Computer-Aided Drug Design and In-Silico Approaches in Modern Pharmaceutical Research: A Review

AbstractComputer-aided drug design (CADD) and in-silico approaches have revolutionized modern pharmaceutical research by accelerating drug discovery, reducing costs, and improving the efficiency of therapeutic development. These computational strategies enable researchers to identify potential drug candidates, predict pharmacokinetic properties, and optimize lead compounds before experimental testing. Techniques such as molecular docking, quantitative structure–activity relationship (QSAR) analysis, pharmacophore modeling, molecular dynamics simulations, and artificial intelligence-based prediction systems have significantly enhanced drug development pipelines. Structure-based and ligand-based drug design approaches facilitate the identification of biological targets and lead optimization. Furthermore, in-silico ADMET (absorption, distribution, metabolism, excretion, and toxicity) prediction helps reduce drug failure rates. The integration of computational chemistry, bioinformatics, and machine learning is transforming pharmaceutical research toward precision medicine and faster therapeutic innovation. Despite its advantages, challenges such as data accuracy, computational complexity, and experimental validation remain. Overall, computer-aided drug design represents a powerful tool for modern drug discovery, enabling safer, more effective, and cost-efficient pharmaceutical development.