QSPR modeling and multi-criteria ranking of antiviral drugs using degree-based topological indices, artificial neural networks, and TOPSIS.

In the present research work, graph theory has been used to represent the molecular structures of antiviral drugs corresponding to the influenza strain treatment using degree-based topological descriptors and Laplacian energy to understand their structural and physicochemical behavior. The graph theory-based descriptors are used to construct the quantitative structure property relationship and provide input to an ANN model used to predict various physicochemical properties among the selected antiviral drugs. The comparison shows that the ANN model outperforms the Linear Regression model by demonstrating higher R2 and lower RMSE. The suggested ANN-QSPR model was verified through the 5-fold cross-validation process, showing high prediction efficiency and better robustness regarding various physicochemical properties of antiviral compounds. Moreover, an MCDM approach using the TOPSIS method has been employed to assess and rank the antiviral drugs using both structural and physicochemical aspects. The integrated framework in the present research work offers a comprehensive mathematical and computational platform to perform drug analysis and decision-making in antiviral drug design tasks.