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Grant support

This work was supported by National Natural Science Foundation of China (Grant Nos. 61873280, 61972416), Taishan Scholarship (tsqn201812029), Major projects of the National Natural Science Foundation of China (Grant No. 41890851), Natural Science Foundation of Shandong Province (No. ZR2019MF012), Fundamental Research Funds for the Central Universities (19CX05003A-6).

Analysis of institutional authors

Rodriguez-Paton, AAuthorSong, TCorresponding Author

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June 7, 2021
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KG-DTI: a knowledge graph based deep learning method for drug-target interaction predictions and Alzheimer's disease drug repositions

Publicated to:Applied Intelligence. 52 (1): 846-857 - 2022-01-01 52(1), DOI: 10.1007/s10489-021-02454-8

Authors: Wang, Shudong; Du, Zhenzhen; Ding, Mao; Rodriguez-Paton, Alfonso; Song, Tao

Affiliations

China Univ Petr, Coll Comp Sci & Technol, Qingdao 266580, Peoples R China - Author
Shandong Univ, Hosp 2, Cheeloo Coll Med, Dept Neurol Med, Jinan 250033, Peoples R China - Author
Univ Politecn Madrid, Fac Comp Sci, Dept Artificial Intelligence, Campus Montegancedo, Madrid 28660, Spain - Author

Abstract

Drug repositioning, which recommends approved drugs to potential targets by predicting drug-target interactions (DTIs), can save the cost and shorten the period of drug development. In this work, we propose a novel knowledge graph based deep learning method, named KG-DTI, for DTIs predictions. Specifically, a knowledge graph of 29,607 positive drug-target pairs is constructed by DistMult embedding strategy. A Conv-Conv module is proposed to extract features of drug-target pairs (DTPs), which is followed by a fully connected neural network for DTIs calculation. Data experiments are conducted on randomly chosen 11,840 positive and negative samples. It is obtained that KG-DTI achieves average ACC by 88.0%, F1-Score by 87.7%, AUROC by 94.3% and AUPR by 95% in five-fold cross-validation. In practice, KG-DTI is applied to reposition drugs to Alzheimer's disease (AD) by Apolipoprotein E target. As results, it is found that seven of the top ten recommended drugs have been used in clinic practice or with literature supported useful to AD. Ligand-target docking results show that the top one recommended drug can dock with Apolipoprotein E significantly, which gives vital hints in repositioning potential drug to AD treatment.

Keywords

Deep learningDiscoveryDrug repositioningDrug-target interactionKnowledge graph

Quality index

Bibliometric impact. Analysis of the contribution and dissemination channel

The work has been published in the journal Applied Intelligence due to its progression and the good impact it has achieved in recent years, according to the agency WoS (JCR), it has become a reference in its field. In the year of publication of the work, 2022, it was in position 48/145, thus managing to position itself as a Q2 (Segundo Cuartil), in the category Computer Science, Artificial Intelligence. Notably, the journal is positioned en el Cuartil Q2 para la agencia Scopus (SJR) en la categoría Artificial Intelligence.

From a relative perspective, and based on the normalized impact indicator calculated from World Citations provided by WoS (ESI, Clarivate), it yields a value for the citation normalization relative to the expected citation rate of: 1.3. This indicates that, compared to works in the same discipline and in the same year of publication, it ranks as a work cited above average. (source consulted: ESI Nov 14, 2024)

This information is reinforced by other indicators of the same type, which, although dynamic over time and dependent on the set of average global citations at the time of their calculation, consistently position the work at some point among the top 50% most cited in its field:

  • Weighted Average of Normalized Impact by the Scopus agency: 2.52 (source consulted: FECYT Feb 2024)

Specifically, and according to different indexing agencies, this work has accumulated citations as of 2025-07-08, the following number of citations:

  • WoS: 17
  • Scopus: 41

Impact and social visibility

From the perspective of influence or social adoption, and based on metrics associated with mentions and interactions provided by agencies specializing in calculating the so-called "Alternative or Social Metrics," we can highlight as of 2025-07-08:

  • The use of this contribution in bookmarks, code forks, additions to favorite lists for recurrent reading, as well as general views, indicates that someone is using the publication as a basis for their current work. This may be a notable indicator of future more formal and academic citations. This claim is supported by the result of the "Capture" indicator, which yields a total of: 37 (PlumX).

Leadership analysis of institutional authors

This work has been carried out with international collaboration, specifically with researchers from: China.

There is a significant leadership presence as some of the institution’s authors appear as the first or last signer, detailed as follows: Last Author (SONG, TAO).

the author responsible for correspondence tasks has been SONG, TAO.