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

Result code in 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>
Result on the web
<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>

Result language
angličtina
Original language name
QSAR Modeling Based on Conformation Ensembles Using a Multi-Instance Learning Approach
Original language description
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.
Czech name
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Czech description
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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
10608 - Biochemistry and molecular biology

Project
<a href="/en/project/LTARF18013" target="_blank" >LTARF18013: Improve the output of primary screening of biologically active compounds using computational models</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year
2021
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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