Machine learning determined models of inhibitory activities for fluorinated Cinchona alkaloids (CROSBI ID 717112)
Prilog sa skupa u zborniku | sažetak izlaganja sa skupa | domaća recenzija
Podaci o odgovornosti
Mikelić, Ana ; Ramić, Alma ; Primožič, Ines ; Hrenar, Tomica
engleski
Machine learning determined models of inhibitory activities for fluorinated Cinchona alkaloids
A series of 25 fluorinated Cinchona alkaloids derivatives was theoretically investigated by calculation of their potential energy surfaces (PES). PES for all compounds were sampled by performing molecular dynamics simulations [1] and then decomposed by principal component analysis. Each PES was represented by three points in the newly determined reduced space. These points were used as independent variables for establishing activity/PES regression models whereas previously measured inhibitory activities towards human acetyl- and butyrylcholinesterase were used as dependent variables. Multivariate linear regression models were built by applying an extensive machine learning protocol where linear combinations of original variables as well as their higherorder polynomial terms were used. Leave-one-out cross- validation (LOO-CV) was used to validate obtained models [2, 3]. Optimal activity/PES models were selected based on the adjusted R 2, predicted R 2 and the LOO-CV mean squared error (Figure 1).
inhibitory activity, Cinchona alkaloids, machine learning, principal component analysis, potential energy surfaces, ab initio molecular dynamics, multivariate linear regression
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Podaci o prilogu
164-165.
2022.
objavljeno
Podaci o matičnoj publikaciji
6. Simpozij studenata doktorskih studija PMF-a: Knjiga sažetaka / 6th Faculty of Science PhD Student Symposium: Book of Abstracts
Schneider, Petra
Zagreb: Prirodoslovno-matematički fakultet Sveučilišta u Zagrebu
978-953-6076-93-2
Podaci o skupu
6. Simpozij studenata doktorskih studija PMF-a = 6th Faculty of Science PhD Student Symposium
predavanje
23.05.2022-24.05.2022
Zagreb, Hrvatska