Monte Carlo optimization-based QSAR modeling of Staphylococcus aureus inhibitory activity of coumarin-1,2,3-triazole hybrids
In this study, 51 coumarin-1,2,3-triazole hybrids with known minimum inhibitory concentration (MIC) values against Staphylococcus aureus were used for the generation of a Monte Carlo based optimized QSAR model on correlations and logic (CORAL) software. The entire dataset was divided into four diffe...
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Serbian Chemical Society
2025-01-01
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| Series: | Journal of the Serbian Chemical Society |
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| Online Access: | https://doiserbia.nb.rs/img/doi/0352-5139/2025/0352-51392400094M.pdf |
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| author | Mishra Krishna N. Upadhyay Harish C. Verma Poonam |
| author_facet | Mishra Krishna N. Upadhyay Harish C. Verma Poonam |
| author_sort | Mishra Krishna N. |
| collection | DOAJ |
| description | In this study, 51 coumarin-1,2,3-triazole hybrids with known minimum inhibitory concentration (MIC) values against Staphylococcus aureus were used for the generation of a Monte Carlo based optimized QSAR model on correlations and logic (CORAL) software. The entire dataset was divided into four different sets, namely the training set (Tr), the invisible training set (iTr), the calibration set (C) and the validation set (V) of three random splits. For each split, five models were generated using various combinations of SMILES, graphs and hybrid optimal descriptors with various connectivity indices. Finally, fifteen models were obtained from three random, non-identical splits. For the best model from each split, the correlation coefficient (r2) ranged from 0.9672 to 0.8693, while the cross-validated correlation coefficient (Q2) ranged from 0.9478 to 0.8250. The mean absolute error (MAE) for the best models was less than 0.065. Additionally, favourable values of the index of ideality of correlation (IIC) and correlation intensity index (CII) were reported, justifying the robustness, reliability and predictive potential of the developed models. Further, good and bad fingerprints were estimated based on correlation weights for structural attributes. |
| format | Article |
| id | doaj-art-308911d280404d69a366459fe0269161 |
| institution | DOAJ |
| issn | 0352-5139 1820-7421 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Serbian Chemical Society |
| record_format | Article |
| series | Journal of the Serbian Chemical Society |
| spelling | doaj-art-308911d280404d69a366459fe02691612025-08-20T02:56:36ZengSerbian Chemical SocietyJournal of the Serbian Chemical Society0352-51391820-74212025-01-01901395210.2298/JSC240330094M0352-51392400094MMonte Carlo optimization-based QSAR modeling of Staphylococcus aureus inhibitory activity of coumarin-1,2,3-triazole hybridsMishra Krishna N.0https://orcid.org/0009-0001-9613-7241Upadhyay Harish C.1https://orcid.org/0000-0002-2545-4530Verma Poonam2https://orcid.org/0009-0001-9591-8927Laboratory of Chemistry, Department of Applied Sciences, Rajkiya Engineering College (Affiliated with Dr. A.P.J. Abdul Kalam Technical University, Lucknow), Churk, Sonbhadra, IndiaLaboratory of Chemistry, Department of Applied Sciences, Rajkiya Engineering College (Affiliated with Dr. A.P.J. Abdul Kalam Technical University, Lucknow), Churk, Sonbhadra, IndiaLaboratory of Chemistry, Department of Applied Sciences, Rajkiya Engineering College (Affiliated with Dr. A.P.J. Abdul Kalam Technical University, Lucknow), Churk, Sonbhadra, IndiaIn this study, 51 coumarin-1,2,3-triazole hybrids with known minimum inhibitory concentration (MIC) values against Staphylococcus aureus were used for the generation of a Monte Carlo based optimized QSAR model on correlations and logic (CORAL) software. The entire dataset was divided into four different sets, namely the training set (Tr), the invisible training set (iTr), the calibration set (C) and the validation set (V) of three random splits. For each split, five models were generated using various combinations of SMILES, graphs and hybrid optimal descriptors with various connectivity indices. Finally, fifteen models were obtained from three random, non-identical splits. For the best model from each split, the correlation coefficient (r2) ranged from 0.9672 to 0.8693, while the cross-validated correlation coefficient (Q2) ranged from 0.9478 to 0.8250. The mean absolute error (MAE) for the best models was less than 0.065. Additionally, favourable values of the index of ideality of correlation (IIC) and correlation intensity index (CII) were reported, justifying the robustness, reliability and predictive potential of the developed models. Further, good and bad fingerprints were estimated based on correlation weights for structural attributes.https://doiserbia.nb.rs/img/doi/0352-5139/2025/0352-51392400094M.pdfcoral softwareantibacterialstructural attributesindex of ideality of correlationcorrelation intensity index |
| spellingShingle | Mishra Krishna N. Upadhyay Harish C. Verma Poonam Monte Carlo optimization-based QSAR modeling of Staphylococcus aureus inhibitory activity of coumarin-1,2,3-triazole hybrids Journal of the Serbian Chemical Society coral software antibacterial structural attributes index of ideality of correlation correlation intensity index |
| title | Monte Carlo optimization-based QSAR modeling of Staphylococcus aureus inhibitory activity of coumarin-1,2,3-triazole hybrids |
| title_full | Monte Carlo optimization-based QSAR modeling of Staphylococcus aureus inhibitory activity of coumarin-1,2,3-triazole hybrids |
| title_fullStr | Monte Carlo optimization-based QSAR modeling of Staphylococcus aureus inhibitory activity of coumarin-1,2,3-triazole hybrids |
| title_full_unstemmed | Monte Carlo optimization-based QSAR modeling of Staphylococcus aureus inhibitory activity of coumarin-1,2,3-triazole hybrids |
| title_short | Monte Carlo optimization-based QSAR modeling of Staphylococcus aureus inhibitory activity of coumarin-1,2,3-triazole hybrids |
| title_sort | monte carlo optimization based qsar modeling of staphylococcus aureus inhibitory activity of coumarin 1 2 3 triazole hybrids |
| topic | coral software antibacterial structural attributes index of ideality of correlation correlation intensity index |
| url | https://doiserbia.nb.rs/img/doi/0352-5139/2025/0352-51392400094M.pdf |
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