Wen Ming, Zhang Zhimin, Niu Shaoyu, Sha Haozhi, Yang Ruihan, Yun Yonghuan, Lu Hongmei
College of Chemistry and Chemical Engineering, Central South University , Changsha 410083, PR China.
Institute of Environment and Plant Protection, Chinese Academy of Tropical Agricultural Sciences , Haikou 571101, PR China.
J Proteome Res. 2017 Apr 7;16(4):1401-1409. doi: 10.1021/acs.jproteome.6b00618. Epub 2017 Mar 13.
Identifying interactions between known drugs and targets is a major challenge in drug repositioning. In silico prediction of drug-target interaction (DTI) can speed up the expensive and time-consuming experimental work by providing the most potent DTIs. In silico prediction of DTI can also provide insights about the potential drug-drug interaction and promote the exploration of drug side effects. Traditionally, the performance of DTI prediction depends heavily on the descriptors used to represent the drugs and the target proteins. In this paper, to accurately predict new DTIs between approved drugs and targets without separating the targets into different classes, we developed a deep-learning-based algorithmic framework named DeepDTIs. It first abstracts representations from raw input descriptors using unsupervised pretraining and then applies known label pairs of interaction to build a classification model. Compared with other methods, it is found that DeepDTIs reaches or outperforms other state-of-the-art methods. The DeepDTIs can be further used to predict whether a new drug targets to some existing targets or whether a new target interacts with some existing drugs.
识别已知药物与靶点之间的相互作用是药物重新定位中的一项重大挑战。通过提供最有效的药物 - 靶点相互作用（DTI），药物 - 靶点相互作用的计算机模拟预测可以加快昂贵且耗时的实验工作。DTI的计算机模拟预测还可以提供有关潜在药物 - 药物相互作用的见解，并促进对药物副作用的探索。传统上，DTI预测的性能在很大程度上取决于用于表示药物和靶蛋白的描述符。在本文中，为了准确预测已批准药物与靶点之间的新DTI，而无需将靶点分为不同类别，我们开发了一种基于深度学习的算法框架，名为DeepDTIs。它首先使用无监督预训练从原始输入描述符中提取表示，然后应用已知的相互作用标签对来构建分类模型。与其他方法相比，发现DeepDTIs达到或优于其他现有最先进的方法。DeepDTIs可进一步用于预测新药是否靶向某些现有靶点，或者新靶点是否与某些现有药物相互作用。
