Lead Optimization in Drug Discovery: Integrating Advanced Computational Approaches for Enhanced Drug Design

The process of drug discovery is complex, lengthy, and expensive; it can take more than a decade to succeed and demands significant investments of financial resources. Despite these efforts, drug candidates still experience high failure rates during clinical trials. Computeraided Drug Design (CADD) has emerged as a powerful tool to overcome these obstacles in pharmaceutical research. CADD is used for drug development, such as target identification, lead discovery, optimisation, ADMET, and drug repurposing. CADD reduces the time and cost of drug discovery. Computational chemistry, bioinformatics, and structural biology are incorporated to predict drug-target interactions. The two major methodologies used to identify and optimise potential drug candidates are Structure-Based Drug Design (SBDD) and Ligand-Based Drug Design (LBDD). Molecular docking, molecular dynamics simulations, Quantitative Structure- Activity Relationship (QSAR) modelling, pharmacophore modelling, and virtual screening are the techniques that allow efficient prediction of binding affinity, molecular behaviour, and biological activity. The recent advent of Artificial Intelligence (AI) has also enhanced CADD tools by screening large datasets, predicting pharmacokinetic properties, and designing new molecules. These developments have boosted the efficiency, accuracy, and success rate of drug discovery. (CADD) reduces time costs, as do the physical and technological capabilities of the field of computational technologies. The integration of AI continues to grow, and CADD is bound to assume a more crucial role in designing safer and more efficient therapeutic agents in the future. In this review, we will discuss the general concepts of CADD, the methodologies of CADD, and CADD in drug discovery.