School of Computer Science and Technology, Wuhan University of Technology, Wuhan, China.
Department of Mathematics, Faculty of Science, Zagazig University, Zagazig, Egypt.
Sci Rep. 2018 Jan 24;8(1):1506. doi: 10.1038/s41598-017-19122-y.
The global prevalence of hepatitis C Virus (HCV) is approximately 3% and one-fifth of all HCV carriers live in the Middle East, where Egypt has the highest global incidence of HCV infection. Quantitative structure-activity relationship (QSAR) models were used in many applications for predicting the potential effects of chemicals on human health and environment. The adaptive neuro-fuzzy inference system (ANFIS) is one of the most popular regression methods for building a nonlinear QSAR model. However, the quality of ANFIS is influenced by the size of the descriptors, so descriptor selection methods have been proposed, although these methods are affected by slow convergence and high time complexity. To avoid these limitations, the antlion optimizer was used to select relevant descriptors, before constructing a nonlinear QSAR model based on the PIC and these descriptors using ANFIS. In our experiments, 1029 compounds were used, which comprised 579 HCVNS5B inhibitors (PIC < ~14) and 450 non-HCVNS5B inhibitors (PIC > ~14). The experimental results showed that the proposed QSAR model obtained acceptable accuracy according to different measures, where [Formula: see text] was 0.952 and 0.923 for the training and testing sets, respectively, using cross-validation, while [Formula: see text] was 0.8822 using leave-one-out (LOO).
全球丙型肝炎病毒 (HCV) 的流行率约为 3%，五分之一的 HCV 携带者居住在中东，其中埃及 HCV 感染的全球发病率最高。定量构效关系 (QSAR) 模型在许多应用中用于预测化学品对人类健康和环境的潜在影响。自适应神经模糊推理系统 (ANFIS) 是用于构建非线性 QSAR 模型的最流行的回归方法之一。然而，ANFIS 的质量受到描述符大小的影响，因此已经提出了描述符选择方法，尽管这些方法受到收敛缓慢和时间复杂度高的影响。为了避免这些限制，使用了蚁狮优化器来选择相关描述符，然后使用 ANFIS 根据 PIC 和这些描述符构建非线性 QSAR 模型。在我们的实验中，使用了 1029 种化合物，其中包括 579 种 HCV NS5B 抑制剂 (PIC < ~14) 和 450 种非 HCV NS5B 抑制剂 (PIC > ~14)。实验结果表明，所提出的 QSAR 模型根据不同的度量标准获得了可接受的准确性，其中交叉验证时训练集和测试集的 [Formula: see text] 分别为 0.952 和 0.923，而使用留一法 (LOO) 时 [Formula: see text] 为 0.8822。
