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Pregled bibliografske jedinice broj: 1124874

Machine learning-based prediction of multi-target antimicrobial activity


Mikelić, Ana; Primožič, Ines; Ramić, Alma; Odžak, Renata; Hrenar, Tomica
Machine learning-based prediction of multi-target antimicrobial activity // Simpozij studenata doktorskih studija PMF-a : knjiga sažetaka / Barišić, Dajana (ur.).
Zagreb: Prirodoslovno-matematički fakultet, 2021. str. 358-359 (poster, domaća recenzija, sažetak, znanstveni)


CROSBI ID: 1124874 Za ispravke kontaktirajte CROSBI podršku putem web obrasca

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 : Prirodoslovno-matematički fakultet, 2021, 358-359

ISBN
978-953-6076-90-1

Skup
5. Simpozij studenata doktorskih studija

Mjesto i datum
Zagreb, Hrvatska, 24.-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


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
Machine learning-based prediction of multi-target antimicrobial activity // Simpozij studenata doktorskih studija PMF-a : knjiga sažetaka / Barišić, Dajana (ur.).
Zagreb: Prirodoslovno-matematički fakultet, 2021. str. 358-359 (poster, domaća recenzija, sažetak, znanstveni)
Mikelić, A., Primožič, I., Ramić, A., Odžak, R. & Hrenar, T. (2021) Machine learning-based prediction of multi-target antimicrobial activity. U: Barišić, D. (ur.)Simpozij studenata doktorskih studija PMF-a : knjiga sažetaka.
@article{article, editor = {Bari\v{s}i\'{c}, D.}, year = {2021}, pages = {358-359}, keywords = {machine learning, multivariate linear regression, principal component analysis, potential energy surface, ab initio molecular dynamics, antimicrobial activity, Cinchona alkaloids derivatives}, isbn = {978-953-6076-90-1}, title = {Machine learning-based prediction of multi-target antimicrobial activity}, keyword = {machine learning, multivariate linear regression, principal component analysis, potential energy surface, ab initio molecular dynamics, antimicrobial activity, Cinchona alkaloids derivatives}, publisher = {Prirodoslovno-matemati\v{c}ki fakultet}, publisherplace = {Zagreb, Hrvatska} }
@article{article, editor = {Bari\v{s}i\'{c}, D.}, year = {2021}, pages = {358-359}, keywords = {machine learning, multivariate linear regression, principal component analysis, potential energy surface, ab initio molecular dynamics, antimicrobial activity, Cinchona alkaloids derivatives}, isbn = {978-953-6076-90-1}, title = {Machine learning-based prediction of multi-target antimicrobial activity}, keyword = {machine learning, multivariate linear regression, principal component analysis, potential energy surface, ab initio molecular dynamics, antimicrobial activity, Cinchona alkaloids derivatives}, publisher = {Prirodoslovno-matemati\v{c}ki fakultet}, publisherplace = {Zagreb, Hrvatska} }




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