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Modeling QSPR for pyelonephritis drugs: a topological indices approach using MATLAB

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Abstract

Graph theory serves as a powerful mathematical framework for modeling complex systems, including the chemical structures of pharmaceutical compounds. This study employs degree-based topological indices to analyze the chemical structures of 12 commonly prescribed antibiotics for treating kidney infections. These indices represent numerical values derived from a graph, allowing for the prediction of the physicochemical properties of compounds without the need for laboratory experiments. A computer-based technique and algorithm were employed to streamline calculations and data analysis. The degree-based topological indices were calculated using the MATLAB program. A comparison of their topological indices was conducted to predict the physicochemical characteristics of the selected drugs. Additionally, SPSS software was utilized to develop Quantitative Structure–Property Relationship (QSPR) models, which analyze the correlation between topological indicators and the drugs' physicochemical properties. Various regression models were applied to evaluate the effectiveness of the drugs. Effective predictors were identified, and optimal equations were established based on the highest correlation coefficients and Fisher's test. The study found that the power equation is the most effective method for estimating molar refraction (MR) and polarizability (P) using the Randic ( \({R}_{-1/2}\)) index. Conversely, the cubic equation proved the most reliable technique for estimating (MR) and (P) using the second modified Zagreb (\({}^{m}{M}_{2}\)) and atomic bond connectivity (ABC) indices. The findings highlight the potential of using degree-based topological indices in predicting the physicochemical characteristics of drugs. Identifying effective predictors and optimal equations contributes to understanding the relationship between chemical structure and properties, paving the way for further research in drug design and development.

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Data availability

The data that support the findings of this study are openly available in ChemSpider at [ http://www.chemspider.com/AboutUs.aspx], reference number [64].

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This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

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Authors and Affiliations

  1. Department of Mathematics, Statistics, and Computer Science, Semnan University, Semnan, 35131-19111, Iran

    Mehri Hasani & Masoud Ghods

  2. Research Group MASEP, Research Institute of Sciences and Engineering (RISE), University of Sharjah, Sharjah, 27272, UAE

    Sourav Mondal

  3. Department of Mathematics, COMSATS University Islamabad, Lahore Campus, Pakistan

    Muhammad Kamran Siddiqui & Imran Zulfiqar Cheema

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  1. Mehri Hasani
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  2. Masoud Ghods
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Contributions

All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by MH and MG. The first draft of the manuscript was written by MH and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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Correspondence to Masoud Ghods.

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Hasani, M., Ghods, M., Mondal, S. et al. Modeling QSPR for pyelonephritis drugs: a topological indices approach using MATLAB. J Supercomput 81, 479 (2025). https://doi.org/10.1007/s11227-025-06967-8

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