3DProtDTA: a deep learning model for drug-target affinity prediction based on residue-level protein graphs

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

3DProtDTA: a deep learning model for drug-target affinity prediction based on residue-level protein graphs

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

3DProtDTA: a deep learning model for drug-target affinity prediction based on residue-level protein graphs

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10403 - Physical chemistry

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2023

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

RSC Advances

ISSN

2046-2069

e-ISSN

2046-2069

Svazek periodika

13

Číslo periodika v rámci svazku

15

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

12

Strana od-do

10261-10272

Kód UT WoS článku

000960996800001

EID výsledku v databázi Scopus

2-s2.0-85165325224