论文标题
通过多任务深度学习模型预测潜在的市售抑制剂对SARS-COV-2的预测
Prediction of Potential Commercially Available Inhibitors against SARS-CoV-2 by Multi-Task Deep Learning Model
论文作者
论文摘要
Covid-19的爆发造成了全球数百万次死亡，总感染的数量仍在增加。有必要确定一些可能使用的有效药物，这些药物可用于防止患者的严重症状或什至死亡。幸运的是，已经做出了许多努力，并确定了几种有效的药物。迅速增加的数据对培训有效而特定的深度学习模型有很大帮助。在这项研究中，我们提出了一个多任务深度学习模型，目的是筛选市场可用且有效的SARS-COV-2抑制剂。首先，我们在几个异源蛋白质 - 配体相互作用数据集上预估了一个模型。该模型在某些基准数据集上实现了竞争结果。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，微调模型用于选择针对SARS-COV-2蛋白靶标的市售药物。总体而言，二十种化合物被列为潜在的抑制剂。我们进一步探索了模型的解释性，并观察到了预测的重要结合位点。基于此预测，还进行了分子对接，以可视化所选抑制剂的结合模式。
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.
