DRUG DEVELOPMENT: ROLE OF GENERATIVE ARTIFICIAL INTELLIGENCE

This article explores the transformation of pharmaceutical research and development by the combination of digital twins and generative artificial intelligence (AI), especially in drug discovery. It highlights various cases that illustrate the increasing use of digital twins of biological systems alongside generative AI algorithms to speed up the identification of new drugs. This approach involves simulating vast chemical landscapes and predicting molecular properties, which facilitates the discovery of novel compounds that may have previously been overlooked. By leveraging computational models and machine learning, researchers can design targeted compounds, refine potential therapy candidates, and simulate their interactions within complex biological environments. This method accelerates the identification of promising drug candidates while predicting their efficacy and safety more efficiently, without extensive in-vivo testing. Furthermore, digital twins allow for a more personalized approach to drug development, enhancing the chances of success for specific patient groups. This cutting-edge methodology presents significant opportunities to streamline drug development processes, lower costs, and ultimately improve patient outcomes by delivering more effective treatments. However, the integration of these technologies also poses challenges, including the necessity for interdisciplinary collaboration and ongoing improvements in AI models, computational capabilities, and data integration. As the field progresses, further innovations will be essential to fully harness the potential of these technologies.