  The rational design of novel molecules with desired bioactivity is a critical
but challenging task in drug discovery, especially when treating a novel target
family or understudied targets. Here, we propose PGMG, a pharmacophore-guided
deep learning approach for bioactivate molecule generation. Through the
guidance of pharmacophore, PGMG provides a flexible strategy to generate
bioactive molecules with structural diversity in various scenarios using a
trained variational autoencoder. We show that PGMG can generate molecules
matching given pharmacophore models while maintaining a high level of validity,
uniqueness, and novelty. In the case studies, we demonstrate the application of
PGMG to generate bioactive molecules in ligand-based and structure-based drug
de novo design, as well as in lead optimization scenarios. Overall, the
flexibility and effectiveness of PGMG make it a useful tool for accelerating
the drug discovery process.
