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The rational design of novel molecules with desired bioactivity is a criticalbut challenging task in drug discovery, especially when treating a novel targetfamily or understudied targets. Here, we propose PGMG, a pharmacophore-guideddeep learning approach for bioactivate molecule generation. Through theguidance of pharmacophore, PGMG provides a flexible strategy to generatebioactive molecules with structural diversity in various scenarios using atrained variational autoencoder. We show that PGMG can generate moleculesmatching given pharmacophore models while maintaining a high level of validity,uniqueness, and novelty. In the case studies, we demonstrate the application ofPGMG to generate bioactive molecules in ligand-based and structure-based drugde novo design, as well as in lead optimization scenarios. Overall, theflexibility and effectiveness of PGMG make it a useful tool for acceleratingthe drug discovery process.

PGMG: A Pharmacophore-Guided Deep Learning Approach for Bioactive Molecular Generation