Cechanover Institute of Precision and Regenerative Medicine, School of Medicine, The Chinese University of Hong Kong (Shenzhen), Shenzhen 518172, China.
School of Life Sciences, University of Science and Technology of China, Hefei 230026, China.
Viruses. 2023 Mar 30;15(4):891. doi: 10.3390/v15040891.
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) 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. 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 generated compound library. By combination with other in silico analysis methods, a total of 27 compounds were selected for experimental validation of anti-M activities. Among all the selected hits, two compounds (gyssypol acetic acid and hyperoside) displayed inhibitory effects against M 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.
由 SARS-CoV-2 引起的 COVID-19 大流行仍然是全球公共卫生威胁，并促使开发抗病毒疗法。人工智能可能是促进新兴和再现疾病药物开发的策略之一。SARS-CoV-2 的主要蛋白酶（M）是一种有吸引力的药物靶标，因为它在病毒生命周期中起关键作用，并且在 SARS-CoV 中高度保守。在这项研究中，我们使用数据增强方法来提高转移学习模型在筛选 SARS-CoV-2 M 潜在抑制剂方面的性能。该方法在外部测试集中似乎优于图卷积神经网络、随机森林和 Chemprop。经过微调的模型用于筛选天然化合物库和生成的化合物库。通过与其他计算分析方法相结合，共选择了 27 种化合物用于抗 M 活性的实验验证。在所选择的所有命中物中，两种化合物（醋酸绿原酸和金丝桃苷）对 M 表现出抑制作用，IC50 值分别为 67.6 μM 和 235.8 μM。本研究的结果可能为发现 SARS-CoV-2 和其他冠状病毒的潜在治疗性先导物提供了一种有效的策略。
