CAS Key Laboratory of Receptor Research, Stake Key Laboratory of Drug Research; Drug Discovery and Design Center, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, 201203, China.
Department of Computer Science, Hunan University, Changsha, 410082, China.
Brief Bioinform. 2022 May 13;23(3). doi: 10.1093/bib/bbac147.
Target prediction and virtual screening are two powerful tools of computer-aided drug design. Target identification is of great significance for hit discovery, lead optimization, drug repurposing and elucidation of the mechanism. Virtual screening can improve the hit rate of drug screening to shorten the cycle of drug discovery and development. Therefore, target prediction and virtual screening are of great importance for developing highly effective drugs against COVID-19. Here we present D3AI-CoV, a platform for target prediction and virtual screening for the discovery of anti-COVID-19 drugs. The platform is composed of three newly developed deep learning-based models i.e., MultiDTI, MPNNs-CNN and MPNNs-CNN-R models. To compare the predictive performance of D3AI-CoV with other methods, an external test set, named Test-78, was prepared, which consists of 39 newly published independent active compounds and 39 inactive compounds from DrugBank. For target prediction, the areas under the receiver operating characteristic curves (AUCs) of MultiDTI and MPNNs-CNN models are 0.93 and 0.91, respectively, whereas the AUCs of the other reported approaches range from 0.51 to 0.74. For virtual screening, the hit rate of D3AI-CoV is also better than other methods. D3AI-CoV is available for free as a web application at http://www.d3pharma.com/D3Targets-2019-nCoV/D3AI-CoV/index.php, which can serve as a rapid online tool for predicting potential targets for active compounds and for identifying active molecules against a specific target protein for COVID-19 treatment.
目标预测和虚拟筛选是计算机辅助药物设计的两种强大工具。靶标鉴定对于发现命中物、优化先导化合物、药物重定位和阐明作用机制具有重要意义。虚拟筛选可以提高药物筛选的命中率，从而缩短药物发现和开发的周期。因此，目标预测和虚拟筛选对于开发针对 COVID-19 的高效药物具有重要意义。在这里，我们提出了 D3AI-CoV，这是一个用于发现抗 COVID-19 药物的靶标预测和虚拟筛选平台。该平台由三个新开发的基于深度学习的模型组成，即 MultiDTI、MPNns-CNN 和 MPNN s-CNN-R 模型。为了将 D3AI-CoV 的预测性能与其他方法进行比较，我们准备了一个名为 Test-78 的外部测试集，其中包含 39 种新发表的独立活性化合物和 39 种来自 DrugBank 的非活性化合物。对于靶标预测，MultiDTI 和 MPNN s-CNN 模型的接收器工作特征曲线（AUC）的面积分别为 0.93 和 0.91，而其他报道的方法的 AUC 范围为 0.51 至 0.74。对于虚拟筛选，D3AI-CoV 的命中率也优于其他方法。D3AI-CoV 可作为一个免费的网络应用程序在 http://www.d3pharma.com/D3Targets-2019-nCoV/D3AI-CoV/index.php 上使用，它可以作为一个快速的在线工具，用于预测活性化合物的潜在靶标，并识别针对 COVID-19 治疗的特定靶标蛋白的活性分子。
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