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.
@article{arxiv.2102.04600,
title = {Graph Energy-based Model for Substructure Preserving Molecular Design},
author = {Ryuichiro Hataya and Hideki Nakayama and Kazuki Yoshizoe},
journal= {arXiv preprint arXiv:2102.04600},
year = {2021}
}