The model is validated with the high values of R2 , R2 test and Q2 cv (R2= 0.731, R2 adj =0.641 and Q2 cv=0.51, R2 test =0.666, MSE=0.003).
📌 3 Key Takeaways
🎯
Objective & Hypothesis:The main objective of our study is the modeling of the cytotoxic activity of a series of 17 compounds derived from new thioglycosides of the arylamino-1, 3,4-thiadiazole and glycosyl-1, 3,4-thiadiazole-triazole conjugates against Escherichia coli using 2D-QSAR and some statistical tools (Principal Component Analysis (PCA), Multiple Linear Regression (MLR), This study consists of three main steps: Data set selection, molecular descriptor generation, construction and validation of predictive models for the studied activity.
📊
Key Findings & Data:To build and validate our QSAR model, the dataset was divided into two sets: 17 molecules constitute the training set and the remaining 4 molecules constitute the test set.
💡
Conclusion & Impact:The model is validated with the high values of R2 , R2 test and Q2 cv (R2= 0.731, R2 adj =0.641 and Q2 cv=0.51, R2 test =0.666, MSE=0.003).
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:2D-QSAR · E. coli · RLM
Abstract
The main objective of our study is the modeling of the cytotoxic activity of a series of 17 compounds derived from new thioglycosides of the arylamino-1, 3,4-thiadiazole and glycosyl-1, 3,4-thiadiazole-triazole conjugates against Escherichia coli using 2D-QSAR and some statistical tools (Principal Component Analysis (PCA), Multiple Linear Regression (MLR), This study consists of three main steps: Data set selection, molecular descriptor generation, construction and validation of predictive models for the studied activity. To build and validate our QSAR model, the dataset was divided into two sets: 17 molecules constitute the training set and the remaining 4 molecules constitute the test set. The division of the data set was done by random selection. The training set and the test set were validated separately using internal and external tests, such as the y-randomization and Golbraikh and Trouphsa model validation criteria. The model is validated with the high values of R2 , R2 test and Q2 cv (R2= 0.731, R2 adj =0.641 and Q2 cv=0.51, R2 test =0.666, MSE=0.003).
BADAOUI, H., KOUBI, Y., MOUKHLISS, Y., KHATABİ, K. E., OUABANE, M., CHOUKRAD, M., AJANA, M. A., SBAİ, A., LAKHLIFI, T., BOUACHRINE, M. (2023). Cytotoxic activity of new thioglycosides against Escherichia coli: 2DQSAR study by Multiple Linear Regression method. Turkish Computational and Theoretical Chemistry, Cilt: 7 - Sayı: 1, 51–61. https://ajindex.com/articles/879267
« Synthesis, biological evaluation and 2D-QSAR analysis of benzoxazoles as antimicrobial agents », Eur. J. Med. Chem., vol. 44, no 2, p. 501‑510, févr. 2009, doi: 10.1016/j.ejmech.2008.04.001. [2] Hu, Y.; Li, C.Y.; Wang, X.M.; Yang, Y.H.; Zhu, H.L. 1,3,4-Thiadiazole: Synthesis, Reactions, and Applications in Medicinal, Agricultural, and Materials Chemistry. Chem. Rev. 2014, 114, 5572–5610 [3] El-Sayed, W.A.; Metwally, M.A.; Nada, D.S. Abdel-Rahman A.A.-H. Synthesis and Antimicrobial Activity of New Substituted 5-(Pyridine-3-yl)-1,3,4-thiadiazoles and Their Sugar Derivatives. J. Heterocycl. Chem. 2013, 50, 194–201. [4] Jordheim, L.P.; Durantel, D.; Zoulim, F.; Dumontet, C. Advances in the development of nucleoside and nucleotide analogues for cancer and viral diseases. Nat. Rev. Drug. Disc. 2013, 12, 447–464 [5] “Chemical Structure Drawing Software | ACD/ChemSketch.” https://www.acdlabs.com [6] “ChemAxon - Software Solutions and Services for Chemistry & Biology.” https://chemaxon.com [7] Samir Chtita, Majdouline Larif, Mounir Ghamali, Mohammed Bouachrine, Tahar Lakhlifi, Quantitative structure–activity relationship studies of dibenzo[ a, d ]cycloalkenimine derivatives for non-competitive antagonists of N -methyl- d -aspartate based on density functional theory with electronic and topological descriptors. J. Taibah Univ. Sci. 9: 143–154 (2015). https://doi.org/10.1016/j.jtusci.2014.10.006 [8] XLSTAT | Logiciel statistique pour Excel. https://www.xlstat.com/fr/. [9] M. Ghamali, Samir Chtita, Rachid Hmamouchi, Azeddine Adad, Mohammed Bouachrine, Tahar Lakhlifi, The inhibitory activity of aldose reductase of flavonoid compounds: Combining DFT and QSAR calculations. J. Taibah Univ. Sci. 10: 534–542 (2016). https://doi.org/10.1016/j.jtusci.2015.09.006 [10] Bok-Hee Jung and SoonGohn Kim " A Festival Satisfaction Evaluation Method using Multiple [11] M. Ghamali, et al. QSAR analysis of the toxicity of phenols and thiophenols using MLR and ANN. J. Taibah Univ. Sci. 11: 1–10 (2017). [12] S. Chtita, M. Ghamali, M. Larif, A. Adad, R. Hmammouchi, M. Bouachrine and T. Lakhlifi, Prediction of biological activity of imidazo [1, 2-a] pyrazine derivatives by combining DFT and QSAR results, International Journal of Innovative Research in Science, Engineering and Technology, 2(12): 7951-7962 (2013). [13] Partha Pratim Roy, Somnath Paul, Indrani Mitra, Kunal Roy, On Two Novel Parameters for Validation of Predictive QSAR Models. Molecules 14: 1660–1701 (2009). https://doi.