Generative AI for Drug Discovery

Despite notable strides in drug development, the biopharma sector continues to grapple with the twin burdens of high costs and lengthy timelines associated with research and development (R&D) endeavors. The emergence of AI-driven methodologies in drug discovery has heralded a transformative shift in this landscape, catalyzed by a surge in market entrants, substantial capital injections, and the maturation of technological capabilities.

Over the past decade, investment in AI-driven drug discovery has experienced exponential growth, underpinned by the relentless pursuit of innovation and efficiency gains. Notably, recent breakthroughs in generative AI and the advent of large language models (LLMs) such as ChatGPT have further augmented the potential of AI to revolutionize drug discovery paradigms.

In this presentation, Dr. Wang will embark on a concise exploration of the burgeoning realm of AI-enabled drug discovery, delving into the exhilarating prospects it offers while candidly addressing the inherent challenges. By elucidating the transformative power of AI in reshaping drug discovery processes, we aim to provide insights into the pivotal role of cutting-edge technologies in accelerating pharmaceutical innovation and enhancing patient outcomes.

About the speaker
Yanshan Wang

Yanshan Wang

Vice Chair of Research and Assistant Professor in Health Informatics, University of Pittsburgh; Chair, AMIA NLP Working Group; Founder & CEO of BonafideNLP

Dr. Wang is vice chair of research and assistant professor with a primary appointment in the Department of Health Information Management, and secondary appointments in the Intelligent Systems Program, Department of Biomedical Informatics, Clinical and Translational Science Institute, UPMC Hillman Cancer Center, at the University of Pittsburgh. He is also the current Chair of the American Medical Informatics Association (AMIA) Natural Language Processing Working Group, and the NLP Lead of the National Center for Advancing Translational Sciences (NCATS) Accrual to Clinical Trails (ACT) Network. He has over 10 years of research experience in artificial intelligence (AI), natural language processing ( NLP), and machine learning methodologies and applications in health care. His research goal is to leverage different dimensions of data and data-driven computational approaches to meet the needs of clinicians, researchers, and patients.



Sessions: April 2nd – 3rd 2024
Trainings: April 15th – 19th 2024


Presented by