McCoy Kevin, Gudapati Sateesh, He Lawrence, Horlander Elaina, Kartchner David, Kulkarni Soham, Mehra Nidhi, Prakash Jayant, Thenot Helena, Vanga Sri Vivek, Wagner Abigail, White Brandon, Mitchell Cassie S
Laboratory for Pathology Dynamics, Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA 30332, USA.
Computer Science, Georgia Institute of Technology, Atlanta, GA 30332, USA.
Pharmaceutics. 2021 May 26;13(6):794. doi: 10.3390/pharmaceutics13060794.
Link prediction in artificial intelligence is used to identify missing links or derive future relationships that can occur in complex networks. A link prediction model was developed using the complex heterogeneous biomedical knowledge graph, SemNet, to predict missing links in biomedical literature for drug discovery. A web application visualized knowledge graph embeddings and link prediction results using TransE, CompleX, and RotatE based methods. The link prediction model achieved up to 0.44 hits@10 on the entity prediction tasks. The recent outbreak of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), also known as COVID-19, served as a case study to demonstrate the efficacy of link prediction modeling for drug discovery. The link prediction algorithm guided identification and ranking of repurposed drug candidates for SARS-CoV-2 primarily by text mining biomedical literature from previous coronaviruses, including SARS and middle east respiratory syndrome (MERS). Repurposed drugs included potential primary SARS-CoV-2 treatment, adjunctive therapies, or therapeutics to treat side effects. The link prediction accuracy for nodes ranked highly for SARS coronavirus was 0.875 as calculated by human in the loop validation on existing COVID-19 specific data sets. Drug classes predicted as highly ranked include anti-inflammatory, nucleoside analogs, protease inhibitors, antimalarials, envelope proteins, and glycoproteins. Examples of highly ranked predicted links to SARS-CoV-2: human leukocyte interferon, recombinant interferon-gamma, cyclosporine, antiviral therapy, zidovudine, chloroquine, vaccination, methotrexate, artemisinin, alkaloids, glycyrrhizic acid, quinine, flavonoids, amprenavir, suramin, complement system proteins, fluoroquinolones, bone marrow transplantation, albuterol, ciprofloxacin, quinolone antibacterial agents, and hydroxymethylglutaryl-CoA reductase inhibitors. Approximately 40% of identified drugs were not previously connected to SARS, such as edetic acid or biotin. In summary, link prediction can effectively suggest repurposed drugs for emergent diseases.
人工智能中的链接预测用于识别复杂网络中缺失的链接或推导未来可能出现的关系。利用复杂的异构生物医学知识图谱SemNet开发了一种链接预测模型，以预测生物医学文献中用于药物发现的缺失链接。一个网络应用程序使用基于TransE、CompleX和RotatE的方法可视化知识图谱嵌入和链接预测结果。该链接预测模型在实体预测任务中达到了高达0.44的命中率@10。最近爆发的严重急性呼吸综合征冠状病毒2（SARS-CoV-2），也称为新冠肺炎，作为一个案例研究，以证明链接预测模型在药物发现中的有效性。链接预测算法主要通过挖掘包括SARS和中东呼吸综合征（MERS）在内的先前冠状病毒的生物医学文献，指导对SARS-CoV-2重新利用的候选药物进行识别和排名。重新利用的药物包括潜在的SARS-CoV-2主要治疗药物、辅助治疗药物或治疗副作用的药物。根据对现有新冠肺炎特定数据集的人工参与验证计算，SARS冠状病毒排名靠前的节点的链接预测准确率为0.875。预测排名靠前的药物类别包括抗炎药、核苷类似物、蛋白酶抑制剂、抗疟药、包膜蛋白和糖蛋白。与SARS-CoV-2预测排名靠前的链接示例：人白细胞干扰素、重组干扰素-γ、环孢素、抗病毒治疗、齐多夫定、氯喹、疫苗接种、甲氨蝶呤、青蒿素、生物碱、甘草酸、奎宁、黄酮类化合物、安普那韦、苏拉明、补体系统蛋白、氟喹诺酮类、骨髓移植、沙丁胺醇、环丙沙星、喹诺酮类抗菌剂和羟甲基戊二酰辅酶A还原酶抑制剂。大约40%的已识别药物以前未与SARS相关联，如依地酸或生物素。总之，链接预测可以有效地为突发疾病推荐重新利用的药物。
