Transformers for Molecular Graph Generation

Abstract

This work introduces an autoregressive generative model for graphs which is based on the transformer architecture and applied to the domain of molecular graph generation. Utilizing the multi-head self-attention mechanism to directly model distributions over atoms and bonds, it can sample new molecular graphs in an autoregressive manner. The benchmark framework MOSES is used to compare the proposed approach to other state-of-the-art molecule generation models. It is shown that the model is capable of generalizing from the training data to generate novel and realistic molecules.