Qu Jia, Song Zihao, Cheng Xiaolong, Jiang Zhibin, Zhou Jie
School of Computer Science and Artificial Intelligence, Changzhou University, Changzhou, Jiangsu, China.
School of Computer Science and Engineering, Shaoxing University, Shaoxing, Zhejiang, China.
Front Microbiol. 2023 Aug 22;14:1179414. doi: 10.3389/fmicb.2023.1179414. eCollection 2023.
With the increasingly serious problem of antiviral drug resistance, drug repurposing offers a time-efficient and cost-effective way to find potential therapeutic agents for disease. Computational models have the ability to quickly predict potential reusable drug candidates to treat diseases.
In this study, two matrix decomposition-based methods, i.e., Matrix Decomposition with Heterogeneous Graph Inference (MDHGI) and Bounded Nuclear Norm Regularization (BNNR), were integrated to predict anti-viral drugs. Moreover, global leave-one-out cross-validation (LOOCV), local LOOCV, and 5-fold cross-validation were implemented to evaluate the performance of the proposed model based on datasets of DrugVirus that consist of 933 known associations between 175 drugs and 95 viruses.
The results showed that the area under the receiver operating characteristics curve (AUC) of global LOOCV and local LOOCV are 0.9035 and 0.8786, respectively. The average AUC and the standard deviation of the 5-fold cross-validation for DrugVirus datasets are 0.8856 ± 0.0032. We further implemented cross-validation based on MDAD and aBiofilm, respectively, to evaluate the performance of the model. In particle, MDAD (aBiofilm) dataset contains 2,470 (2,884) known associations between 1,373 (1,470) drugs and 173 (140) microbes. In addition, two types of case studies were carried out further to verify the effectiveness of the model based on the DrugVirus and MDAD datasets. The results of the case studies supported the effectiveness of MHBVDA in identifying potential virus-drug associations as well as predicting potential drugs for new microbes.
随着抗病毒药物耐药性问题日益严重，药物重新利用为寻找疾病的潜在治疗药物提供了一种省时且经济高效的方法。计算模型有能力快速预测治疗疾病的潜在可重新利用药物候选物。
在本研究中，整合了两种基于矩阵分解的方法，即带异构图推理的矩阵分解（MDHGI）和有界核范数正则化（BNNR）来预测抗病毒药物。此外，基于由175种药物和95种病毒之间的933个已知关联组成的DrugVirus数据集，实施了全局留一法交叉验证（LOOCV）、局部LOOCV和五折交叉验证，以评估所提出模型的性能。
结果表明，全局LOOCV和局部LOOCV的受试者工作特征曲线（AUC）下面积分别为0.9035和0.8786。DrugVirus数据集五折交叉验证的平均AUC和标准差为0.8856±0.0032。我们还分别基于MDAD和aBiofilm实施交叉验证，以评估模型的性能。具体而言，MDAD（aBiofilm）数据集包含1373（1470）种药物与173（140）种微生物之间的2470（2884）个已知关联。此外，基于DrugVirus和MDAD数据集进一步开展了两种类型的案例研究，以验证模型的有效性。案例研究结果支持了MHBVDA在识别潜在病毒-药物关联以及预测新微生物的潜在药物方面的有效性。
