Authors: Prof. R. Raveendhra
Abstract: Artificial intelligence (AI) is transforming pharmaceutical research by enabling rapid molecular prediction, virtual screening, and biological data integration. However, many current AI systems lack energetic realism and mechanistic interpretability. This manuscript presents a conceptual framework termed Adaptive Quantum-Generative Optimization (AQGO), integrating generative AI, molecular transformers, quantum-mechanical screening, molecular docking, and expert pharmacological validation. The framework is designed to improve lead optimization by combining data-driven molecular generation with physics-based energetic evaluation. The article reviews current advances in AI-driven drug discovery, the role of quantum chemistry in molecular simulation, translational challenges, and future directions for hybrid AI–quantum systems. Emphasis is placed on explainability, reproducibility, ethical deployment, and scientific transparency. The proposed architecture highlights the potential of combining generative intelligence with quantum-mechanical validation to support more efficient and reliable pharmaceutical discovery pipelines.
