Shifa Khan; Dahiya S; Nema P; Kashaw SK · 2025 · Bulletin of Pharmaceutical Research
Paper
Drug discovery is a complex and resource-intensive process requiring substantial time and financial investment, often taking 10-15 years and costing billions of dollars to bring a single drug to market. Despite these efforts, success rates in clinical trials remain low, with a significant number of failures attributed to suboptimal pharmacokinetic and toxicity profiles. Computer-aided drug design (CADD) has emerged as a transformative tool in accelerating the drug discovery process by minimizing costs and failures during the later stages of development. CADD encompasses a range of computational approaches, including structure-based drug design (SBDD) and ligand-based drug design (LBDD), which leverage the structural and chemical information of therapeutic targets and ligands to identify and optimize lead molecules. These techniques, combined with advancements in artificial intelligence (AI) and machine learning (ML), have revolutionized the identification of drug candidates by enabling high-throughput virtual screening, molecular docking, quantitative structureactivity relationships (QSAR), and pharmacophore modeling. The availability of three-dimensional structures of proteins through X-ray crystallography, NMR, and computational methods like homology modeling and ab initio modeling has further facilitated target preparation for SBDD. Simultaneously, LBDD capitalizes known ligand interactions to design new molecules with enhanced activity and improved pharmacokinetic properties. This review highlights the principles, methodologies, and recent advancements in CADD, emphasizing its critical role in identifying novel drug candidates. By integrating traditional computational methods with AI and ML, CADD continues to redefine the paradigm of modern drug design, paving the way for innovative, efficient, and targeted therapeutic solutions.
Analysis
Preparing paper insights from the available abstract and paper details.
Source record