Reinforcement Learning in Drug Discovery

This chapter explores the training of deep reinforcement learning models for drug discovery, focusing on the actor-critic framework that generates and evaluates drug compounds. It highlights the importance of balancing multiple characteristics in the reward function to create effective drug candidates and discusses challenges like mode collapse in generative models. The chapter concludes by emphasizing the collaboration between computational approaches and chemists to enhance the drug screening process.