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. 2023 Mar 30;15(4):891.
doi: 10.3390/v15040891.

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

Huijun Zhang  1   2 , Boqiang Liang  3 , Xiaohong Sang  1 , Jing An  4 , Ziwei Huang  1   4   5
Affiliations

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

Huijun Zhang et al. Viruses. .

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 (Mpro) 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 Mpro. 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-Mpro activities. Among all the selected hits, two compounds (gyssypol acetic acid and hyperoside) displayed inhibitory effects against Mpro 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.

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

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
t-Distributed stochastic neighbor embedding (t-SNE) analysis of (A) active molecules (magenta) and inactive molecules (cyan) of the original dataset; (B) molecules in five subsets; (C) active molecules in five subsets; (D) molecules in original dataset (cyan) and the independent test dataset (red).
Figure 2
Figure 2
Top ranked 20 natural compounds screened by fine-tuned model. Rankings were from averages of five independent model predictions.
Figure 3
Figure 3
Top ranked 20 de novo generated molecules screened by fine-tuned model. Rankings were from averages of five independent model predictions.
Figure 4
Figure 4
Inhibition of SARS-CoV-2 Mpro. (A) Inhibition percentage of selected compounds at concentrations of 200 μM. (B) Inhibition percentage of selected compounds at concentrations of 40 μM. (C) Representative curves of Boceprevir, compound T2730 and T2844. All data are from at least three independent experiments and shown as mean ± SD.
See this image and copyright information in PMC

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