College of Mathematics and Physics, Qingdao University of Science and Technology, Qingdao, 266061, China; Artificial Intelligence and Biomedical Big Data Research Center, Qingdao University of Science and Technology, Qingdao, 266061, China; School of Computer Science and Technology, Shandong University, Qingdao, 266237, China.
Key Laboratory of Synthetic Biology, CAS Center for Excellence in Molecular Plant Sciences, Chinese Academy of Sciences, Shanghai, 200032, China.
Comput Biol Med. 2021 Sep;136:104676. doi: 10.1016/j.compbiomed.2021.104676. Epub 2021 Jul 29.
Analysis and prediction of drug-target interactions (DTIs) play an important role in understanding drug mechanisms, as well as drug repositioning and design. Machine learning (ML)-based methods for DTIs prediction can mitigate the shortcomings of time-consuming and labor-intensive experimental approaches, while providing new ideas and insights for drug design. We propose a novel pipeline for predicting drug-target interactions, called DNN-DTIs. First, the target information is characterized by a number of features, namely, pseudo-amino acid composition, pseudo position-specific scoring matrix, conjoint triad composition, transition and distribution, Moreau-Broto autocorrelation, and structural features. The drug compounds are subsequently encoded using substructure fingerprints. Next, eXtreme gradient boosting (XGBoost) is used to determine the subset of non-redundant features of importance. The optimal balanced set of sample vectors is obtained by applying the synthetic minority oversampling technique (SMOTE). Finally, a DTIs predictor, DNN-DTIs, is developed based on a deep neural network (DNN) via a layer-by-layer learning scheme. Experimental results indicate that DNN-DTIs achieves better performance than other state-of-the-art predictors with ACC values of 98.78%, 98.60%, 97.98%, 98.24% and 98.00% on Enzyme, Ion Channels (IC), GPCR, Nuclear Receptors (NR) and Kuang's datasets. Therefore, the accurate prediction performance of DNN-DTIs makes it a favored choice for contributing to the study of DTIs, especially drug repositioning.
药物-靶点相互作用（DTIs）的分析和预测在理解药物机制、药物重定位和设计方面起着重要作用。基于机器学习（ML）的 DTIs 预测方法可以弥补耗时耗力的实验方法的不足，同时为药物设计提供新的思路和见解。我们提出了一种新的药物-靶点相互作用预测管道，称为 DNN-DTIs。首先，通过一些特征来描述靶点信息，即伪氨基酸组成、伪位置特异性评分矩阵、联合三联体组成、跃迁和分布、Moreau-Broto 自相关和结构特征。然后，使用子结构指纹对药物化合物进行编码。接下来，使用极端梯度提升（XGBoost）来确定重要的非冗余特征子集。通过应用合成少数过采样技术（SMOTE）获得最佳平衡的样本向量集。最后，通过逐层学习方案，基于深度神经网络（DNN）开发了一种 DTIs 预测器 DNN-DTIs。实验结果表明，DNN-DTIs 在 Enzyme、Ion Channels（IC）、GPCR、Nuclear Receptors（NR）和 Kuang's 数据集上的 ACC 值分别为 98.78%、98.60%、97.98%、98.24%和 98.00%，优于其他最先进的预测器，具有较好的预测性能。因此，DNN-DTIs 的准确预测性能使其成为研究 DTIs 的首选方法，特别是药物重定位。
