A Soft Computing Strategy Combining Fuzzy Analytic Hierarchy and Fuzzy Artificial Neural Networks for Predictive Modeling and Therapeutic Ranking in Bowel Cancer Drug Development

Bowel cancer remains a major cause of cancer-related mortality, underscoring the need for improved computational tools to support drug discovery and therapeutic prioritization. This study presents a hybrid fuzzy computational framework that integrates Fuzzy Artificial Neural Networks (Fuzzy ANNs) with the Fuzzy Analytic Hierarchy Process (Fuzzy AHP) to predict anticancer drug properties and rank potential therapeutic candidates. Fuzzy ANNs were employed to model nonlinear relationships between molecular descriptors and anticancer activity, leveraging fuzzy logic to address uncertainty and ambiguity in pharmacological data. The optimized model demonstrated strong predictive performance, achieving an \(R^2=0.92\) and RMSE = 0.18, outperforming the baseline ANN. To complement property prediction, Fuzzy AHP was used to rank candidate drugs under multi-criteria decision-making settings, incorporating efficacy, toxicity, and pharmacokinetic parameters. Consistency and robustness of the ranking process were confirmed using sensitivity analysis, with all comparison matrices satisfying \(CR