Artificial intelligence (AI) is revolutionizing the field of drug discovery and development, offering innovative solutions to complex and costly challenges. The increasing digitization of data has propelled the application of AI across various sectors of society, particularly within the pharmaceutical industry, where it has played a pivotal role in optimizing and accelerating the research process. This study aimed to demonstrate the importance of utilizing and comprehending the functional aspects of AI in pharmaceutical research, specifically focusing on drug development. An integrative review was conducted using freely accessible academic platforms, selecting studies addressing the theme of this work published in the last 10 years. AI can be employed throughout the drug development process, from discovery to commercialization, highlighting its impact on the economic and efficient creation of new pharmaceutical agents with desired properties. The combination of AI and drug design technology has facilitated the prediction of pharmaceutical activities, physicochemical properties, pharmacogenetics, distribution, metabolism, and toxicity, as well as quantitative structure-property or structure-activity relationships (QSAR/QSPR). The use of deep learning (DL) methods has driven the evolution of machine learning (ML) techniques, enabling a more precise and efficient analysis of the vast datasets available for drug research and development. While the advancements in AI application in drug development are promising, numerous challenges persist, such as the need for robust datasets for DL training and enhancements in method accuracy. Nevertheless, the utilization of AI in pharmacovigilance and the identification of new therapeutic targets underscore its potential to revolutionize the approach to treating a variety of diseases, fostering significant advancements in public health.
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