Idrees Nazeran, Noor Esha, Rashid Saima, Agama Fekadu Tesgera
Department of Mathematics, Government College University Faisalabad, Faisalabad, 38000, Pakistan.
Department of Mathematics, Wollega University, 395, Nekemte, Ethiopia.
Sci Rep. 2025 Jan 8;15(1):1271. doi: 10.1038/s41598-024-81482-z.
Topological indices (TIs) of chemical graphs of drugs hold the potential to compute important properties and biological activities leading to more thoughtful drug design. Here, we considered certain drugs treating eye-related disorders, including cataract, glaucoma, diabetic retinopathy, and macular degeneration. By combining modeling and decision-makings approaches, this study presents a cost-effective way to comprehend the behavior of molecules. First, the topological indices of chemical graphs of molecules are determined, which provides valuable insights into their behavior. These models are first trained using known data and are also validated by the dataset of known properties. Models for quantitative structure property relations (QSPR) are computed using the quadratic regression method. TIs having correlation value greater than 0.7 with properties like molar weight, index of refraction, molar volume, polarizability, and molar refraction are taken in this work. Weights are assigned to different properties of drugs depending upon the correlation of the properties with topological indices. Furthermore, we used the multiple-choice decision-making techniques TOPSIS and SAW, to rank the drugs treating eye disorders to create well-informed selections. We can precisely forecast the behavior of chemicals by utilizing machine learning to analyze large amounts of data. This method may contribute to the discovery of new relevant drugs with desirable properties and helpful in comprehending the effects of chemicals on their efficacy.
药物化学图的拓扑指数（TIs）有潜力计算重要性质和生物活性，从而实现更周全的药物设计。在此，我们考虑了某些治疗眼部相关疾病的药物，包括白内障、青光眼、糖尿病性视网膜病变和黄斑变性。通过结合建模和决策方法，本研究提出了一种经济高效的方式来理解分子行为。首先，确定分子化学图的拓扑指数，这为其行为提供了有价值的见解。这些模型首先使用已知数据进行训练，并通过已知性质的数据集进行验证。使用二次回归方法计算定量结构性质关系（QSPR）模型。在本研究中，选取了与摩尔质量、折射率、摩尔体积、极化率和摩尔折射等性质相关值大于0.7的拓扑指数。根据药物性质与拓扑指数的相关性，为药物的不同性质赋予权重。此外，我们使用多准则决策技术TOPSIS和SAW对治疗眼部疾病的药物进行排名，以做出明智的选择。通过利用机器学习分析大量数据，我们可以精确预测化学物质的行为。该方法可能有助于发现具有理想性质的新相关药物，并有助于理解化学物质对其疗效的影响。
