Role of Computer-Aided Design and Development in Modern Pharmaceuticals

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The integration of molecular biology and computational technologies has transformed modern drug discovery and development processes. Advanced computational methodologies, particularly artificial intelligence (AI) and machine learning (ML), have reshaped traditional drug development approaches through unprecedented access to ligand property data, target binding interactions, three-dimensional protein structures, and virtual libraries containing billions of drug-like molecules. AI and deep learning (DL) implementations have enhanced multiple stages of drug discovery, from target identification to lead optimization. These computational advances, backed by improved hardware capabilities and sophisticated algorithms, now enable targeting of previously "undruggable" proteins. This review presents modern computational approaches in pharmaceutical development, including strategies for challenging protein targets through covalent regulation, allosteric inhibition, protein-protein interaction modulation, and targeted protein degradation. AI-driven methods have accelerated drug discovery pipelines, reduced development costs, and improved clinical trial success rates. The transition from traditional broad-spectrum approaches to precision medicine, supported by computational tools, has enabled personalized therapeutic strategies. Current limitations in computer-aided drug design persist, yet the combination of computational predictions with experimental validation continues to advance therapeutic development. Recent developments in quantum computing and advanced neural networks promise to further enhance drug discovery efficiency and success rates in the coming decades.