Molecular dynamics simulations play a crucial role in drug discovery by identifying cryptic and allosteric binding sites, enhancing virtual screening, and predicting small-molecule binding energies. These simulations model atomic-level interactions, revealing how proteins and ligands interact dynamically. However, current methods face challenges such as high computational costs and approximations in force fields, limiting the accuracy and length of simulations. Despite these limitations, advancements in computing power and algorithms are improving the efficiency and reliability of molecular dynamics simulations. The review highlights the importance of protein flexibility in ligand binding, as traditional "lock-and-key" models are outdated. Examples like the acetylcholine binding protein (AChBP) demonstrate how protein conformational changes affect binding. Molecular dynamics simulations help identify these dynamic states, which are essential for drug design. The simulations use force fields to approximate atomic interactions, though they are not perfect and require refinement. To address computational limitations, techniques like accelerated molecular dynamics (aMD) and GPU acceleration are employed. These methods allow for faster simulations, enabling the study of longer time scales and more complex systems. Hybrid approaches combining quantum mechanics and molecular dynamics are also being explored to improve accuracy in systems where quantum effects are significant. Molecular dynamics simulations are also used to improve virtual screening by considering receptor flexibility through the relaxed complex scheme (RCS). This method enhances the identification of potential drug candidates by accounting for multiple conformations of the receptor. Additionally, advanced free-energy calculations using molecular dynamics simulations provide accurate predictions of binding affinities, aiding in drug optimization. Despite these advancements, challenges remain, including the need for better force fields and more extensive conformational sampling. However, ongoing research and technological improvements are making molecular dynamics simulations an increasingly important tool in drug discovery. The future of computer-aided drug design is promising, with molecular dynamics simulations playing a key role in developing novel pharmacological therapeutics.Molecular dynamics simulations play a crucial role in drug discovery by identifying cryptic and allosteric binding sites, enhancing virtual screening, and predicting small-molecule binding energies. These simulations model atomic-level interactions, revealing how proteins and ligands interact dynamically. However, current methods face challenges such as high computational costs and approximations in force fields, limiting the accuracy and length of simulations. Despite these limitations, advancements in computing power and algorithms are improving the efficiency and reliability of molecular dynamics simulations. The review highlights the importance of protein flexibility in ligand binding, as traditional "lock-and-key" models are outdated. Examples like the acetylcholine binding protein (AChBP) demonstrate how protein conformational changes affect binding. Molecular dynamics simulations help identify these dynamic states, which are essential for drug design. The simulations use force fields to approximate atomic interactions, though they are not perfect and require refinement. To address computational limitations, techniques like accelerated molecular dynamics (aMD) and GPU acceleration are employed. These methods allow for faster simulations, enabling the study of longer time scales and more complex systems. Hybrid approaches combining quantum mechanics and molecular dynamics are also being explored to improve accuracy in systems where quantum effects are significant. Molecular dynamics simulations are also used to improve virtual screening by considering receptor flexibility through the relaxed complex scheme (RCS). This method enhances the identification of potential drug candidates by accounting for multiple conformations of the receptor. Additionally, advanced free-energy calculations using molecular dynamics simulations provide accurate predictions of binding affinities, aiding in drug optimization. Despite these advancements, challenges remain, including the need for better force fields and more extensive conformational sampling. However, ongoing research and technological improvements are making molecular dynamics simulations an increasingly important tool in drug discovery. The future of computer-aided drug design is promising, with molecular dynamics simulations playing a key role in developing novel pharmacological therapeutics.