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3DProtDTA: a deep learning model for drug-target affinity prediction based on residue-level protein graphs
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SYSNO ASEP 0571080 Druh ASEP J - Článek v odborném periodiku Zařazení RIV J - Článek v odborném periodiku Poddruh J Článek ve WOS Název 3DProtDTA: a deep learning model for drug-target affinity prediction based on residue-level protein graphs Tvůrce(i) Voitsitskyi, T. (GB)
Stratiichuk, R. (GB)
Koleiev, I. (GB)
Popryho, L. (GB)
Ostrovsky, Z. (GB)
Henitsoi, P. (GB)
Khropachov, I. (GB)
Vozniak, V. (GB)
Zhytar, R. (GB)
Nechepurenko, D. (GB)
Yesylevskyy, Semen (UOCHB-X) ORCID
Nafiiev, A. (GB)
Starosyla, S. (GB)Zdroj.dok. RSC Advances. - : Royal Society of Chemistry
Roč. 13, č. 15 (2023), s. 10261-10272Poč.str. 12 s. Jazyk dok. eng - angličtina Země vyd. GB - Velká Británie Klíč. slova drug-target affinity prediction ; protein structure Obor OECD Physical chemistry Způsob publikování Open access Institucionální podpora UOCHB-X - RVO:61388963 UT WOS 000960996800001 EID SCOPUS 85165325224 DOI 10.1039/d3ra00281k Anotace Accurate prediction of the drug-target affinity (DTA) in silico is of critical importance for modern drug discovery. Computational methods of DTA prediction, applied in the early stages of drug development, are able to speed it up and cut its cost significantly. A wide range of approaches based on machine learning were recently proposed for DTA assessment. The most promising of them are based on deep learning techniques and graph neural networks to encode molecular structures. The recent breakthrough in protein structure prediction made by AlphaFold made an unprecedented amount of proteins without experimentally defined structures accessible for computational DTA prediction. In this work, we propose a new deep learning DTA model 3DProtDTA, which utilises AlphaFold structure predictions in conjunction with the graph representation of proteins. The model is superior to its rivals on common benchmarking datasets and has potential for further improvement. Pracoviště Ústav organické chemie a biochemie Kontakt asep@uochb.cas.cz ; Kateřina Šperková, Tel.: 232 002 584 ; Jana Procházková, Tel.: 220 183 418 Rok sběru 2024 Elektronická adresa https://doi.org/10.1039/D3RA00281K
Počet záznamů: 1