A Network Embedding Based Approach to Drug-Target Interaction Prediction Using Additional Implicit Networks

Zhang，Han1; Hou，Chengbin1,2; McDonald，David1; He，Shan1

10.1007/978-3-030-86362-3_40

2021

0302-9743

1611-3349

LECT NOTES ARTIF INT

12891

491-503

Identifying novel drug-target interactions (DTIs) is a crucial step in drug discovery. Since experimentally determining DTIs is expensive and time-consuming, it becomes popular to employ computational methods for providing promising candidate DTIs. However, in the existing computational methods, the drug implicit network and target implicit network constructed from a DTI network (a bipartite network) have been ignored in the DTI prediction problem, while such implicit networks constructed from a bipartite network have been proven useful in other problems, e.g., the link prediction task in a bipartite network. Motivated by that, we propose a novel DTI prediction method which considers the implicit networks in addition to drug structure similarity network and target sequence similarity network. The experiments over five real-world DTI datasets demonstrate the competitive performance of the proposed method compared to the state-of-the-art methods. The code is available at https://github.com/BrisksHan/NE-DTIP.

Drug-Target Interaction Prediction Implicit networks Network embedding Network topology

其他

英语

CPCI-S ; EI

Computer Science

Computer Science, Artificial Intelligence ; Computer Science, Theory & Methods

WOS:000711965200040

20213910941657

Computational methods ; Drug interactions ; Embeddings ; Forecasting ; Tensors

Medicine and Pharmacology:461.6 ; Artificial Intelligence:723.4 ; Chemical Reactions:802.2 ; Algebra:921.1

2-s2.0-85115444384

Scopus

1.School of Computer Science,University of Birmingham,Birmingham,United Kingdom
2.Department of Computer Science and Engineering,Southern University of Science and Technology,Shenzhen,China

Zhang，Han,Hou，Chengbin,McDonald，David,et al. A Network Embedding Based Approach to Drug-Target Interaction Prediction Using Additional Implicit Networks[C],2021:491-503.