Importance of Computer-aided Drug Design in Modern Pharmaceutical Research

Background: Computer-Aided Drug Design (CADD) approaches are essential in the drug discovery and development process. Both academic institutions and pharmaceutical and biotechnology corporations utilize them to enhance the efficacy of bioactive compounds. Objective: This study aims to entice researchers by investigating the benefits of Computer-Aided Drug and Design (CADD) and its fundamental principles. The main focus is to speed up the drug discovery process, improve accuracy, and reduce the time and financial resources needed, ultimately making a positive impact on public health. Methods: A comprehensive literature search was conducted using databases such as PubMed and Scopus, focusing on studies published till 2024. The selection of studies was based on their analysis of the connection between contemporary pharmaceutical research and computer-aided drug design, with a focus on both structure-based and ligand-based drug design strategies can include molecular docking, fragment-based drug discovery, de novo drug design, pharmacophore modelling, Quantitative structure-activity relationship, 3D-QSAR, homology modelling, in silico absorption–distribution– metabolism–excretion–toxicity, and machine learning/deep learning. Results: Computer-Aided Drug Design (CADD) approaches are mathematical tools used to modify and measure certain characteristics of possible drug candidates. These methods are implemented in various applications. These encompass a variety of software products that are accessible to the public and can be purchased for corporate use. The CADD method is used at several stages of the drug development process, including as a foundation for chemical synthesis and biological testing. It provides information for the development of future SAR (Structure-Activity Relationship), resulting in enhanced molecules in terms of their activity and ADME (Absorption, Distribution, Metabolism, and Excretion). CADD techniques are predominantly employed to analyze and assess the affinity of large molecules for specific biomolecules, such as DNA, RNA, proteins, and enzymes, which serve exclusively as receptors. CADD improves the selection of lead compounds by predicting various parameters, including drug-likeness, physicochemical properties, pharmacokinetics, and toxicity. The application of CADD in drug modelling is to tackle challenges such as cost and time constraints. Modern computer-assisted drug discovery necessitates conducting virtual screening and high-throughput screening (HTS). Conclusion: Computer-aided drug design plays a crucial role for academic institutions and leading pharmaceutical companies in the development of drugs that enhance potency with the significance of reducing both time and costs.