To fight against the present pandemic scenario of COVID-19 outbreak, medication with drugs and vaccines is extremely essential other than ventilation support. In this paper, we present a list of ligands which are expected to have the highest binding affinity with the S-glycoprotein of 2019-nCoV and thus can be used to make the drug for the novel coronavirus. Here, we implemented an architecture using 1D convolutional networks to predict drug–target interaction (DTI) values. The network was trained on the KIBA (Kinase Inhibitor Bioactivity) dataset. With this network, we predicted the KIBA scores (which gives a measure of binding affinity) of a list of ligands against the S-glycoprotein of 2019-nCoV. Based on these KIBA scores, we are proposing a list of ligands (33 top ligands based on best interactions) which have a high binding affinity with the S-glycoprotein of 2019-nCoV and thus can be used for the formation of drugs.

为了应对当前的 COVID-19 疫情大流行，除了通气支持之外，药物和疫苗的治疗也极其重要。在本文中，我们提出了一系列配体，预计与 2019-nCoV 的 S-糖蛋白具有最高的结合亲和力，因此可用于制造新型冠状病毒的药物。在这里，我们实现了一种使用一维卷积网络来预测药物-靶标相互作用 (DTI) 值的架构。该网络在 KIBA（激酶抑制剂生物活性）数据集上进行了训练。通过该网络，我们预测了针对 2019-nCoV 的 S-糖蛋白的一系列配体的 KIBA 评分（给出了结合亲和力的衡量标准）。基于这些 KIBA 评分，我们提出了一系列配体（基于最佳相互作用的 33 个顶级配体），这些配体与 2019-nCoV 的 S-糖蛋白具有高结合亲和力，因此可用于药物的形成。