Computational Molecular Biology 2024, Vol.14, No.3, 115-124 http://bioscipublisher.com/index.php/cmb 116 2 Fundamental Concepts in Computational Chemistry 2.1 Quantum mechanics and molecular modeling Quantum mechanics (QM) and molecular modeling are foundational to computational chemistry, providing detailed insights into the electronic structure and properties of molecules. These methods are crucial for understanding the interactions at the atomic level, which is essential for drug discovery. Hybrid quantum mechanics/molecular mechanics (QM/MM) approaches are particularly valuable as they combine the accuracy of QM with the efficiency of molecular mechanics (MM). This hybrid method allows for the detailed analysis of ligand-receptor interactions, which is critical for predicting the binding affinity and specificity of potential drug candidates (Barbault and Maurel, 2015; Cascella et al., 2015). The continuous improvement in computational power and algorithm design has significantly enhanced the capabilities of QM/MM simulations, making them indispensable tools in modern drug discovery (Engkvist et al., 2018). 2.2 Molecular dynamics and simulations Molecular dynamics (MD) simulations have become a cornerstone in the field of drug discovery due to their ability to provide dynamic structural and energetic information about biomolecular systems. MD simulations explicitly account for the structural flexibility and entropic effects of molecules, allowing for a more accurate estimation of the thermodynamics and kinetics associated with drug-target interactions (Vivo et al., 2016). These simulations are particularly useful for identifying cryptic or allosteric binding sites, enhancing virtual screening methodologies, and predicting small-molecule binding energies (Durrant and McCammon, 2011). Advanced MD techniques, such as free-energy perturbation, metadynamics, and steered MD, are frequently employed to study drug-target binding. These methods help optimize target affinity and drug residence time, which are crucial for improving drug efficacy (Decherchi and Cavalli, 2020). Additionally, MD simulations are instrumental in investigating the pathogenic mechanisms of diseases, drug resistance mechanisms, and the role of water molecules in ligand binding and optimization (Ganesan et al., 2017; Liu et al., 2018). The integration of MD simulations with other computational tools, such as docking and virtual screening, further streamlines the drug discovery process. This integration allows for the rapid evaluation of millions of compounds, significantly reducing the time and cost associated with drug development (Rosales-Hernández and Correa-Basurto, 2015). As computational resources continue to advance, the role of MD simulations in drug discovery is expected to grow, providing even more detailed and accurate insights into molecular interactions (Salo-Ahen et al., 2020). 3 Applications of Computational Chemistry in Drug Discovery 3.1 Structure-based drug design Computational chemistry has revolutionized the field of drug discovery by providing tools and methods that significantly reduce the time and cost associated with the development of new therapeutics. Structure-based drug design (SBDD) relies on the three-dimensional structure of biological targets to identify and optimize potential drug candidates. This approach includes techniques such as ligand docking, pharmacophore modeling, and de novo design. SBDD is analogous to high-throughput screening, where both the target and ligand structures are crucial for the design process (Sliwoski et al., 2014). Enhanced sampling methods like metadynamics have been employed to investigate the complex mechanisms of drug binding to flexible targets, providing insights into the most probable association and dissociation pathways and the related binding free energy profiles (Cavalli et al., 2015). The integration of computational tools with experimental routines has shown a powerful impact on rational drug design, facilitating the identification of promising candidate drugs. 3.2 Ligand-based drug design Ligand-based drug design (LBDD) uses information from known active ligands to predict the activity of new compounds. This method includes techniques such as ligand-based pharmacophores, molecular descriptors, and quantitative structure-activity relationships (QSAR). Machine learning (ML) approaches have been increasingly applied in LBDD to construct models that predict biological activity, optimize hits, and improve the prediction of
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