Qin Huiling, Hussain Mazhar, Hanif Muhammad Farhan, Siddiqui Muhammad Kamran, Hussain Zahid, Fiidow Mohamed Abubakar
Department of Rehabilitation Medicine, The Affiliated Hospital of Youjiang Medical University for Nationalities, Baise, China.
Key Laboratory of Research and Development on Clinical Molecular Diagnosis for High-Incidence Diseases of Baise, Baise, Guangxi, China.
Sci Rep. 2025 Feb 1;15(1):3965. doi: 10.1038/s41598-025-88419-0.
In this paper, we discussed the role of topological descriptors in the QSPR modeling of pulmonary cancer drugs. Degree-based topological indices were computed using computational methods driven by Python that are mathematical representations of properties of molecules without physical measurement. These descriptors were analyzed through linear regression models using SPSS software to predict significant physicochemical properties like boiling point, flash point, molar refractivity, and polarizability. The results show excellent correlations between the computed indices and the observed properties, except for flash point, which ascertains the dependability of the approach in QSPR analysis. The integration of computational and mathematical chemistry will make it easier to evaluate drugs because it can assure consistent data for preclinical development. The paper also reveals specific indices that are superior to others regarding predictive accuracy, thus giving a basis for refining the models to suit the individual compound. This review sets the pace for establishing methodologies that are efficient in designing new and efficient treatments against cancer since it gives insight into the strengths and limitations of topological modeling. This work marked the transformation in accelerating the math involved in drug discovery to reduce such research costs.
在本文中，我们讨论了拓扑描述符在肺癌药物定量构效关系（QSPR）建模中的作用。基于度的拓扑指数是使用由Python驱动的计算方法计算得出的，这些方法是分子性质的数学表示，无需进行物理测量。使用SPSS软件通过线性回归模型对这些描述符进行分析，以预测诸如沸点、闪点、摩尔折射率和极化率等重要的物理化学性质。结果表明，除闪点外，计算得出的指数与观察到的性质之间具有极好的相关性，这确定了该方法在QSPR分析中的可靠性。计算化学与数学化学的结合将使药物评估更加容易，因为它可以确保临床前开发数据的一致性。本文还揭示了在预测准确性方面优于其他指数的特定指数，从而为改进模型以适应单个化合物提供了依据。这篇综述为建立高效设计新型抗癌有效疗法的方法奠定了基础，因为它深入了解了拓扑建模的优势和局限性。这项工作标志着在加速药物发现中所涉及的数学运算以降低此类研究成本方面的转变。
