Reinforcement learning for molecular generation

Patrick Riley

Many recent strategies have been proposed for searching the enormous space of small organic molecules. In this talk, I'll review the area and present some recent work from our group: MolDQN (Molecular Deep Q-Networks) and RL-VAE (Reinforcement Learning Variational Autoencoders). In addition to explaining the core ideas and experimental results, I'll show some (unpublished) deeper analysis of the value function approximation in the MolDQN.

Back to Workshop I: From Passive to Active: Generative and Reinforcement Learning with Physics