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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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Fig. 1 DTIs prediction problem depictionCC BY · PubMed Central

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 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 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 greenCC BY · PubMed Central

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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: Thafar, Maha A.
Olayan, Rawan S.
Ashoor, Haitham
Albaradei, Somayah
Gao, Xin
Gojobori, Takashi
Essack, Magbubah
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: gold
doi.org
Licence: cc-by
creativecommons.org/licenses/by/4.0 (tdm)
creativecommons.org/licenses/by/4.0 (vor)
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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