A dual-view deep learning-driven discovery of cinnamoyl anthranilic acid derivatives against orthopoxvirus through targeting host ITGB3.

Authors

Mengyi Xu, Fan Liu, Xiaosa Zhao, Jing Pang, Xixi Guo, Shijiao Feng, Zhiwen Li, Yanan Ni, Yinghong Li, Minghao Yin, Weijin Huang, Danqing Song, Yanxiang Wang

Published in

European journal of medicinal chemistry. Volume 298. Pages 118002. Jul 25, 2025. Epub Jul 25, 2025.

Abstract

The orthopoxvirus genus, particularly the monkeypox virus (MPXV), continues to pose a significant global public health threat. Therefore, the development of novel anti-orthopoxvirus agents remains an urgent priority. Machine learning has proven to be an effective approach for identifying potential drug candidates. In this study, we implemented a dual-view deep learning model that combines BERT and a graph neural network to analyze molecular sequences and structural graphs. The model was trained following a pre-training-then-fine-tuning paradigm and was subsequently applied to identify new molecules with potential anti-orthopoxvirus activity. Notably, a cinnamoyl anthranilic acid derivative (compound 6) was successfully predicted and demonstrated potent anti-orthopoxvirus effects both in vitro and in vivo. Furthermore, integrin subunit beta 3 (ITGB3) has been validated as one of the direct target protein of 6. In conclusion, we established a robust dual-view deep learning model for the discovery of novel anti-orthopoxvirus agents, and compound 6 is a promising candidate for orthopoxvirus treatment via ITGB3 targeting.

PMID:
40749255
Bibliographic data and abstract were imported from PubMed on 02 Aug 2025.

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