Naslov
Machine learning-based prediction of multi-target antimicrobial activity

Autori
Mikelić, Ana ; Primožič, Ines ; Ramić, Alma ; Odžak, Renata ; Hrenar, Tomica

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

Izvornik
Simpozij studenata doktorskih studija PMF-a : knjiga sažetaka / Barišić, Dajana - Zagreb, 2021, 358-359

ISBN
978-953-6076-90-1

Skup
5. Simpozij studenata doktorskih studija PMF-a = 5th Faculty of Science PhD Student Symposium

Mjesto i datum
Zagreb, Hrvatska, 24.04.2021. - 25.04.2021

Vrsta sudjelovanja
Poster

Vrsta recenzije
Domaća recenzija

Ključne riječi
machine learning ; multivariate linear regression ; principal component analysis ; potential energy surface ; ab initio molecular dynamics ; antimicrobial activity ; Cinchona alkaloids derivatives

Sažetak
Reduced space of multi-target antimicrobial activities was used as a dependent variable for estimation of Cinchona alkaloids derivatives [1] activities. A panel of various Gram- positive and Gram-negative bacteria provided activity data whose principal components were extracted by 2nd-order tensor decomposition. These principal components were regressed on the theoretically computed energy fingerprints of all compounds. Extensive machine learning procedure was applied for generation of multivariate linear regression models with linear combination of original variables as well as their higher- order polynomial terms. Regression models of antimicrobial activity in dependence on molecular dynamics data were builded and thoroughly validated by leave-one- out cross- validation technique (LOO-CV) [2]. The most optimal representation was selected on the basis of R2 values and LOO-CV mean squared error and is presented on Fig. 1.

Izvorni jezik
Engleski

Znanstvena područja
Kemija

POVEZANOST RADA