Role of topological indices in predictive modeling and ranking of drugs treating eye disorders.

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.