GraphDTI: A robust deep learning predictor of drug-target interactions from multiple heterogeneous data

• Published in: Journal of cheminformatics Vol. 13; no. 1; p. 58
• Main Authors: Liu, Guannan, Singha, Manali, Pu, Limeng, Neupane, Prasanga, Feinstein, Joseph, Wu, Hsiao-Chun, Ramanujam, J., Brylinski, Michal
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 11.08.2021; BioMed Central Ltd; Springer Nature B.V; BMC
• Subjects: Binding sites; Cellular signal transduction; Chemistry; Chemistry and Materials Science; Computational Biology/Bioinformatics; Computational linguistics; Computer Applications in Chemistry; Datasets; Deep learning; Documentation and Information in Chemistry; Drug perturbed gene expression; Drugs; Drug–target interactions; Feature selection; Gene expression; Genes; Information processing; Language processing; Learning algorithms; Machine learning; Macromolecules; Methodology; Molecular interactions; Multi-layer perceptron; Natural language interfaces; Phenotypes; Protein binding; Protein interaction; Protein-protein interactions; Proteins; Protein–protein interaction network; Robustness; Side effects; Signal transduction; Target recognition; Theoretical and Computational Chemistry; Therapeutic targets; Deep learning; GraphDTI; Feature selection; Drug perturbed gene expression; Drug–target interactions; Machine learning; Multi-layer perceptron; Protein–protein interaction network
• ISSN: 1758-2946; 1758-2946
• DOI: 10.1186/s13321-021-00540-0

Traditional techniques to identify macromolecular targets for drugs utilize solely the information on a query drug and a putative target. Nonetheless, the mechanisms of action of many drugs depend not only on their binding affinity toward a single protein, but also on the signal transduction through cascades of molecular interactions leading to certain phenotypes. Although using protein-protein interaction networks and drug-perturbed gene expression profiles can facilitate system-level investigations of drug-target interactions, utilizing such large and heterogeneous data poses notable challenges. To improve the state-of-the-art in drug target identification, we developed GraphDTI, a robust machine learning framework integrating the molecular-level information on drugs, proteins, and binding sites with the system-level information on gene expression and protein-protein interactions. In order to properly evaluate the performance of GraphDTI, we compiled a high-quality benchmarking dataset and devised a new cluster-based cross-validation protocol. Encouragingly, GraphDTI not only yields an AUC of 0.996 against the validation dataset, but it also generalizes well to unseen data with an AUC of 0.939, significantly outperforming other predictors. Finally, selected examples of identified drugtarget interactions are validated against the biomedical literature. Numerous applications of GraphDTI include the investigation of drug polypharmacological effects, side effects through offtarget binding, and repositioning opportunities.