Computational biology has completely changed the paradigm of drug development, moving it from random screening to a logical, predictive science. Three fundamental computational approaches Structure-Based Drug Design (SBDD), Ligand-Based Drug Design (LBDD), and Network Pharmacology are integrated in this review's potent, synergistic framework. In order to uncover important treatment targets, we show how these approaches function together as a coherent pipeline, with Network Pharmacology offering a systems-level blueprint of disease mechanisms. This realization immediately drives LBDD for intelligent screening utilizing pharmacophore and QSAR models in the absence of structural data, and SBDD for atomic-level rational design in the presence of it. Importantly, we stress that the foundation of this integrated strategy is early and iterative in silico ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity) profiling, which guarantees the development of molecules with the best possible safety and drug-likeness. A new era of effective, multifaceted pharmaceutical development is ushered in by this technique, which de-risks the discovery process and speeds up the time from target identification to viable lead candidate by combining different disciplines into a single workflow.

The pharmaceutical industry is undergoing a transformative revolution driven by artificial intelligence, fundamentally reshaping drug discovery and early development processes. This comprehensive review examines how AI technologies from machine learning to deep neural networks are enhancing predictive accuracy and operational efficiency across the entire development pipeline. By analyzing complex biological data, these computational approaches enable unprecedented precision in target identification, lead optimization, and preclinical assessment, significantly accelerating therapeutic development. However, substantial challenges persist in implementation, including data harmonization issues, model interpretability constraints, and integration barriers within existing regulatory frameworks. This analysis critically evaluates both the transformative potential and practical limitations of AI applications, highlighting their capacity to not only streamline development pipelines but also pioneer innovative approaches in personalized medicine and novel therapeutic solutions for complex diseases, while addressing the critical hurdles that must be overcome for successful integration into pharmaceutical research and development.