Dr. Haoda Fu (付灏达) is a Research Fellow and an Enterprise Lead for Machine Learning, Artifificial Intelligence, and Digital Connected Care from Eli Lilly and Company. Dr. Haoda Fu is a Fellow of ASA (American Statistical Association). He is also an adjunct professor of biostatistics department, Indiana university school of medicine. Dr. Fu received his Ph.D. in statistics from University of Wisconsin-Madison in 2007 and joined Lilly after that. Since he joined Lilly, he is very active in statistics methodology research. He has more than 90 publications in the areas, such as Bayesian adaptive design, survival analysis, recurrent event modeling, personalized medicine, indirect and mixed treatment comparison, joint modeling, Bayesian decision making, and rare events analysis. In recent years, his research area focuses on machine learning and artifificial intelligence. His research has been published in various top journals including JASA, JRSS, Biometrics, ACM, IEEE, JAMA, Annals of Internal Medicine etc.. He has been teaching topics of machine learning and AI in large industry conferences including teaching this topic in FDA workshop. He was board of directors for statistics organizations and program chairs, committee chairs such as ICSA, ENAR, and ASA Biopharm session.

Title: AI/ML for drug discovery
Abstract: AI and ML are revolutionizing drug discovery, particularly in the area of de novo drug design. These tools can predict the properties of potential drug candidates and identify promising drug targets by analyzing large amounts of data from various sources. Using AI and ML, researchers can generate new chemical entities with optimized drug-like properties. De novo drug design using AI and ML has led to the discovery of new treatments for cancer, infectious diseases, and metabolic disorders. However, challenges remain, such as the need for high-quality data and the exploration of larger chemical spaces. Despite these challenges, AI and ML have the potential to transform the way new drugs are designed and developed, accelerating the drug discovery process and bringing new treatments to patients faster than ever before.