School of Information Engineering, Xijing University, Xi'an 710123, China.
Biomed Res Int. 2021 Apr 24;2021:9933873. doi: 10.1155/2021/9933873. eCollection 2021.
Identifying the interactions of the drug-target is central to the cognate areas including drug discovery and drug reposition. Although the high-throughput biotechnologies have made tremendous progress, the indispensable clinical trials remain to be expensive, laborious, and intricate. Therefore, a convenient and reliable computer-aided method has become the focus on inferring drug-target interactions (DTIs). In this research, we propose a novel computational model integrating a pyramid histogram of oriented gradients (PHOG), Position-Specific Scoring Matrix (PSSM), and rotation forest (RF) classifier for identifying DTIs. Specifically, protein primary sequences are first converted into PSSMs to describe the potential biological evolution information. After that, PHOG is employed to mine the highly representative features of PSSM from multiple pyramid levels, and the complete describers of drug-target pairs are generated by combining the molecular substructure fingerprints and PHOG features. Finally, we feed the complete describers into the RF classifier for effective prediction. The experiments of 5-fold Cross-Validations (CV) yield mean accuracies of 88.96%, 86.37%, 82.88%, and 76.92% on four golden standard data sets (, , (), and , respectively). Moreover, the paper also conducts the state-of-art light gradient boosting machine (LGBM) and support vector machine (SVM) to further verify the performance of the proposed model. The experimental outcomes substantiate that the established model is feasible and reliable to predict DTIs. There is an excellent prospect that our model is capable of predicting DTIs as an efficient tool on a large scale.
识别药物-靶点的相互作用是包括药物发现和药物重定位在内的相关领域的核心。尽管高通量生物技术已经取得了巨大的进展，但不可避免的临床试验仍然昂贵、费力且复杂。因此，一种方便可靠的计算机辅助方法已成为推断药物-靶点相互作用（DTIs）的焦点。在这项研究中，我们提出了一种新的计算模型，该模型集成了金字塔方向梯度直方图（PHOG）、位置特异性评分矩阵（PSSM）和旋转森林（RF）分类器，用于识别 DTIs。具体来说，首先将蛋白质的一级序列转换为 PSSM，以描述潜在的生物进化信息。然后，使用 PHOG 从多个金字塔层中挖掘 PSSM 的高度代表性特征，并通过组合分子子结构指纹和 PHOG 特征生成药物-靶点对的完整描述符。最后，我们将完整的描述符输入 RF 分类器进行有效预测。在四个黄金标准数据集（,, (), 和 ）上进行的 5 折交叉验证（CV）实验的平均准确率分别为 88.96%、86.37%、82.88%和 76.92%。此外，本文还进行了最先进的轻梯度提升机（LGBM）和支持向量机（SVM）实验，以进一步验证所提出模型的性能。实验结果证实，所建立的模型是可行和可靠的，可用于预测 DTIs。我们的模型有望成为一种高效的大规模预测 DTIs 的工具。
