Publication: Interpretable SMILES-based QSAR model of inhibitory activity of sirtuins 1 and 2
Issued Date
2021-09-01
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ISSN
18755402
13862073
13862073
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2-s2.0-85111420566
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Mahidol University
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SCOPUS
Bibliographic Citation
Combinatorial Chemistry and High Throughput Screening. Vol.24, No.8 (2021), 1217-1228
Suggested Citation
Apilak Worachartcheewan, Alla P. Toropova, Andrey A. Toropov, Reny Pratiwi, Virapong Prachayasittikul, Chanin Nantasenamat Interpretable SMILES-based QSAR model of inhibitory activity of sirtuins 1 and 2. Combinatorial Chemistry and High Throughput Screening. Vol.24, No.8 (2021), 1217-1228. doi:10.2174/1386207323666200902141907 Retrieved from: https://repository.li.mahidol.ac.th/handle/123456789/76593
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Title
Interpretable SMILES-based QSAR model of inhibitory activity of sirtuins 1 and 2
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Abstract
Background: Sirtuin 1 (Sirt1) and sirtuin 2 (Sirt2) are NAD+-dependent histone deacetylases which play important functional roles in the removal of the acetyl group of acetyl-lysine substrates. Considering the dysregulation of Sirt1 and Sirt2 as etiological causes of diseases, Sirt1 and Sirt2 are lucrative target proteins for treatment, thus there has been great interest in the development of Sirt1 and Sirt2 inhibitors. Objective: This study compiled the bioactivity data of Sirt1 and Sirt2 for the construction of quantitative structure-activity relationship (QSAR) models in accordance with the OECD principles. Methods: Simplified molecular-input line-entry system (SMILES)-based molecular descriptors were used to characterize the molecular features of inhibitors while the Monte Carlo method of the CORAL software was employed for multivariate analysis. The dataset was subjected to 3 random splits in which each split separated the data into 4 subsets consisting of training, invisible training, calibration, and external sets. Results: Statistical indices for the evaluation of QSAR models suggested the good statistical quality of models of Sirt1 and Sirt2 inhibitors. Furthermore, mechanistic interpretation of molecular substructures that are responsible for modulating the bioactivity (i.e., promoters of increase or decrease of bioactivity) was extracted via the analysis of correlation weights. It exhibited molecular features involved in Sirt1 and Sirt2 inhibitors. Conclusion: It is anticipated that QSAR models presented herein can be useful as guidelines in the rational design of potential Sirt1 and Sirt2 inhibitors for the treatment of Sirtuin-related diseases.