Drug Discovery and Optimization With AI
Dipesh Uike, S. Sugapriya, Manpreet Kaur, Amruta Mahalle, Neha Dhule, P. Selvakumar, T. C. Manjunath · Advances in computational intelligence and robotics book series · 2025
Across many scientific disciplines, and application is in modern development long, costly over a can cost billions of dollars. predictive analytics, are now playing pivotal roles in revolutionizing this process by making. The drug discovery process typically includes generating hypotheses about drug interactions. Using vast datasets derived from genomics, proteomics, chemical libraries, and biomedical literature, AI algorithms can process and analyze complex biological information in a fraction of the time it would take using conventional methods. response to emerging diseases, where speed is critical—as demonstrated during the COVID-19 pandemic.omics data to reveal previously unknown associations between biological markers and diseases. interpret thousands of scientific articles, extracting valuable insights about genes, proteins, and potential disease mechanisms. By integrating this information with experimental data, error experimentation. predictive ability helps researchers filter out unsuitable compounds early, reducing the risk of late-stage failures.