Stepanić, Višnja
Vrsta, podvrsta i kategorija rada
Sažeci sa skupova, sažetak, znanstveni
Izvornik
Book of Abstracts, The 3rd COST-sponsored ARBRE- MOBIEU plenary meeting
/ Ivošević DeNardis, Nadica ; Campos-Olivas, Ramon ; Miele, Adriana E. ; England, Patrick ; Vuletić, Tomislav - Zagreb : Institut Ruđer Bošković ; Hrvatsko biofizičko društvo, 2019, 113-114
ISBN
978-953-7941-28-4
Skup
3rd COST-sponsored ARBRE-MOBIEU plenary meeting Molecular Biophysics - ABC of the puzzle of Life
Mjesto i datum
Zagreb, Hrvatska, 18.03.2019. - 20.03.2019
Vrsta sudjelovanja
Poster
Vrsta recenzije
Međunarodna recenzija
Ključne riječi
herbicides ; machine learning methods ; models ; QSAR
Sažetak
Herbicides are chemical molecules used for destruction of weeds. Massive usage of herbicides has resulted in two global problems: increase in herbicide resistance and harmful impact of human health [1, 2]. In order to facilitate development of novel, more specific herbicides and development of strategies for impeding the weed resistance development, 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. The analysis has been done by using proper machine learning approaches. References [1] Forouzesh A, Zand E, Soufizadeh S, Foroushani SS. W Classification of herbicides according to chemical family for weed resistance management strategies–an update, Weed Res, 2015 ; 55: 334-358. doi: 10.1111/wre.12153. [2] Lushchak VI, Matviishyn TM, Husak VV, Storey JM, Storey KB. Pesticide toxicity: a mechanistic approach. EXCLI J. 2018 ; 17:1101-1136. doi: 10.17179/excli2018-1710.
Izvorni jezik
Engleski
Znanstvena područja
Kemija, Interdisciplinarne prirodne znanosti, Biotehnologija
POVEZANOST RADA
Projekti:
KK.01.1.1.01
Ustanove:
Farmaceutsko-biokemijski fakultet, Zagreb,
Institut "Ruđer Bošković", Zagreb
Profili:
Vesna Pehar Pejčinović
(autor)
Vesna Pehar
(autor)
Višnja Stepanić
(autor)
Milena Jadrijević-Mladar Takač
(autor)