Abstract:

Over the last few years, we have seen a dramatic uptick in the application of Machine Learning in drug discovery. Developments in deep learning have led to a renaissance in Quantitative Structure-Activity Relationships (QSAR) and de-novo molecule generation. While the field continues to advance, it faces several challenges. As with any application of machine learning, the results will depend on the data, the representation, and the algorithms used to generate the machine learning models. In many cases, drug discovery data presents some unique challenges not found in data from other disciplines. Furthermore, the optimal means of representing molecules in machine learning is still an open question. This presentation will highlight current challenges and hopefully motivate new work to move the field forward.

Biography:

Pat Walters heads the Computation & Informatics group at Relay Therapeutics in Cambridge, MA. His group focuses on novel computational methods that integrate computer simulations and experimental data to provide insights that drive drug discovery programs. Pat is co-author of the book “Deep Learning for the Life Sciences,” published by O’Reilly and Associates. His AI work began with expert systems in the late 1980s, moved to machine learning in the 1990s, and has continued through 25 years in the pharmaceutical industry. Before joining Relay, Pat spent more than 20 years at Vertex Pharmaceuticals, where he was Global Head of Modeling & Informatics. He is a member of the editorial advisory board for the Journal of Medicinal Chemistry and has been a guest editor for multiple scientific journals. Pat received his Ph.D. in Organic Chemistry from the University of Arizona, where he studied the application of artificial intelligence in conformational analysis. Before obtaining his Ph.D., he worked at Varian Instruments as both a chemist and a software developer. Pat received his B.S. in Chemistry from the University of California, Santa Barbara.