De novo molecular design with deep molecular generative models for PPI inhibitors

Jianmin Wang, Yanyi Chu, Jiashun Mao, Hyeon Nae Jeon, Haiyan Jin, Amir Zeb, Yuil Jang, Kwang Hwi Cho, Tao Song, Kyoung Tai No

Research output: Contribution to journalArticlepeer-review

Abstract

We construct a protein-protein interaction (PPI) targeted drug-likeness dataset and propose a deep molecular generative framework to generate novel drug-likeness molecules from the features of the seed compounds. This framework gains inspiration from published molecular generative models, uses the key features associated with PPI inhibitors as input and develops deep molecular generative models for de novo molecular design of PPI inhibitors. For the first time, quantitative estimation index for compounds targeting PPI was applied to the evaluation of the molecular generation model for de novo design of PPI-Targeted compounds. Our results estimated that the generated molecules had better PPI-Targeted drug-likeness and drug-likeness. Additionally, our model also exhibits comparable performance to other several state-of-The-Art molecule generation models. The generated molecules share chemical space with iPPI-DB inhibitors as demonstrated by chemical space analysis. The peptide characterization-oriented design of PPI inhibitors and the ligand-based design of PPI inhibitors are explored. Finally, we recommend that this framework will be an important step forward for the de novo design of PPI-Targeted therapeutics.

Original languageEnglish
Article numberbbac285
JournalBriefings in Bioinformatics
Volume23
Issue number4
DOIs
Publication statusPublished - 2022 Jul 1

Bibliographical note

Publisher Copyright:
© 2022 The Author(s). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

All Science Journal Classification (ASJC) codes

  • Information Systems
  • Molecular Biology

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