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Scientific paper - Original scientific paper
On Approximating the pIC50 Value of COVID-19 Medicines In Silico with Artificial Neural Networks
Biomedicines, 11 (2023), 2; 1-22. https://doi.org/10.3390/biomedicines11020284
Baressi Šegota, Sandi; Lorencin, Ivan; Kovač, Zoran; Car, Zlatan

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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), 1-22. doi: 10.3390/biomedicines11020284

Baressi Šegota, Sandi, et al. "On Approximating the pIC50 Value of COVID-19 Medicines In Silico with Artificial Neural Networks." Biomedicines, vol. 11, no. 2, 2023, pp. 1-22. https://doi.org/10.3390/biomedicines11020284

Baressi Šegota, Sandi, Ivan Lorencin, Zoran Kovač and Zlatan Car. "On Approximating the pIC50 Value of COVID-19 Medicines In Silico with Artificial Neural Networks." Biomedicines 11, no. 2 (2023): 1-22. https://doi.org/10.3390/biomedicines11020284

Baressi Šegota, S., et al. (2023) 'On Approximating the pIC50 Value of COVID-19 Medicines In Silico with Artificial Neural Networks', Biomedicines, 11(2), pp. 1-22. doi: 10.3390/biomedicines11020284

Baressi Šegota S, Lorencin I, Kovač Z, Car Z. On Approximating the pIC50 Value of COVID-19 Medicines In Silico with Artificial Neural Networks. Biomedicines [Internet]. 2023 [cited 2024 December 24];11(2):1-22. doi: 10.3390/biomedicines11020284

S. Baressi Šegota, I. Lorencin, Z. Kovač and Z. Car, "On Approximating the pIC50 Value of COVID-19 Medicines In Silico with Artificial Neural Networks", Biomedicines, vol. 11, no. 2, pp. 1-22, 2023. [Online]. Available at: https://urn.nsk.hr/urn:nbn:hr:271:218377. [Accessed: 24 December 2024]

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Metadata
Title (english)On Approximating the pIC50 Value of COVID-19 Medicines In Silico with Artificial Neural Networks
AuthorSandi Baressi Šegota
MBZ: 397955
AuthorIvan Lorencin
MBZ: 380265
AuthorZoran Kovač
AuthorZlatan Car
MBZ: 259465
Author's institutionUniversity of Rijeka
(Faculty of Dental Medicine)
Scientific / art field,
discipline and subdiscipline		BIOMEDICINE AND HEALTHCARE
Dental Medicine
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Computing
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Basic Technical Sciences
Scientific / art field,
discipline and subdiscipline		INTERDISCIPLINARY AREAS OF KNOWLEDGE
Biotechnology in Biomedicine (natural science, biomedicine and healthcare, bioethics area
Abstract (english)
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.
Keywords (english)
Languageenglish
Publication typeScientific paper - Original scientific paper
Publication statusPublished
Peer reviewPeer review - international
Publication versionPublished version
Journal titleBiomedicines
Numberingvol. 11, no. 2, pp. 1-22
p-ISSN2227-9059
DOIhttps://doi.org/10.3390/biomedicines11020284
URN:NBNurn:nbn:hr:271:218377
Publication2023
Document URLhttp://bib.irb.hr/1246979
Type of resourceText
Access conditionsOpen access
Terms of useLicence Attribution 4.0 International (CC BY 4.0) icon
Created on2023-01-31 12:03:54