Integrating fuzzy logic and neural networks with multi-criteria decision-making for intelligent evaluation of drug compound design attributes

This study proposes a fuzzy machine learning framework for optimizing antiepileptic drug selection using Quantitative Structure-Property Relationship (QSPR) modeling under pharmacological uncertainty. Feature relevance was assessed using Random Forest-based importance and SelectKBest with mutual information, and a feedforward neural network was trained with 5-fold cross-validation. Fuzzy membership functions were incorporated to model variability in clinical and experimental data. Compared with Multiple Linear Regression and conventional QSPR models, the proposed approach achieved a 24 percent reduction in RMSE. Predicted pharmacological attributes were further integrated into a Multi-Criteria Decision-Making framework using TOPSIS to rank drug candidates based on efficacy, safety, and cost. The resulting rankings showed 95 percent Spearman correlation with clinician evaluations, demonstrating the framework reliability for uncertainty-aware antiepileptic drug prioritization and custom MCDM libraries for TOPSIS based prioritization.