Computational Molecular Biology 2024, Vol.14, No.3, 115-124 http://bioscipublisher.com/index.php/cmb 120 components of traditional medicines for their efficacy against psoriasis. These examples highlight the successful application of computational chemistry in identifying and optimizing drug candidates, ultimately accelerating the drug discovery process. 6.2 Comparison of computational and traditional methods The traditional drug discovery approach is often expensive, time-consuming, and labor-intensive, typically requiring around 12 years and 2.7 billion USD to bring a new drug to market. In contrast, computational methods offer a more efficient and cost-effective alternative. Techniques such as molecular docking, pharmacophore modeling, and quantitative structure-activity relationship (QSAR) models enable the rapid prediction of drug-target interactions and the identification of potential drug candidates (Figure 2) (Sliwoski et al., 2014; Hasan et al., 2022). The study of Hasan et al. (2022) outlines a comprehensive workflow in computer-aided drug design (CADD), demonstrating both structure-based and ligand-based approaches. Key processes include pharmacophore modeling, molecular docking, and molecular dynamics simulations. These simulations help in understanding the interaction between potential drugs and target proteins, allowing for optimization of the drug candidates. The final step, involving MM-GBSA and MM-PBSA methods, provides energy calculations to assess binding affinity, which is crucial in predicting the effectiveness of the drug candidate. This workflow helps streamline drug discovery, reducing time and resources compared to traditional methods. Computational methods also provide a virtual shortcut in the drug discovery pipeline, reducing the need for extensive experimental screening and allowing for the prioritization of the most promising compounds (Leelananda and Lindert, 2016). For instance, virtual high-throughput screening and protein-ligand docking have been successfully employed to predict the binding affinity of compounds, thereby streamlining the drug development process (Lin et al., 2020). Additionally, the use of machine learning and artificial intelligence in computational drug design has further enhanced the predictive accuracy and efficiency of these methods (Decherchi and Cavalli, 2020). While traditional methods rely heavily on experimental assays and animal models, computational approaches can complement these techniques by providing valuable insights into the molecular mechanisms of drug action and potential off-target effects (Agamah et al., 2019). This integration of computational and experimental methods has proven to be a powerful strategy in rational drug design, ultimately leading to more effective and safer therapeutics (Macalino et al., 2015). 7 Future Directions and Emerging Trends 7.1 Integration of computational chemistry with omics data The integration of computational chemistry with omics data represents a significant advancement in drug discovery. Omics technologies, which include genomics, proteomics, and metabolomics, generate vast amounts of data that can be leveraged to identify and validate drug targets more efficiently. Computational platforms that utilize omics data can help in ranking disease-relevant targets by analyzing large datasets, thus expediting the drug discovery process (Paananen and Fortino, 2019). The high-throughput nature of omics technologies allows for the quantitative measurement of numerous putative targets, providing a rich dataset for computational analysis. This integration not only enhances target identification but also aids in understanding the molecular mechanisms underlying diseases, thereby facilitating the development of more effective therapeutic agents. 7.2 Personalized medicine and computational approaches Personalized medicine aims to tailor medical treatment to the individual characteristics of each patient, and computational approaches are pivotal in achieving this goal. By integrating diverse biological data, including genetic, proteomic, and metabolomic information, computational methods can predict individual responses to drugs and identify optimal therapeutic strategies (Niazi and Mariam, 2023). Machine learning and artificial intelligence play crucial roles in analyzing these complex datasets, enabling the prediction of drug efficacy and safety on a personalized level (Yuguda et al., 2023). The convergence of computational chemistry with
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