Qureshi Abid, Kaur Gazaldeep, Kumar Manoj
Bioinformatics Centre, Institute of Microbial Technology, Council of Scientific and Industrial Research, Chandigarh, India.
Chem Biol Drug Des. 2017 Jan;89(1):74-83. doi: 10.1111/cbdd.12834. Epub 2016 Sep 9.
Viral infections constantly jeopardize the global public health due to lack of effective antiviral therapeutics. Therefore, there is an imperative need to speed up the drug discovery process to identify novel and efficient drug candidates. In this study, we have developed quantitative structure-activity relationship (QSAR)-based models for predicting antiviral compounds (AVCs) against deadly viruses like human immunodeficiency virus (HIV), hepatitis C virus (HCV), hepatitis B virus (HBV), human herpesvirus (HHV) and 26 others using publicly available experimental data from the ChEMBL bioactivity database. Support vector machine (SVM) models achieved a maximum Pearson correlation coefficient of 0.72, 0.74, 0.66, 0.68, and 0.71 in regression mode and a maximum Matthew's correlation coefficient 0.91, 0.93, 0.70, 0.89, and 0.71, respectively, in classification mode during 10-fold cross-validation. Furthermore, similar performance was observed on the independent validation sets. We have integrated these models in the AVCpred web server, freely available at http://crdd.osdd.net/servers/avcpred. In addition, the datasets are provided in a searchable format. We hope this web server will assist researchers in the identification of potential antiviral agents. It would also save time and cost by prioritizing new drugs against viruses before their synthesis and experimental testing.
由于缺乏有效的抗病毒治疗方法，病毒感染一直威胁着全球公共卫生。因此，迫切需要加快药物研发进程，以确定新的高效候选药物。在本研究中，我们利用从ChEMBL生物活性数据库获得的公开实验数据，开发了基于定量构效关系（QSAR）的模型，用于预测针对人类免疫缺陷病毒（HIV）、丙型肝炎病毒（HCV）、乙型肝炎病毒（HBV）、人类疱疹病毒（HHV）等26种致命病毒的抗病毒化合物（AVC）。在10折交叉验证中，支持向量机（SVM）模型在回归模式下的最大皮尔逊相关系数分别为0.72、0.74、0.66、0.68和0.71，在分类模式下的最大马修斯相关系数分别为0.91、0.93、0.70、0.89和0.71。此外，在独立验证集上也观察到了类似的性能。我们已将这些模型集成到AVCpred网络服务器中，可在http://crdd.osdd.net/servers/avcpred免费获取。此外，数据集以可搜索的格式提供。我们希望这个网络服务器能帮助研究人员识别潜在的抗病毒药物。它还将通过在合成和实验测试之前对针对病毒的新药进行优先排序，节省时间和成本。
