Evan Feinberg is a scientist and entrepreneur whose lifelong mission is to radically improve the way we discover new medicines. Dr. Feinberg started Genesis Therapeutics, an a16z-funded, AI-driven drug discovery company, to do just that. Before graduate school, Dr. Feinberg studied Applied Physics as an undergraduate at Yale while working on carbon nanotubes as cancer drug delivery agents at Sloan-Kettering. After grappling with his own physical disability, he experienced first-hand the limits of our current pharmacopeia. At Stanford, Evan invented novel neural network architectures for chemistry while working with Prof. Vijay Pande; their paper on PotentialNet is now the fifth-most read paper in 2019 in ACS Central Science. Dr. Feinberg completed his graduate career while consulting for Merck on deep learning and demonstrating step-change improvement in predicting the properties of drug candidates. Since then, Dr. Feinberg recruited elite software engineers and industry-leading biopharma veterans and drug developers to found Genesis.
The pharmaceutical industry is applying a data science approach combined with active machine learning in various areas which include the integration of experimental work and computational modeling, automation, big data analytics, and informatics. This session will focus on pharma preparations and applications of AI and Machine Learning across drug discovery and development. Various examples will demonstrate how pharma is harnessing the opportunity of large data sets to predict and improve translation of preclinical findings to the specific human disease conditions to be evaluated in clinical studies.