ArticleScientific reports2026
Integrating fuzzy logic and neural networks with multi-criteria decision-making for intelligent evaluation of drug compound design attributes.
Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Authors and funding
5 authors.
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Abstract
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.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.