3DProtDTA: a deep learning model for drug-target affinity prediction based on residue-level protein graphs
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61388963%3A_____%2F23%3A00571080" target="_blank" >RIV/61388963:_____/23:00571080 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.1039/D3RA00281K" target="_blank" >https://doi.org/10.1039/D3RA00281K</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1039/d3ra00281k" target="_blank" >10.1039/d3ra00281k</a>
Alternative languages
Result language
angličtina
Original language name
3DProtDTA: a deep learning model for drug-target affinity prediction based on residue-level protein graphs
Original language description
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.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10403 - Physical chemistry
Result continuities
Project
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Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2023
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
RSC Advances
ISSN
2046-2069
e-ISSN
2046-2069
Volume of the periodical
13
Issue of the periodical within the volume
15
Country of publishing house
GB - UNITED KINGDOM
Number of pages
12
Pages from-to
10261-10272
UT code for WoS article
000960996800001
EID of the result in the Scopus database
2-s2.0-85165325224