Prediction framework for drug-target interactions with multi-source descriptors fusion

CHENG Zhixing; DING Yanrui

doi:10.16152/j.cnki.xdxbzr.2023-05-010

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Prediction framework for drug-target interactions with multi-source descriptors fusion

更新时间：2023-10-30

- Prediction framework for drug-target interactions with multi-source descriptors fusion
- Journal of Northwest University (Natural Science Edition) Vol. 53, Issue 5, Pages: 782-793(2023)
- 作者机构：
  
  1.江南大学　江苏省媒体设计与软件技术重点实验室，江苏　无锡　214122
  2.江南大学　理学院，江苏　无锡　214122
- 作者简介：
- 基金信息：
- DOI：10.16152/j.cnki.xdxbzr.2023-05-010
  CLC：
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CHENG Zhixing, DING Yanrui. Prediction framework for drug-target interactions with multi-source descriptors fusion. [J]. Journal of Northwest University (Natural Science Edition) 53(5):782-793(2023)
DOI：

CHENG Zhixing, DING Yanrui. Prediction framework for drug-target interactions with multi-source descriptors fusion. [J]. Journal of Northwest University (Natural Science Edition) 53(5):782-793(2023) DOI： 10.16152/j.cnki.xdxbzr.2023-05-010.

摘要

识别药物靶标相互作用(DTI)是药物发现的一项关键任务，在虚拟筛选、药物重定位和识别药物副作用等领域都发挥了至关重要的作用。通过传统的生物实验方法识别DTI通常昂贵且耗时，随着深度学习在自然语言处理等领域取得的巨大成功，已有许多研究利用深度学习预测潜在的DTI。然而，先前的许多研究仅利用药物和蛋白的单视角特征预测DTI，忽视了多源描述符的潜在价值。该文提出了一种融合多源描述符的药物靶标相互作用预测框架(DFDTI)，充分利用药物和蛋白不同视角的结构信息。首先，通过全连接(FC)层为多源描述符生成低维表示；然后，考虑到不同类型的描述符对DTI预测的贡献度不同，利用通道注意力机制给予不同的描述符权重；此外，使用单层Transformer编码器增强描述符的特征表示；最后，拼接药物和蛋白的增强特征表示，并输入到深度神经网络(DNN)中以预测DTI。实验结果表明，DFDTI能够有效融合不同类型的描述符，在3类评价指标上均优于该文中所有的基线方法。

Abstract

Identifying drug-target interactions (DTIs) is a key task in drug discovery, and it plays an important role in the fields of virtual screening, drug repurposing and identification of potential drug side effects. Traditional biological experimental methods of identifying DTIs are often expensive and time-consuming. With the great success of deep learning in natural language processing and other fields, many studies have used deep learning to predict DTIs. However, many previous studies only utilized single-view features of drugs and proteins to predict DTIs, ignoring the potential value of multi-source descriptors. A drug-target interaction prediction framework with multi-source descriptors fusion (DFDTI) is proposed in this paper to make full use of the structural information of drugs and proteins from different view. First, low dimensional representations are generated for the multi-source descriptors through fully connected (FC) layers. Then, considering the different contribution of different types of descriptors, the channel attention mechanism is used to give weights to descriptors. In addition, one layer Transformer encoder is used to enhance the feature representation of descriptors. Finally, the enhanced features representations of drugs and proteins are concatenated and input into the deep neural network (DNN) to predict DTIs. Experimental results show that the DFDTI can effectively fuse different types of descriptors and outperform all baseline methods in this paper on all three types of evaluation metrics.

关键词

药物-靶标相互作用特征融合注意力机制深度学习

Keywords

drug-target interactionfeature fusionattention mechanismdeep learning

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