Discovery of Potential Inhibitors of SARS-CoV-2 Main Protease by a Transfer Learning Method

The COVID-19 pandemic caused by SARS-CoV-2 remains a global public health threat and has prompted the development of Antiviral therapies. Artificial intelligence may be one of the strategies to facilitate drug development for emerging and re-emerging diseases. The main protease (M^pro) of SARS-CoV-2 is an attractive drug target due to its essential role in the virus life cycle and high conservation among SARS-CoVs. In this study, we used a data augmentation method to boost transfer learning model performance in screening for potential inhibitors of SARS-CoV-2 M^pro. This method appeared to outperform graph convolution neural network, random forest and Chemprop on an external test set. The fine-tuned model was used to screen for a natural compound library and a de novo generated compound library. By combination with Other in silico analysis methods, a total of 27 compounds were selected for experimental validation of anti-M^pro activities. Among all the selected hits, two compounds (gyssypol acetic acid and hyperoside) displayed inhibitory effects against M^pro with IC50 values of 67.6 μM and 235.8 μM, respectively. The results obtained in this study may suggest an effective strategy of discovering potential therapeutic leads for SARS-CoV-2 and Other coronaviruses.

SARS-CoV-2 Mpro; deep learning; drug development; natural compound; transfer learning.