Today we’re joined by Alpha Lee, Winton Advanced Fellow in the Department of Physics at the University of Cambridge, and Co-Founder of data-driven drug discovery startup, PostEra. Our conversation centers around Alpha’s research which can be broken down into three main categories: data-driven drug discovery, material discovery, and physical analysis of machine learning.  We discuss the similarities and differences between drug discovery and material science, including the parallels in the design test cycle, and the major differences in cost. We also explore the goals associated with uncertainty estimation, why deep networks are easier to optimize than shallow networks, the concept of energy landscape, and how it all fits into his research. We also talk about his startup, PostEra which offers medicinal chemistry as a service powered by machine learning. The complete show notes for this episode can be found at twimlai.com/talk/377.