Artificial intelligence (AI) has developed as a transformative tool in drug discovery development. Answering the limitations of traditional methods which are costly, time-consuming, and related with high rates of failure. With the advent of AI, advancements in machine learning (ML), deep learning (DL), and data-driven modeling have permitted effective assessment of large-scale biological, chemical, and clinical datasets. AI supports multiple phases of drug discovery, that includes target identification, hit discovery, lead optimization, and early prediction of pharmacokinetic and toxicity profiles. Further, AI-driven pipelines enhance decision-making by integrating diverse data and improving predictive accuracy. Techniques like genetic algorithms directly support to lead optimization by producing novel candid leads with better therapeutic potential. The integration of AI in clinical trial design and biomarker detection also helps in the development of personalized medicine. In spite of these benefits, tasks like data quality, model interpretability, and regulatory concerns stay significant. Answering these limitations through explainable AI and large scale data integration approaches is crucial for larger acceptance. Largely, AI has the potential to considerably speed up the drug discovery processes, by lowering the costs, and also enhancing success ratio rates, thereby progressing pharmaceutical research and allowing the development of new, safer and more, efficient therapeutic candid leads. Thus the present review highlights the importance of AI in drug discovery and development.
Pasupuleti Sreenivasa Rao*, Palukuru Sreenivasulu Reddy, Ramalinga Viswakumar (Wed,) studied this question.