Published Paper Details:

By speeding up research, cutting expenses, and opening up new treatment approaches, artificial intelligence (AI) is revolutionizing the field of drug discovery and development. To find pharmacological targets, create lead compounds, forecast pharmacokinetics and toxicity, and improve clinical study designs, AI-driven methods make use of enormous datasets from genomes, proteomics, metabolomics, clinical trials, and empirical data. Cloud computing and big data infrastructures facilitate scalable operations, while machine learning, deep learning, and natural language processing improve the effectiveness of virtual screening, de novo molecule creation, and structure-activity relationship modeling. From hit detection to regulatory filing, end-to-end innovation is made possible by combining AI with automation, robots, and high-throughput experimentation. Notwithstanding its potential, issues with data quality, interpretability, moral application, and regulatory approval of insights generated by AI still exist. To fully realize AI's promise in precision and personalized medicines, interdisciplinary cooperation between academia, industry, and regulatory agencies is necessary, as are ongoing developments and explainable AI.