Role of Computational Chemistry in Modern Drug Discovery: Recent Advances, Applications, Challenges, and Future Perspectives

The development of a novel therapeutic agent is a time-consuming and resource-intensive endeavor, traditionally requiring extensive chemical synthesis and biological evaluation. A major bottleneck in this pipeline is the efficient selection of viable lead molecules from vast chemical libraries. Computational chemistry has emerged as a cornerstone of modern drug discovery, enabling the in silico evaluation of molecular properties and target-ligand interactions prior to wet-lab experimentation. This review provides a comprehensive overview of key computational methodologies employed across distinct stages of drug discovery. Structure-based approaches, such as molecular docking and molecular dynamics (MD) simulations, offer structural insights into ligand binding modes, binding affinities, and macromolecular stability. Ligand-based strategies, including Quantitative Structure-Activity Relationship (QSAR) modeling and pharmacophore mapping, facilitate the identification of critical structural features essential for biological activity. Additionally, virtual screening (VS) enables high-throughput filtration of candidate compounds, while in silico ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity) profiling aids in the early elimination of pharmacokinetically unfavorable candidates. Recently, the integration of Artificial Intelligence (AI) and Machine Learning (ML)—particularly deep learning and generative algorithms—has advanced molecular property prediction, de novo drug design, and lead optimization. However, computational predictions remain dependent on dataset quality, algorithmic parameters, and underlying physical assumptions, necessitating validation through experimental assays. Herein, we discuss the applications, advantages, structural limitations, and future outlook of computational approaches in pharmaceutical research.