Abstract: Reinforcement learning is coupled to a deep generative model based on a three-dimensional scaffold model to generate drug candidates targeting a particular protein, building up atom or functional groups from a starting core scaffold. A reward function can use parallel graph neural network models and take the particular protein into account when calculating reward based on criteria such as binding, synthetic accessibility, and the like. In an agent-critic reinforcement learning model, the agent learns to build molecules in three-dimensional space while optimizing the criteria.