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Cited by 144Computational Drug Discovery MethodsBioinformatics and Genomic NetworksRecommender Systems and Techniques

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DTiGEMS+: drug–target interaction prediction using graph embedding, graph mining, and similarity-based techniques

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Thafar, Maha A., Olayan, Rawan S., Ashoor, Haitham, Albaradei, Somayah, Bajic, Vladimir B., Gao, Xin, Gojobori, Takashi, Essack, Magbubah·2020·Journal of Cheminformatics
DOI 10.1186/s13321-020-00447-2PMC PMC7325230
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Abstract

In silico prediction of drug–target interactions is a critical phase in the sustainable drug development process, especially when the research focus is to capitalize on the repositioning of existing drugs. However, developing such computational methods is not an easy task, but is much needed, as current methods that predict potential drug–target interactions suffer from high false-positive rates. Here we introduce DTiGEMS+, a computational method that predicts D rug–T arget i nteractions using G raph E mbedding, graph M ining, and S imilarity-based techniques. DTiGEMS+ combines similarity-based as well as feature-based approaches, and models the identification of novel drug–target interactions as a link prediction problem in a heterogeneous network. DTiGEMS+ constructs the heterogeneous network by augmenting the known drug–target interactions graph with two other complementary graphs namely: drug–drug similarity, target–target similarity. DTiGEMS+ combines different computational techniques to provide the final drug target prediction, these techniques include graph embeddings, graph mining, and machine learning. DTiGEMS+ integrates multiple drug–drug similarities and target–target similarities into the final heterogeneous graph construction after applying a similarity selection procedure as well as a similarity fusion algorithm. Using four benchmark datasets, we show DTiGEMS+ substantially improves prediction performance compared to other state-of-the-art in silico methods developed to predict of drug-target interactions by achieving the highest average AUPR across all datasets (0.92), which reduces the error rate by 33.3% relative to the second-best performing model in the state-of-the-art methods comparison.

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Crossref · 10.1186/s13321-020-00447-2

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Fig. 1 DTIs prediction problem depiction
Fig. 1 DTIs prediction problem depictionCC BY · PubMed Central
Fig. 2 Integrating multiple similarities using different functions
Fig. 2 Integrating multiple similarities using different functionsCC BY · PubMed Central
Fig. 3 An illustration of Sum and Max scores for a D–D–D–T path structure
Fig. 3 An illustration of Sum and Max scores for a D–D–D–T path structureCC BY · PubMed Central
Fig. 4 DTiGEMS+ prediction Framework. DTIs: drug–target interactions; DD: drug–drug; TT: target–target; FV: feature vector; FSS alg.: forward similarity selection algorithm; SNF fuc: similarity network fusion function; COS similarity: cosine similarity; ML: machine learning
Fig. 4 DTiGEMS+ prediction Framework. DTIs: drug–target interactions; DD: drug–drug; TT: target–target; FV: feature vector; FSS alg.: forward similarity selection algorithm; SNF fuc: similarity network fusion function; COS similarity: cosine similarity; ML: machine learningCC BY · PubMed Central
Fig. 5 Comparison results for DTiGEMS+ and other methods in terms of AUPR using the Yamanishi_08 datasets. The best performing method is indicated in blue, the second-best method in purple, and all other methods in green
Fig. 5 Comparison results for DTiGEMS+ and other methods in terms of AUPR using the Yamanishi_08 datasets. The best performing method is indicated in blue, the second-best method in purple, and all other methods in greenCC BY · PubMed Central

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Research works printed but not linked: 2

  1. Tenenbaum D (2019) KEGGREST: client-side REST access to KEGG. R Package Version 1.24.0. Fred Hutchinson Cancer Research Center, Seattle, WA, USA
  2. Kam HT (1995) Random decision forest. In: Proceedings of the 3rd international conference on document analysis and recognition, vol 1416, pp 278–282

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Metadata

Authors
Thafar, M. A., Olayan, R. S., Ashoor, H., Albaradei, S., Bajic, V. B., Gao, X., Gojobori, T., & Essack, M.
Affiliations
  • Taif University (SA)
  • The Jackson Laboratory for Genomic Medicine (US)
  • King Abdulaziz University (SA)
  • King Abdullah University of Science and Technology (SA)
Corresponding author
Essack, Magbubah
ORCID
Venue
Journal of Cheminformatics
Publisher
Springer Science and Business Media LLC
Published
2020-06-29
Volume
12
Issue
1
Pages
44-44
ISSN
1758-2946 (ISSN-L)
DOI
10.1186/s13321-020-00447-2
PMC
PMC7325230
OpenAlex
W3039465695
Cited by
143
References
97
Citations by year
2026: 12 · 2025: 18 · 2024: 28 · 2023: 24 · 2022: 15 · 2021: 44 · 2020: 2
FWCI
10.68
Type
journal article
Language
en
Open access
Licence
Locations
  • Journal of Cheminformatics (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • The Mouseion at the JAXlibrary (Jackson Laboratory) (submittedVersion)
  • King Abdullah University of Science and Technology Repository (King Abdullah University of Science and Technology) (submittedVersion, cc-by)
Funders
King Abdullah University of Science and Technology (BAS/1/1606-01-01, BAS/1/1059-01-01, BAS/1/1624-01-01, FCC/1/1976-26-01, FCC/1/1976-17-01)
Topics
  • Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Drug Discovery › Computational Drug Discovery Methods
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
  • Physical Sciences › Computer Science › Information Systems › Recommender Systems and Techniques
Keywords
drug-target interaction prediction, graph embedding, graph mining, similarity-based prediction, heterogeneous networks, link prediction, drug-drug similarity, similarity fusion, drug repurposing, false positive reduction

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