基于多任务深度学习模型的SARS-CoV-2潜在市售抑制剂预测
Prediction of Potential Commercially Available Inhibitors against SARS-CoV-2 by Multi-Task Deep Learning Model
摘要：
COVID-19的爆发导致全球数百万人死亡，总感染人数仍在上升。有必要确定一些可能有效的药物，可用于预防感染者出现严重症状甚至死亡。幸运的是，已经做出了许多努力，并确定了几种有效的药物。快速增长的数据量对于训练有效和特定的深度学习模型有很大的帮助。在这项研究中，我们提出了一个多任务深度学习模型，用于筛选商业上可用的有效抑制剂SARS-CoV-2。首先，我们在几个异质蛋白质与配体相互作用数据集上预训练了一个模型。该模型在一些基准数据集上取得了有竞争力的结果。接下来，收集了冠状病毒特异性数据集，并用于微调模型。然后，微调模型用于选择商业上可获得的针对SARS-CoV-2蛋白靶点的药物。总体而言，20种化合物被列为潜在的抑制剂。我们进一步探索了模型的可解释性，并观察了预测的重要结合位点。在此基础上，还进行了分子对接，以可视化所选抑制剂的结合模式。
英文摘要：
The outbreak of COVID-19 caused millions of deaths worldwide, and the number of total infections is still rising. It is necessary to identify some potentially effective drugs that can be used to prevent the development of severe symptoms or even death for those infected. Fortunately, many efforts have been made, and several effective drugs have been identified. The rapidly increasing amount of data is of great help for training an effective and specific deep learning model. In this study, we propose a multi-task deep learning model for the purpose of screening commercially available and effective inhibitors against SARS-CoV-2. First, we pretrained a model on several heterogenous protein-ligand interaction datasets. The model achieved competitive results on some benchmark datasets. Next, a coronavirus-specific dataset was collected and used to fine-tune the model. Then, the fine-tuned model was used to select commercially available drugs against SARS-CoV-2 protein targets. Overall, twenty compounds were listed as potential inhibitors. We further explored the model interpretability and observed the predicted important binding sites. Based on this prediction, molecular docking was also performed to visualize the binding modes of the selected inhibitors.
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