Computer-aided drug design (CADD) has become a crucial tool in drug discovery, offering a targeted approach to identify and optimize compounds for therapeutic use. CADD methods are broadly categorized into structure-based and ligand-based approaches. Structure-based CADD utilizes the three-dimensional structure of target proteins to predict interactions with potential ligands, while ligand-based CADD relies on the chemical properties of known active and inactive compounds to guide the design of new molecules. These methods are essential for reducing the number of compounds that need to be tested experimentally, improving the efficiency of drug discovery, and enhancing the likelihood of identifying compounds with favorable pharmacokinetic and pharmacodynamic properties. Structure-based CADD involves the use of target protein structures to determine how ligands interact with the protein, which is critical for designing compounds that bind effectively to the target. This approach is particularly useful when high-resolution structural data of the target protein is available. Ligand-based CADD, on the other hand, is beneficial when structural information is limited, often for membrane proteins. Both approaches aim to improve the hit rate in drug discovery by focusing on compounds that are more likely to be active, thus reducing the cost and time associated with traditional high-throughput screening (HTS). CADD methods include virtual high-throughput screening (vHTS), which uses computational tools to screen large chemical databases and prioritize compounds for further testing. This approach allows researchers to focus on compounds that are more likely to be active, thereby reducing the number of compounds that need to be synthesized and tested experimentally. Additionally, de novo drug design, which involves the creation of novel compounds from scratch, is another important aspect of CADD. This method uses computational algorithms to generate and optimize new molecules based on their potential to interact with target proteins. The use of CADD in drug discovery has led to the identification of numerous compounds that have been successfully developed into therapeutic agents. These methods have proven to be particularly effective in the discovery of compounds that target specific proteins, such as enzymes involved in disease processes. The integration of CADD with other computational tools, such as pharmacophore modeling and quantitative structure-activity relationship (QSAR) analysis, has further enhanced the ability to predict the activity and properties of potential drug candidates. Overall, CADD has become an indispensable tool in modern drug discovery, offering a more efficient and targeted approach to the identification and optimization of therapeutic compounds.Computer-aided drug design (CADD) has become a crucial tool in drug discovery, offering a targeted approach to identify and optimize compounds for therapeutic use. CADD methods are broadly categorized into structure-based and ligand-based approaches. Structure-based CADD utilizes the three-dimensional structure of target proteins to predict interactions with potential ligands, while ligand-based CADD relies on the chemical properties of known active and inactive compounds to guide the design of new molecules. These methods are essential for reducing the number of compounds that need to be tested experimentally, improving the efficiency of drug discovery, and enhancing the likelihood of identifying compounds with favorable pharmacokinetic and pharmacodynamic properties. Structure-based CADD involves the use of target protein structures to determine how ligands interact with the protein, which is critical for designing compounds that bind effectively to the target. This approach is particularly useful when high-resolution structural data of the target protein is available. Ligand-based CADD, on the other hand, is beneficial when structural information is limited, often for membrane proteins. Both approaches aim to improve the hit rate in drug discovery by focusing on compounds that are more likely to be active, thus reducing the cost and time associated with traditional high-throughput screening (HTS). CADD methods include virtual high-throughput screening (vHTS), which uses computational tools to screen large chemical databases and prioritize compounds for further testing. This approach allows researchers to focus on compounds that are more likely to be active, thereby reducing the number of compounds that need to be synthesized and tested experimentally. Additionally, de novo drug design, which involves the creation of novel compounds from scratch, is another important aspect of CADD. This method uses computational algorithms to generate and optimize new molecules based on their potential to interact with target proteins. The use of CADD in drug discovery has led to the identification of numerous compounds that have been successfully developed into therapeutic agents. These methods have proven to be particularly effective in the discovery of compounds that target specific proteins, such as enzymes involved in disease processes. The integration of CADD with other computational tools, such as pharmacophore modeling and quantitative structure-activity relationship (QSAR) analysis, has further enhanced the ability to predict the activity and properties of potential drug candidates. Overall, CADD has become an indispensable tool in modern drug discovery, offering a more efficient and targeted approach to the identification and optimization of therapeutic compounds.