QSAR Modeling Based on Conformation Ensembles Using a Multi-Instance Learning Approach

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989592%3A15110%2F21%3A73610525" target="_blank" >RIV/61989592:15110/21:73610525 - isvavai.cz</a>

Výsledek na webu

<a href="https://pubs.acs.org/doi/10.1021/acs.jcim.1c00692" target="_blank" >https://pubs.acs.org/doi/10.1021/acs.jcim.1c00692</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1021/acs.jcim.1c00692" target="_blank" >10.1021/acs.jcim.1c00692</a>

Jazyk výsledku

angličtina

Název v původním jazyce

QSAR Modeling Based on Conformation Ensembles Using a Multi-Instance Learning Approach

Popis výsledku v původním jazyce

Modern QSAR approaches have wide practical applications in drug discovery for designing potentially bioactive molecules. If such models are based on the use of 2D descriptors, important information contained in the spatial structures of molecules is lost. The major problem in constructing models using 3D descriptors is the choice of a putative bioactive conformation, which affects the predictive performance. The multi-instance (MI) learning approach considering multiple conformations in model training could be a reasonable solution to the above problem. In this study, we implemented several multi-instance algorithms, both conventional and based on deep learning, and investigated their performance. We compared the performance of MI-QSAR models with those based on the classical single-instance QSAR (SI-QSAR) approach in which each molecule is encoded by either 2D descriptors computed for the corresponding molecular graph or 3D descriptors issued for a single lowest energy conformation. The calculations were carried out on 175 data sets extracted from the ChEMBL23 database. It is demonstrated that (i) MI-QSAR outperforms SI-QSAR in numerous cases and (ii) MI algorithms can automatically identify plausible bioactive conformations.

Název v anglickém jazyce

QSAR Modeling Based on Conformation Ensembles Using a Multi-Instance Learning Approach

Popis výsledku anglicky

Modern QSAR approaches have wide practical applications in drug discovery for designing potentially bioactive molecules. If such models are based on the use of 2D descriptors, important information contained in the spatial structures of molecules is lost. The major problem in constructing models using 3D descriptors is the choice of a putative bioactive conformation, which affects the predictive performance. The multi-instance (MI) learning approach considering multiple conformations in model training could be a reasonable solution to the above problem. In this study, we implemented several multi-instance algorithms, both conventional and based on deep learning, and investigated their performance. We compared the performance of MI-QSAR models with those based on the classical single-instance QSAR (SI-QSAR) approach in which each molecule is encoded by either 2D descriptors computed for the corresponding molecular graph or 3D descriptors issued for a single lowest energy conformation. The calculations were carried out on 175 data sets extracted from the ChEMBL23 database. It is demonstrated that (i) MI-QSAR outperforms SI-QSAR in numerous cases and (ii) MI algorithms can automatically identify plausible bioactive conformations.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10608 - Biochemistry and molecular biology

Projekt

<a href="/cs/project/LTARF18013" target="_blank" >LTARF18013: Zvýšení úspěšnosti primárního skríningu biologicky aktivních látek pomocí výpočetních modelů</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2021

Kód důvěrnosti údajů

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

Název periodika

Journal of Chemical Information and Modeling

ISSN

1549-9596

e-ISSN

—

Svazek periodika

61

Číslo periodika v rámci svazku

10

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

11

Strana od-do

4913-4923

Kód UT WoS článku

000711200000011

EID výsledku v databázi Scopus

2-s2.0-85116592231