In recent times, machine learning being an exciting area, has attracted a lot of studies for its ability to foresee complex chemical and biological properties of chemical compounds used in drug design. This article proposes a machine-learning based quantitative structure-property relationship(QSPR) model for benzenoid hydrocarbons and their physical properties through eigenvalues-based graphical indices. Benzenoid hydrocarbons play a crucial role in drug design and pharmaceutical chemistry due to their stability, aromaticity, and ability to participate in various biological interactions. To validate the results, machine learning technique is applied to predict the properties using various graphical indices. Further, to rank the best hydrocarbon MCDM technique namely SAW is adopted. From the analysis, it is obvious that the best predictive graphical index is Laplacian energy for the property polarizability and the best hydrocarbon is Dibenzo[a,h]pyrene.

A healthy human tear film constitutes proteins, electrolytes, water, lipids, mucins, and vitamins. The lack of production of tear in the eye leads to dry eye disease(DED) which is also termed as “Sjögren’s syndrome”. Utilizing degree-based topological indices on molecular graphs reveal insights into the physicochemical properties of these drugs, such as polar surface area, polarizability, boiling point, enthalpy, molar refraction, molar volume, molecular weight and complexity are crucial in predicting their efficacy in the treatment. Various studies have been carried out in QSPR/QSAR analysis using linear regression and curvilinear regression. In this article, multiple linear regression is applied to find the correlation between seven physicochemical properties and 11 topological indices for dry eye disease drugs. For the considered properties of drugs under the study, Polarizability has shown significant results with high correlation and least RMSE ( R=0 .996 & RMSE=1 .419) using various significant topological indices. The study is extended to evaluate and rank 22 dry eye disease drugs using Multi-Criteria Decision-Making (MCDM) techniques such as TOPSIS and VIKOR. The analysis revealed that Tacrolimus and Cyclosporine being ranked number 1 and number 22 respectively as identified by TOPSIS and VIKOR.