Evaluating critical success factors in AI-driven drug discovery using AHP: a strategic framework for optimization

Artificial Intelligence (AI) is reshaping drug discovery by accelerating the identification of therapeutic candidates and reducing development timelines and costs. However, its effectiveness depends on addressing key success factors that influence AI integration. This study presents a novel framework using the Analytic Hierarchy Process (AHP) to systematically evaluate and rank these factors, addressing a crucial gap in strategic planning for AI adoption in pharmaceutical research. The framework comprises six key criteria: Data Quality and Management (DQM), Algorithm Performance and Optimization (APO), Interpretability and Explainability (IE), Regulatory Compliance and Ethical Considerations (RCEC), Computational Efficiency and Scalability (CES), and Validation and Experimental Confirmation (VEC). Expert-driven pairwise comparisons identified Accuracy (ACC), Generalizability (GEN), and Experimental Validation (EV) as top priorities, highlighting the importance of reliable data, robust algorithms, and rigorous validation processes to ensure trustworthy AI outputs. This research contributes to strategic AI adoption by addressing data inconsistencies, algorithmic bias, and scalability limitations. The proposed framework enhances AI applications' efficiency, scalability, and ethical alignment, promoting the development of transparent and reliable drug discovery systems. This comprehensive evaluation is a valuable resource for researchers and industry professionals, facilitating the strategic adoption of AI and bridging the gap between computational predictions and real-world therapeutic outcomes.

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