A network integration approach for drug-target interaction prediction and computational drug repositioning from heterogeneous information

By Yunan Luo, Xinbin Zhao, Jingtian Zhou, Jinglin Yang, Yanqing Zhang, Wenhua Kuang, Jian Peng, Ligong Chen, Jianyang Zeng. Nature Communications, 2017

786 citations recorded by the source corpus.

The emergence of large-scale genomic, chemical and pharmacological data provides new opportunities for drug discovery and repositioning. In this work, we develop a computational pipeline, called DTINet, to predict novel drug–target interactions from a constructed heterogeneous network, which integrates diverse drug-related information. DTINet focuses on learning a low-dimensional vector representation of features, which accurately explains the topological properties of individual nodes in the heterogeneous network, and then makes prediction based on these representations via a vector space projection scheme. DTINet achieves substantial performance improvement over other state-of-the-art methods for drug–target interaction prediction. Moreover, we experimentally validate the novel interactions between three drugs and the cyclooxygenase proteins predicted by DTINet, and demonstrate the new potential applications of these identified cyclooxygenase inhibitors in preventing inflammatory diseases. These results indicate that DTINet can provide a practically useful tool for integrating heterogeneous information to predict new drug–target interactions and repurpose existing drugs. Network-based data integration for drug–target prediction is a promising avenue for drug repositioning, but performance is wanting. Here, the authors introduce DTINet, whose performance is enhanced in the face of noisy, incomplete and high-dimensional biological data by learning low-dimensional vector representations.

Subfields: Computational Theory and Mathematics, Pharmacology

View Paper: https://www.semanticscholar.org/paper/cf1590952770cbb74cc4266181d9f01d6f2e1673

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Citations in the corpus

NeoDTI: Neural integration of neighbor information from a heterogeneous network for discovering new drug-target interactions (2018, methodology): “, a same protein is connected to more than one similar drugs and vice versa), the prediction performance can be easily inflated by easy predictions in this case [5].”
Integration strategies of multi-omics data for machine learning analysis (2021, methodology): “For example, DTINet [111] used random walk-based embedding on multiple interaction and similarity networks of proteins, drugs and diseases in order to detect similar nodes and predict new drug-target interactions (DTI).”
Deep learning in drug discovery: an integrative review and future challenges (2022, background): “A network-based strategy employs heterogeneous networks includes a variety of nodes and edges kinds (Luo et al. 2017; David et al. 2019).”
TCM network pharmacology: A new trend towards combining computational, experimental and clinical approaches. (2021, methodology): “HTINet Target prediction algorithm of TCM based on representation learning 2019 [25]”
Machine learning approaches and databases for prediction of drug–target interaction: a survey paper (2020, methodology): “[118] DeepDTIs DrugBank Luo et al. [197] DTINet DrugBank, HPRD Zong et al. [117] DeepWalk DrugBank He et al. [159] SimBoost, SimBoostQuant The trend of using such continuous-valued datasets may eventually catch on as it is more useful and more meaningful, in the sense that it represents the reality…”
Interpretable bilinear attention network with domain adaptation improves drug–target prediction (2022, neutral): “H p P σPCNNPW (l) c ,b (l) c ,H (l) p )), (1)”
Machine Learning in Drug Discovery: A Review (2021, methodology): “To generate new targets and indications, then utilize the network diffusion algorithm and dimensionality reduction approach (Luo et al. 2017).”
DeepAffinity: Interpretable Deep Learning of Compound-Protein Affinity through Unified Recurrent and Convolutional Neural Networks (2018, background): “We chose three common NSAIDs known for human COX-1/2 selectivity: celecoxib (pIC 50 for COX-1: 4.09; COX-2: 5.17), ibuprofen (COX-1: 4.92, COX-2: 4.10) and rofecoxib (COX-1: < 4; COX-2: 4.6) (Luo et al. , 2017).”