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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

  • Czech description

Classification

  • Type

    J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database

  • CEP classification

  • OECD FORD branch

    10403 - Physical chemistry

Result continuities

  • Project

  • 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