Machine learning-driven multi-targeted drug discovery in colon cancer using biomarker signatures

Computational oncology advances multi-targeted therapies for Colon Cancer (CC) by leveraging molecular data and identifying potential drug candidates. However, challenges persist in understanding CC molecular pathways and identifying essential genes. This research integrates biomarker signatures from high-dimensional gene expression, mutation data, and protein interaction networks. The research study employs Adaptive Bacterial Foraging (ABF) optimization to refine search parameters, maximizing the predictive accuracy of therapeutic outcomes. The CatBoost algorithm efficiently classifies patients based on molecular profiles and predicts drug responses. The ABF-CatBoost integration facilitates a multi-targeted therapeutic approach, addressing drug resistance by analyzing mutation patterns, adaptive resistance mechanisms, and conserved binding sites. External validation datasets assess predictive accuracy and generalizability. The results demonstrated that the proposed system outperformed traditional Machine Learning models, such as Support Vector Machine and Random Forest, in terms of accuracy (98.6%), specificity (0.984), sensitivity (0.979), and F1-score (0.978). The model predicts toxicity risks, metabolism pathways, and drug efficacy profiles, ensuring safer and more effective treatments. The artificial intelligence model personalizes therapy by leveraging patient-specific molecular profiles, optimizing drug selection and dosage while minimizing side effects. By altering the biomarker selection and pathway analysis components, this computational framework is modified for other cancers, expanding its application and impact in personalized cancer treatment. It also improves precision medicine in CC therapy, speeding up drug discovery and improving therapeutic outcomes.