  It is common practice for chemists to search chemical databases based on
substructures of compounds for finding molecules with desired properties. The
purpose of de novo molecular generation is to generate instead of search.
Existing machine learning based molecular design methods have no or limited
ability in generating novel molecules that preserves a target substructure. Our
Graph Energy-based Model, or GEM, can fix substructures and generate the rest.
The experimental results show that the GEMs trained from chemistry datasets
successfully generate novel molecules while preserving the target
substructures. This method would provide a new way of incorporating the domain
knowledge of chemists in molecular design.
