Novel drug-target interactions via link prediction and network embedding

Background As many interactions between the chemical and genomic space remain undiscovered, computational methods able to identify potential drug-target interactions (DTIs) are employed to accelerate drug discovery and reduce the required cost. Predicting new DTIs can leverage drug repurposing by id...

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Vydáno v:	BMC bioinformatics Ročník 23; číslo 1; s. 121 - 16
Hlavní autoři:	Amiri Souri, E., Laddach, R., Karagiannis, S. N., Papageorgiou, L. G., Tsoka, S.
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	London BioMed Central 04.04.2022 BioMed Central Ltd Springer Nature B.V BMC
Témata:	Algorithms Bioinformatics Biomedical and Life Sciences Classification Computational Biology/Bioinformatics Computer Appl. in Life Sciences Computer applications Datasets Drug discovery Drug Interactions Drug Repositioning Drug repurposing Drugs Embedding Gene mapping Genomics Graph-embedding Health aspects Life Sciences Ligands Link prediction Machine learning Mathematical analysis Methods Microarrays Molecular docking Molecular Docking Simulation Network analysis Planning Prediction models Proteins Proteins - chemistry Similarity Similarity measures Therapeutic targets Vectors (mathematics) United Kingdom Link prediction Drug repurposing Graph-embedding Machine learning
ISSN:	1471-2105, 1471-2105
On-line přístup:	Získat plný text
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Popis
Shrnutí:	Background As many interactions between the chemical and genomic space remain undiscovered, computational methods able to identify potential drug-target interactions (DTIs) are employed to accelerate drug discovery and reduce the required cost. Predicting new DTIs can leverage drug repurposing by identifying new targets for approved drugs. However, developing an accurate computational framework that can efficiently incorporate chemical and genomic spaces remains extremely demanding. A key issue is that most DTI predictions suffer from the lack of experimentally validated negative interactions or limited availability of target 3D structures. Results We report DT2Vec, a pipeline for DTI prediction based on graph embedding and gradient boosted tree classification. It maps drug-drug and protein–protein similarity networks to low-dimensional features and the DTI prediction is formulated as binary classification based on a strategy of concatenating the drug and target embedding vectors as input features. DT2Vec was compared with three top-performing graph similarity-based algorithms on a standard benchmark dataset and achieved competitive results. In order to explore credible novel DTIs, the model was applied to data from the ChEMBL repository that contain experimentally validated positive and negative interactions which yield a strong predictive model. Then, the developed model was applied to all possible unknown DTIs to predict new interactions. The applicability of DT2Vec as an effective method for drug repurposing is discussed through case studies and evaluation of some novel DTI predictions is undertaken using molecular docking. Conclusions The proposed method was able to integrate and map chemical and genomic space into low-dimensional dense vectors and showed promising results in predicting novel DTIs.
Bibliografie:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23
ISSN:	1471-2105 1471-2105
DOI:	10.1186/s12859-022-04650-w