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Pregled bibliografske jedinice broj: 1132242

Multi-target antimicrobial activity model of Cinchona alkaloids established by machine learning


Mikelić, Ana; Primožič, Ines; Ramić, Alma; Odžak, Renata; Hrenar, Tomica
Multi-target antimicrobial activity model of Cinchona alkaloids established by machine learning // Math/Chem/Comp 2021 - 32nd MC2 Conference: Book of Abstracts / Vančik, Hrvoj ; Cioslowski, Jerzy ; Namjesnik, Danijel (ur.).
Zagreb: Croatian Chemical Society, 2021. str. 33-33 (predavanje, međunarodna recenzija, sažetak, znanstveni)


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Naslov
Multi-target antimicrobial activity model of Cinchona alkaloids established by machine learning

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

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

Izvornik
Math/Chem/Comp 2021 - 32nd MC2 Conference: Book of Abstracts / Vančik, Hrvoj ; Cioslowski, Jerzy ; Namjesnik, Danijel - Zagreb : Croatian Chemical Society, 2021, 33-33

ISBN
978-953-8334-02-3

Skup
Math/Chem/Comp 2021

Mjesto i datum
Dubrovnik, Hrvatska, 07-11.06.2021

Vrsta sudjelovanja
Predavanje

Vrsta recenzije
Međunarodna recenzija

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

Sažetak
Antimicrobial activity of Cinchona alkaloids derivatives [1] was previously evaluated by using disc diffusion assay against a panel of various Gram-positive and Gram-negative bacteria. Principal components of the activity data were extracted by 2nd-order tensor decomposition and used as dependent variables for multivariate linear regression, whereas theoretically computed energy fingerprints of all compounds were used as independent variables. Potential energy surfaces (PES) of compounds were sampled by performing molecular dynamics simulations and then decomposed by principal component analysis. Regression models were generated by extensive machine learning multivariate linear regression – linear combinations of original variables were used as well as their higher-order polynomial terms. Obtained models were thoroughly validated by leave-one-out cross- validation technique (LOO- CV) [2]. The optimal activity/PES model based on the adjusted and the predicted R2 values as well as LOO-CV mean squared error will be presented.

Izvorni jezik
Engleski

Znanstvena područja
Kemija



POVEZANOST RADA


Projekti:
HRZZ-IP-2016-06-3775 - Aktivnošću i in silico usmjeren dizajn malih bioaktivnih molekula (ADESIRE) (Hrenar, Tomica, HRZZ - 2016-06) ( POIROT)

Ustanove:
Prirodoslovno-matematički fakultet, Zagreb,
Prirodoslovno-matematički fakultet, Split

Profili:

Avatar Url Tomica Hrenar (autor)

Avatar Url Renata Odžak (autor)

Avatar Url Alma Ramic (autor)

Avatar Url Ines Primožič (autor)

Avatar Url Ana Mikelić (autor)


Citiraj ovu publikaciju

Mikelić, Ana; Primožič, Ines; Ramić, Alma; Odžak, Renata; Hrenar, Tomica
Multi-target antimicrobial activity model of Cinchona alkaloids established by machine learning // Math/Chem/Comp 2021 - 32nd MC2 Conference: Book of Abstracts / Vančik, Hrvoj ; Cioslowski, Jerzy ; Namjesnik, Danijel (ur.).
Zagreb: Croatian Chemical Society, 2021. str. 33-33 (predavanje, međunarodna recenzija, sažetak, znanstveni)
Mikelić, A., Primožič, I., Ramić, A., Odžak, R. & Hrenar, T. (2021) Multi-target antimicrobial activity model of Cinchona alkaloids established by machine learning. U: Vančik, H., Cioslowski, J. & Namjesnik, D. (ur.)Math/Chem/Comp 2021 - 32nd MC2 Conference: Book of Abstracts.
@article{article, year = {2021}, pages = {33-33}, keywords = {antimicrobial activity, Cinchona alkaloids, machine learning, principal component analysis, potential energy surfaces, ab initio molecular dynamics, multivariate linear regression}, isbn = {978-953-8334-02-3}, title = {Multi-target antimicrobial activity model of Cinchona alkaloids established by machine learning}, keyword = {antimicrobial activity, Cinchona alkaloids, machine learning, principal component analysis, potential energy surfaces, ab initio molecular dynamics, multivariate linear regression}, publisher = {Croatian Chemical Society}, publisherplace = {Dubrovnik, Hrvatska} }
@article{article, year = {2021}, pages = {33-33}, keywords = {antimicrobial activity, Cinchona alkaloids, machine learning, principal component analysis, potential energy surfaces, ab initio molecular dynamics, multivariate linear regression}, isbn = {978-953-8334-02-3}, title = {Multi-target antimicrobial activity model of Cinchona alkaloids established by machine learning}, keyword = {antimicrobial activity, Cinchona alkaloids, machine learning, principal component analysis, potential energy surfaces, ab initio molecular dynamics, multivariate linear regression}, publisher = {Croatian Chemical Society}, publisherplace = {Dubrovnik, Hrvatska} }




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