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

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.