| dc.description.abstract | The way to create new drugs is to map out the hallmarks of a disease by finding small molecules that can modulate the function of known target molecules. Furthermore, there is a need to identify and obtain small molecules with effective pharmacokinetic properties and low toxicity. Drug development is a long and risky process that involves identifying drug candidates, validating the efficacy of candidate molecules, and assessing toxicity. Traditional drug discovery projects are expensive and time-consuming. In silico drug discovery technology has been an important part of the pharmaceutical industry for many years. The main advantages of in silico pharmaceutical technology are affordability and quick returns. Moreover, it can be applied throughout the process from drug screening stage to pretreatment to clinical application stage, which greatly reduces the risk of failure in accurate drug development. Machine Learning Technology: Using existing databases and libraries of synthetic compounds, computational analysis can virtually detect large numbers of compounds. Therefore, ADME-Tox properties of high-potential drug candidates can be rapidly processed. Once a drug candidate is identified, the next step is to study its pharmacokinetic properties, such as ADME-Tox. Thanks to advances in machine learning algorithms and data libraries, ADME-Tox can also be estimated using computational analysis techniques. It is known that 40-60% of currently tested drug candidates are excluded from pre-therapeutic testing thanks to ADME-Tox analysis. Another method, molecular dynamics (MD), is an in silico simulation analysis based on molecular mechanics (MM) to study the movement of specific particles in a model system over time. MD provides information about events such as structural changes, protein folding, and ligand binding or dissociation by simulating interactions between molecules as realistically as possible at the atomic level. In the context of drug design, the response of proteins to various perturbations, including structural changes, phosphorylation, ligand binding, or mutations, can be tested and observed in well-designed models, allowing MD to understand systemic mechanisms of therapy. It is a powerful tool for process. | tr |
