.png){kind=icon}
返回
Deep-Learning-Based Drug-Target Interaction Prediction
.png){kind=icon}
PRE
AI
DOI：10.1021/acs.jproteome.6b00618.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
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.
Keyword:
deep learning
deep-delief network
feature extraction
drug-target interaction prediction
semisupervised learning
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
3.6
论文数:
9.4K
被引数:
2.3W
机构
引用论文
Targeting of pericytes diminishes neovascularization and lymphangiogenesis in prostate cancer.png)
The Prostate
The ProstateIF0
PREAI
分享收藏Body Composition Changes in the Subtotally Nephrectomized Rat Fed Differing Dietary ProteinsNephron
NephronIF0
PREAI
分享收藏
查看更多内容