This mini-review theoretically illustrates the in silico methods used in the pharmacy field to enhance drug discovery and development and reduce preclinical studies. It is shown that in silico methods are computational-based approaches that study the structure, properties, and activities of molecules using computer simulations and mathematical algorithms. These results highlight the importance of obtaining data that can affect the prediction of in vivo results. Artificial intelligence and machine learning development enhance in silico methods such as quantitative structure-activity relationship, molecular placement, and physiological-based pharmacokinetics, which are usually used. This approach not only saves time and costs but also offers ease of application. Studies conducted to evaluate the use of in silico methods in areas such as pharmacology, toxicology, and pharmaceuticals are provided as examples. It was concluded that over time, in silico methods usage and development increased due to their ability to predict the in vivo performance of the drug.
Key words: Keywords In silico; in vitro; in vivo; drug development; structure activity relationship
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