  Machine learning for molecules holds great potential for efficiently
exploring the vast chemical space and thus streamlining the drug discovery
process by facilitating the design of new therapeutic molecules. Deep
generative models have shown promising results for molecule generation, but the
benefits of specific inductive biases for learning distributions over small
graphs are unclear. Our study aims to investigate the impact of subgraph
structures and vocabulary design on distribution learning, using small drug
molecules as a case study. To this end, we introduce Subcover, a new
subgraph-based fragmentation scheme, and evaluate it through a two-step
variational auto-encoder. Our results show that Subcover's improved
identification of chemically meaningful subgraphs leads to a relative
improvement of the FCD score by 30%, outperforming previous methods. Our
findings highlight the potential of Subcover to enhance the performance and
scalability of existing methods, contributing to the advancement of drug
discovery.
