Meharban Summeira, Ullah Asad, Zaman Shahid, Hamraz Anila, Razaq Abdul
Department of Mathematical Sciences, Karakoram International University Gilgit, Gilgit, 15100, Pakistan.
Department of Mathematics, University of Sialkot, Sialkot, 51310, Pakistan.
Curr Res Struct Biol. 2024 Feb 29;7:100134. doi: 10.1016/j.crstbi.2024.100134. eCollection 2024.
Research is continuously being pursued to treat cancer patients and prevent the disease by developing new medicines. However, experimental drug design and development is a costly, time-consuming, and challenging process. Alternatively, computational and mathematical techniques play an important role in optimally achieving this goal. Among these mathematical techniques, topological indices (TIs) have many applications in the drugs used for the treatment of breast cancer. TIs can be utilized to forecast the effectiveness of drugs by providing molecular structure information and related properties of the drugs. In addition, these can assist in the design and discovery of new drugs by providing insights into the structure-property/structure-activity relationships. In this article, a Quantitative Structure Property Relationship (QSPR) analysis is carried out using some novel degree-based molecular descriptors and regression models to predict various properties (such as boiling point, melting point, enthalpy, flashpoint, molar refraction, molar volume, and polarizability) of 14 drugs used for the breast cancer treatment. The molecular structures of these drugs are topologically modeled through vertex and edge partitioning techniques of graph theory, and then linear regression models are developed to correlate the computed values with the experimental properties of the drugs to investigate the performance of TIs in predicting these properties. The results confirmed the potential of the considered topological indices as a tool for drug discovery and design in the field of breast cancer treatment.
人们一直在进行研究，通过研发新药来治疗癌症患者并预防该疾病。然而，实验性药物设计与开发是一个成本高昂、耗时且具有挑战性的过程。另外，计算和数学技术在最佳实现这一目标方面发挥着重要作用。在这些数学技术中，拓扑指数（TIs）在用于治疗乳腺癌的药物中有许多应用。拓扑指数可通过提供药物的分子结构信息及相关性质来预测药物的有效性。此外，这些指数还能通过深入了解结构-性质/结构-活性关系，协助设计和发现新药。在本文中，使用一些基于度的新型分子描述符和回归模型进行了定量结构-性质关系（QSPR）分析，以预测14种用于乳腺癌治疗的药物的各种性质（如沸点、熔点、焓、闪点、摩尔折射度、摩尔体积和极化率）。这些药物的分子结构通过图论的顶点和边划分技术进行拓扑建模，然后建立线性回归模型，将计算值与药物的实验性质相关联，以研究拓扑指数在预测这些性质方面的性能。结果证实了所考虑的拓扑指数作为乳腺癌治疗领域药物发现和设计工具的潜力。
