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Abstract

Pharmaceutical drug discovery is increasingly characterized by heterogeneous data, computationally intensive analysis, uncertain biological outcomes, and complex resource-allocation decisions. Advances in machine learning, artificial intelligence, predictive analytics, knowledge-based systems, and optimization have created new opportunities to support target identification, virtual screening, molecular design, lead optimization, safety prediction, and portfolio prioritization. However, the availability of increasingly sophisticated analytical models does not automatically translate into better strategic decisions. The central challenge is therefore not only how to predict biological or commercial outcomes, but how to transform predictions into explainable, risk-aware, context-sensitive, and actionable decision intelligence. This conceptual and critical literature-based article develops an AI-Integrated Strategic Decision Intelligence (AISDI) framework for pharmaceutical drug discovery. The framework integrates seven layers: data and information management; descriptive, diagnostic, predictive and prescriptive analytics; AI and advanced computation; explainability and knowledge; risk and resilience; strategic decision intelligence; and human governance and action. A continuous feedback loop connects decision outcomes to new data, model updating, knowledge refinement, and subsequent decisions. The framework synthesizes literature on data analytics capabilities, AI-enabled drug discovery, explainable AI, human-AI collaboration, knowledge-guided reasoning, distributed intelligence, cybersecurity, and strategic decision-making. The article contributes a theoretically grounded architecture for moving pharmaceutical organizations from data accumulation toward decision intelligence while maintaining human oversight, transparency, risk awareness, and organizational accountability. The framework provides a basis for future empirical research examining decision quality, model trust, organizational adoption, and human-AI complementarity in pharmaceutical R&D.

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Citation: Ajay Ganipineni (2026) Predictive Analytics and Decision Intelligence in Pharmaceutical Drug Discovery: A Human-Centered Framework for Explainable, Risk-Aware and Resilient AI-Supported R&D. Epistora J. Biomed. Sci. & Res. 1(1), 1-20. Article EJBSR-2026-102