A computational approach to drug design for multiple sclerosis via QSPR modeling, chemical graph theory, and multi-criteria decision analysis.

Multiple sclerosis (MS) is a complex autoimmune disease of the central nervous system with an unknown etiology. While disease-modifying therapies can slow progression, there is a need for more effective treatments. Quantitative structure-activity relationship (QSAR) modeling using topological indices derived from chemical graph theory is a promising approach to rationally design new drugs for MS. Using a linear regression approach, we create models for Quantitative Structure-Property Relations (QSPR), detecting correlations between properties such as enthalpy of vaporization, flash point, molar weight, polarizability, molar volume, and complexity with certain degree related topological indices. We used a dataset related to drugs for MS with known properties for training the model and also for validation. To prioritize the most promising drug candidates, we used multi-criteria decision making based on the predicted properties and topological indices, allowing for more informed decisions. The 12 drug candidates were prioritized using the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) and two Weighted Aggregated Sum Product Assessment (WASPAS) methods. The rankings obtained using TOPSIS, WASPAS methods showed a high level of agreement among the results. This framework can be broadly applied to rationally design new therapeutics for complex diseases.