A Review on Advances Computer-Aided Drug Design and Its Applications in Drug Discovery

Abstract

Computer-Aided Drug Design (CADD) is a revolutionary method in pharmaceutical research, combining computational chemistry, structural biology, and bioinformatics to accelerate drug discovery and development. The evolution of CADD has significantly reduced the time and resources required in conventional drug development pipelines, which typically span 10-15 years and cost billions of dollars. Modern CADD methodologies encompass structure-based drug design (SBDD), ligand-based drug design (LBDD), molecular dynamics simulations, and virtual screening techniques. These computational approaches enable precise prediction of drug-target interactions, optimization of lead compounds, and evaluation of pharmacokinetic properties. Recent applications of CADD have provided notable successes in developing therapeutics for various diseases, including COVID-19, cancer, and neurological disorders. The integration of artificial intelligence and machine learning algorithms has further enhanced CADD capabilities, particularly in predicting drug-protein interactions and optimizing molecular properties. Despite challenges in scoring functions and protein flexibility predictions, CADD continues to evolve, incorporating quantum mechanical calculations and improved sampling methods. The combination of computational tools and experimental validation has established CADD as an indispensable component in modern drug discovery, offering reduced costs, accelerated development timelines, and improved success rates in clinical trials