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

• URL: http://arxiv.org/abs/2207.00821v1
• Date: Sat, 2 Jul 2022 12:31:17 GMT
• Title: PGMG: A Pharmacophore-Guided Deep Learning Approach for Bioactive Molecular Generation
• Authors: Huimin Zhu, Renyi Zhou, Jing Tang, Min Li
• Abstract summary: We propose PGMG, a pharmacophore-guided deep learning approach for bioactivate molecule generation. We show that PGMG can generate molecules matching given pharmacophore models while maintaining a high level of validity, uniqueness, and novelty. Overall, the flexibility and effectiveness of PGMG make it a useful tool for accelerating the drug discovery process.
• Score: 5.168827506745199
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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