Investigation of strategies for the interclass prediction of the activity of bipharmacophore butyrylcholinesterase inhibitors based on QSAR modeling

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Resumo

Three schemes of interclass prediction of the activity of a number of bipharmacophoric butyrylcholinesterase inhibitors were studied using QSAR modeling. Using machine learning methods (multiple linear regression, random forest, support vector machine and Gaussian process), QSAR models with satisfactory statistical characteristics were constructed. Based on them, rational and random interclass prediction schemes were studied. It was found that these schemes complement each other and their relative efficiency was assessed.

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Sobre autores

V. Grigorev

Institute of Physiologically Active Compounds, Federal Research Center of Problems of Chemical Physics and Medicinal Chemistry of the Russian Academy of Sciences

Autor responsável pela correspondência
Email: beng@ipac.ac.ru
ORCID ID: 0000-0002-5288-3242
Rússia, 142432, Chernogolovka

A. Razdolsky

Institute of Physiologically Active Compounds, Federal Research Center of Problems of Chemical Physics and Medicinal Chemistry of the Russian Academy of Sciences

Email: beng@ipac.ac.ru
ORCID ID: 0000-0002-3389-4659
Rússia, 142432, Chernogolovka

V. Kazachenko

Institute of Physiologically Active Compounds, Federal Research Center of Problems of Chemical Physics and Medicinal Chemistry of the Russian Academy of Sciences

Email: beng@ipac.ac.ru
ORCID ID: 0000-0003-1424-1895
Rússia, 142432, Chernogolovka

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2. Scheme 1.

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3. Fig. 1. The first strategy of interclass forecasting (ICP-1).

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4. Fig. 2. The second strategy of interclass forecasting (ICP-2).

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5. Fig. 3. Frequency of occurrence of group descriptors in QSAR models (MPA-1).

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6. Fig. 4. Frequency of occurrence of group descriptors in QSAR models (MPA-2).

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7. Fig. 5. Relationship between experimental and predicted values ​​of compound activity.

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8. Fig. 6. Frequency of occurrence of group descriptors in QSAR models (MPA-3).

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