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

ABSTRACT: 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 nov

SUBMITTER: Zhang H 

PROVIDER: S-EPMC10143255 | biostudies-literature | 2023 Mar

REPOSITORIES: biostudies-literature