Evaluating the Impact of AI and ML on Modern Drug Discovery

Review Article

Abstract

Artificial intelligence has several effective applications, ranging from language modelling to pharmaceutical sector enhancement, and it speeds up and lowers the cost of medication research and development. As the amount of drug-related data increases, the deep-learning method has been applied at every stage of the drug development process. A broad overview of artificial intelligence (AI) and its use in medication research and discovery is discussed in this review. Drug metabolism, excretion, and recent advancements in colorectal cancer and tooth loss are discussed, along with the integration of plant-based traditional medicine and the use of computer-aided drug discovery and ligand-based quantitative structure activity and property (QSAR/QSPR) and De Novo drug design. The AI-assisted platform used to discover the serotonin 5-HT1A drug is demonstrated, and it reached the clinical trial in less than 12 months—a significantly shorter time than the conventional method, which requires four years to complete. The challenges, ethical considerations, and future perspectives of AI in drug discovery were also discussed in this review