Drug Discovery and Computational Chemistry: A State‐of‐the‐Art Review

ABSTRACT Computational chemistry has become a central component of modern drug discovery by enabling rational design strategies that complement traditional experimental approaches. Computer‐aided drug discovery (CADD) integrates molecular modelling techniques such as structure‐based drug design (SBDD), ligand‐based modelling, molecular docking, molecular dynamics simulations, and free energy calculations to accelerate the identification and optimization of therapeutic candidates. In recent years, advances in artificial intelligence (AI) and machine learning (ML) have further expanded the capabilities of computational drug discovery by enabling accurate prediction of molecular properties and exploration of vast chemical spaces. Modern AI‐driven approaches, including graph neural networks and generative models, are increasingly combined with classical physics‐based simulations to improve predictive accuracy and efficiency in drug development workflows. This review provides a state‐of‐the‐art overview of contemporary computational methods used in drug discovery, highlighting both classical CADD techniques and emerging AI‐based strategies. Particular emphasis is placed on the application of these approaches in the discovery of natural product‐derived anticancer agents. Current challenges related to data quality, model interpretability, and computational limitations are also discussed. Overall, the integration of computational chemistry and AI is expected to play an increasingly important role in accelerating drug discovery and advancing precision medicine.