Dr. Jon-Paul Janet -- Accelerating drug design with AI & simulation

Описание: In the past few years, deep learning methods for molecular design have made the transition from theoretical research prototypes into practical and commercially important tools in use across the pharmaceutical industry. Here, I will present ReInvent, AstraZeneca’s open-source platform for reinforcement learning guided molecular optimization, focusing on the scientific developments behind it and the ever-increasing connection with physics-based molecular simulations. I will highlight some recent approaches to improve the sample efficiency of the reinforcement learning process, thereby allow for integration with more complex simulation workflows. Finally, I will briefly discuss methods for chemical synthesis planning, and how these various models can work together to power increasingly autonomous systems for drug discovery.

Jon Paul Janet is currently Associate Principal Scientist in the Molecular AI group at AstraZenca in Gothenburg, Sweden. Previously, JP works on early stage drug discovery and has developed machine-learning augmented virtual design strategies for inorganic complexes. He received a Ph.D. in Chemical Engineering and Computational Science and Engineering from the Massachusetts Institute of Technology in 2019 following M.Sc. degrees in Scientific Computing and Applied Mathematics from the Technical University of Berlin and the Royal Institute of Technology in Stockholm both in 2015, as well as a B.Sc. in Chemical Engineering from the University of Cape Town in 2012.