COMPUTER-AIDED DRUG DESIGN AND MOLECULAR DOCKING APPLICATIONS

Computer Aided Drug Design (CADD) has emerged as an indispensable approach in modern pharmaceutical research, enabling the rapid identification, optimization, and validation of potential therapeutic molecules with reduced cost and time. The increasing complexity of biological targets and the demand for innovative drugs have driven the integration of computational tools into conventional drug discovery pipelines. CADD involves a combination of structure-based and ligand-based strategies, allowing researchers to explore molecular interactions, predict binding affinities, and assess physicochemical and pharmacokinetic properties in silico. Molecular docking, one of the most widely used computational techniques, plays a central role in predicting the orientation and binding mode of ligands with macromolecular targets, such as proteins or enzymes. This method provides insights into the structural and energetic aspects of drug–receptor interactions, assisting in the selection of promising lead compounds prior to experimental validation. In addition, advancements in computer algorithms, molecular modeling, and high-throughput virtual screening have significantly improved the efficiency of lead discovery and optimization. Machine learning and artificial intelligence techniques are also being incorporated to enhance scoring functions, improve prediction accuracy, and design novel molecules with desirable pharmacological profiles. The combination of CADD with in silico toxicity and ADMET prediction further reduces the risk of late-stage drug failure. Overall, Computer Aided Drug Design and molecular docking have revolutionized early drug discovery, making the process more rational, efficient, and cost-effective, and continue to contribute to innovation in pharmaceutical sciences.