Automated Molecule Generation Using Deep Q-Learning and Graph Neural Networks

Abstract

The concept of generating molecular structures with specific desirable characteristics underlies some of the crucial problems in drug discovery. In this paper, we present a model which constructs new molecules for specific desired properties. This model uses graph neural networks to generate molecular representations, and combining these representations with Reinforcement Learning architecture (Deep Q-Learning), builds new molecules. We used two different graph neural network architectures: Graph Convolutional Network and Graph Attention Network. We compared the molecular representations obtained from these two models with the Morgan Fingerprint representation in three separate experiments using the same Reinforcement Learning design. These experiments are single-objective optimization (drug-likeness), optimizing the penalized LogP with similarity constraint, and multi-objective optimization (drug-likeness with similarity constraint). The results show that the Reinforcement Learning models trained with molecular representations obtained from graph neural networks are more successful than the model trained with Morgan Fingerprint representation.