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Pregled bibliografske jedinice broj: 1246979

On Approximating the pIC50 Value of COVID-19 Medicines In Silico with Artificial Neural Networks


Baressi Šegota, Sandi; Lorencin, Ivan; Kovač, Zoran; Car, Zlatan
On Approximating the pIC50 Value of COVID-19 Medicines In Silico with Artificial Neural Networks // Biomedicines, 11 (2023), 2; 284, 22 doi:https:// .org/10.3390/biomedicines11020284 (međunarodna recenzija, članak, znanstveni)


CROSBI ID: 1246979 Za ispravke kontaktirajte CROSBI podršku putem web obrasca

Naslov
On Approximating the pIC50 Value of COVID-19 Medicines In Silico with Artificial Neural Networks

Autori
Baressi Šegota, Sandi ; Lorencin, Ivan ; Kovač, Zoran ; Car, Zlatan

Izvornik
Biomedicines (2227-9059) 11 (2023), 2; 284, 22

Vrsta, podvrsta i kategorija rada
Radovi u časopisima, članak, znanstveni

Ključne riječi
artificial neural networks ; convolutional neural networks ; machine learning ; pIC50 ; regression modeling ; SMILES

Sažetak
In the case of pandemics such as COVID-19, the rapid development of medicines addressing the symptoms is necessary to alleviate the pressure on the medical system. One of the key steps in medicine evaluation is the determination of pIC50 factor, which is a negative logarithmic expression of the half maximal inhibitory concentration (IC50). Determining this value can be a lengthy and complicated process. A tool allowing for a quick approximation of pIC50 based on the molecular makeup of medicine could be valuable. In this paper, the creation of the artificial intelligence (AI)-based model is performed using a publicly available dataset of molecules and their pIC50 values. The modeling algorithms used are artificial and convolutional neural networks (ANN and CNN). Three approaches are tested—modeling using just molecular properties (MP), encoded SMILES representation of the molecule, and the combination of both input types. Models are evaluated using the coefficient of determination (R2) and mean absolute percentage error (MAPE) in a five-fold cross-validation scheme to assure the validity of the results. The obtained models show that the highest quality regression (R2¯¯¯¯=0.99, σR2¯¯¯¯=0.001 ; MAPE¯¯¯¯¯¯¯¯¯¯¯=0.009%, σMAPE¯¯¯¯¯¯¯¯¯¯¯¯=0.009), by a large margin, is obtained when using a hybrid neural network trained with both MP and SMILES.

Izvorni jezik
Engleski

Znanstvena područja
Računarstvo, Temeljne tehničke znanosti, Interdisciplinarne tehničke znanosti, Temeljne medicinske znanosti, Biotehnologija u biomedicini (prirodno područje, biomedicina i zdravstvo, biotehničko područje)



POVEZANOST RADA


Projekti:
EK-KF-KK.01.1.1.01.0009-1 - Napredne metode i tehnologije u znanosti o podatcima i kooperativnim sustavima - IJ za znanost o podatcima (Lončarić, Sven, EK - KK.01.1.1.01) ( CroRIS)
EK-EFRR-KK.01.2.2.03.0004 - Centar kompetencija za pametne gradove (CEKOM) (Car, Zlatan; Slavić, Nataša; Vilke, Siniša, EK - KK.01.2.2.03) ( CroRIS)
--uniri-mladi-technic-22-57 - Energetska optimizacija industrijskih robotskih manipulatora primjenom algoritama evolucijskog računarstva (Anđelić, Nikola) ( CroRIS)
--uniri-mladi-technic-22-57 - Razvoj inteligentnog sustava za estimaciju točke maksimalne snage fotonaponskog sustava s primjenom na autonomna plovila (Lorencin, Ivan) ( CroRIS)
NadSve-Sveučilište u Rijeci-uniri-tehnic-18-275-1447 - Razvoj inteligentnog ekspertnog sustava za online diagnostiku raka mokračnog mjehura (Car, Zlatan, NadSve - UNIRI potpore) ( CroRIS)

Ustanove:
Tehnički fakultet, Rijeka,
Fakultet dentalne medicine, Rijeka

Profili:

Avatar Url Zlatan Car (autor)

Avatar Url Zoran Kovač (autor)

Avatar Url Sandi Baressi Šegota (autor)

Avatar Url Ivan Lorencin (autor)

Poveznice na cjeloviti tekst rada:

Pristup cjelovitom tekstu rada doi www.mdpi.com

Citiraj ovu publikaciju:

Baressi Šegota, Sandi; Lorencin, Ivan; Kovač, Zoran; Car, Zlatan
On Approximating the pIC50 Value of COVID-19 Medicines In Silico with Artificial Neural Networks // Biomedicines, 11 (2023), 2; 284, 22 doi:https:// .org/10.3390/biomedicines11020284 (međunarodna recenzija, članak, znanstveni)
Baressi Šegota, S., Lorencin, I., Kovač, Z. & Car, Z. (2023) On Approximating the pIC50 Value of COVID-19 Medicines In Silico with Artificial Neural Networks. Biomedicines, 11 (2), 284, 22 doi:https:// .org/10.3390/biomedicines11020284.
@article{article, author = {Baressi \v{S}egota, Sandi and Lorencin, Ivan and Kova\v{c}, Zoran and Car, Zlatan}, year = {2023}, pages = {22}, DOI = {https:// doi.org/10.3390/biomedicines11020284}, chapter = {284}, keywords = {artificial neural networks, convolutional neural networks, machine learning, pIC50, regression modeling, SMILES}, journal = {Biomedicines}, doi = {https:// doi.org/10.3390/biomedicines11020284}, volume = {11}, number = {2}, issn = {2227-9059}, title = {On Approximating the pIC50 Value of COVID-19 Medicines In Silico with Artificial Neural Networks}, keyword = {artificial neural networks, convolutional neural networks, machine learning, pIC50, regression modeling, SMILES}, chapternumber = {284} }
@article{article, author = {Baressi \v{S}egota, Sandi and Lorencin, Ivan and Kova\v{c}, Zoran and Car, Zlatan}, year = {2023}, pages = {22}, DOI = {https:// doi.org/10.3390/biomedicines11020284}, chapter = {284}, keywords = {artificial neural networks, convolutional neural networks, machine learning, pIC50, regression modeling, SMILES}, journal = {Biomedicines}, doi = {https:// doi.org/10.3390/biomedicines11020284}, volume = {11}, number = {2}, issn = {2227-9059}, title = {On Approximating the pIC50 Value of COVID-19 Medicines In Silico with Artificial Neural Networks}, keyword = {artificial neural networks, convolutional neural networks, machine learning, pIC50, regression modeling, SMILES}, chapternumber = {284} }

Časopis indeksira:


  • Current Contents Connect (CCC)
  • Web of Science Core Collection (WoSCC)
    • Science Citation Index Expanded (SCI-EXP)
    • SCI-EXP, SSCI i/ili A&HCI
  • Scopus


Citati:





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