多模态 DTI：基于多模态表示学习的药物-靶点相互作用预测，以弥合新型化学实体与已知异构网络之间的差距。

MultiDTI: drug-target interaction prediction based on multi-modal representation learning to bridge the gap between new chemical entities and known heterogeneous network.

机构信息

Department of Computer Science, Hunan University, Changsha 410082, China.

CAS Key Laboratory of Receptor Research, Drug Discovery and Design Center, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China.

出版信息

Bioinformatics. 2021 Dec 7;37(23):4485-4492. doi: 10.1093/bioinformatics/btab473.

DOI:10.1093/bioinformatics/btab473

PMID:34180970

Abstract

MOTIVATION

Predicting new drug-target interactions is an important step in new drug development, understanding of its side effects and drug repositioning. Heterogeneous data sources can provide comprehensive information and different perspectives for drug-target interaction prediction. Thus, there have been many calculation methods relying on heterogeneous networks. Most of them use graph-related algorithms to characterize nodes in heterogeneous networks for predicting new drug-target interactions (DTI). However, these methods can only make predictions in known heterogeneous network datasets, and cannot support the prediction of new chemical entities outside the heterogeneous network, which hinder further drug discovery and development.

RESULTS

To solve this problem, we proposed a multi-modal DTI prediction model named 'MultiDTI' which uses our proposed joint learning framework based on heterogeneous networks. It combines the interaction or association information of the heterogeneous network and the drug/target sequence information, and maps the drugs, targets, side effects and disease nodes in the heterogeneous network into a common space. In this way, 'MultiDTI' can map the new chemical entity to this learned common space based on the chemical structure of the new entity. That is, bridging the gap between new chemical entities and known heterogeneous network. Our model has strong predictive performance, and the area under the receiver operating characteristic curve of the model is 0.961 and the area under the precision recall curve is 0.947 with 10-fold cross validation. In addition, some predicted new DTIs have been confirmed by ChEMBL database. Our results indicate that 'MultiDTI' is a powerful and practical tool for predicting new DTI, which can promote the development of drug discovery or drug repositioning.

AVAILABILITY AND IMPLEMENTATION

Python codes and dataset are available at https://github.com/Deshan-Zhou/MultiDTI/.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

摘要

动机

预测新的药物-靶标相互作用是新药开发、了解其副作用和药物重定位的重要步骤。异构数据源可为药物-靶标相互作用预测（DTI）提供全面的信息和不同的视角。因此，已经有许多依赖异构网络的计算方法。它们大多使用图相关算法来描述异构网络中的节点，以预测新的药物-靶标相互作用。然而，这些方法只能在已知的异构网络数据集上进行预测，不能支持异构网络之外的新化学实体的预测，这阻碍了进一步的药物发现和开发。

结果

为了解决这个问题，我们提出了一种名为“MultiDTI”的多模态 DTI 预测模型，该模型使用我们基于异构网络的联合学习框架。它结合了异构网络的相互作用或关联信息和药物/靶标序列信息，并将异构网络中的药物、靶标、副作用和疾病节点映射到一个共同的空间中。这样，“MultiDTI”就可以根据新实体的化学结构将新的化学实体映射到这个学习到的共同空间中。也就是说，弥合了新化学实体与已知异构网络之间的差距。我们的模型具有很强的预测性能，模型的接收器操作特征曲线下面积为 0.961，精度召回曲线下面积为 0.947，经过 10 倍交叉验证。此外，一些预测的新 DTIs 已经被 ChEMBL 数据库证实。我们的结果表明，“MultiDTI”是一种强大而实用的预测新 DTI 的工具，可以促进药物发现或药物重定位的发展。