Novel Drug-Target Interactions via Link Prediction and Network Embedding

E. Amiri Souri¹, R. Laddach^1,2, S. N. Karagiannis^2,3,L.G. Papageorgiou⁴, S. Tsoka¹

^{1 Department of Informatics, Faculty of Natural and Mathematical Sciences, King's College London, Bush House, London WC2B 4BG, United Kingdom
2 St. John’s Institute of Dermatology, School of Basic & Medical Biosciences, King’s College London, & NIHR Biomedical Research Centre at Guy’s and St. Thomas’ Hospitals and King’s College London, Guy’s Hospital, King’s College London, London SE1 9RT, United Kingdom
3 Breast Cancer Now Research Unit, School of Cancer & Pharmaceutical Sciences, King’s College London, Guy’s Cancer Centre, London SE1 9RT, United Kingdom
4 Centre for Process Systems Engineering, Department of Chemical Engineering, University College London, Roberts Building, Torrington Place, London WC1E 7JE, United Kingdom}

_{BMC Bioinformatics (2022) | https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-022-04650-w}

---

Abstract

_{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.}

_{Cite this article}

• _{Amiri Souri, E., Laddach, R., Karagiannis, S.N. et al. Novel drug-target interactions via link prediction and network embedding. BMC Bioinformatics 23, 121 (2022)}
  

  Download citation