Artificial intelligence (AI) has evolved from rule-based decision support systems to advanced neural architectures capable of modeling molecular interactions at atomic resolution. Its integration into pharmaceutical research and drug development has transformed target identification, protein structure modeling, binding prediction, and cryptic pocket detection. This study provides a comprehensive theoretical and translational analysis of AI applications in pharmaceutical research, with a particular focus on protein–ligand modeling, cryptic binding site prediction, and structural learning frameworks. A structured qualitative synthesis of foundational and contemporary literature was conducted, examining AI theory, neural network foundations, public sector implementation models, knowledge management frameworks, and advanced biomolecular modeling approaches. Emphasis was placed on deep learning architectures, geometric modeling techniques, graph neural networks, and trigonometry-aware neural systems applied to drug–protein binding prediction. AI methodologies demonstrate substantial theoretical and translational promise across the drug development continuum. Deep neural networks enable improved prediction of protein–ligand conformations, while graph-based approaches enhance detection of cryptic pockets and dynamic conformational shifts. Geometric deep learning frameworks and trigonometry-aware neural networks significantly improve structural prediction accuracy. AI-driven modeling expands the druggable proteome by identifying hidden allosteric sites and facilitating rational drug design. However, implementation challenges persist in validation, interpretability, regulatory oversight, and ethical governance. AI is redefining pharmaceutical research paradigms by enabling dynamic structural modeling, predictive binding analytics, and discovery of previously inaccessible therapeutic targets. Future development must integrate methodological rigor, transparency, and interdisciplinary collaboration to translate computational insights into clinically viable therapeutics.

Artificial intelligence, drug development, protein–ligand modeling, cryptic pockets, deep learning, pharmaceutical research, structural bioinformatics

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