GeNNius: an ultrafast drug-target interaction inference method based on graph neural networks
Keywords: 
Materias Investigacion::Ingeniería
Materias Investigacion::Ingeniería::Biotecnología
Issue Date: 
2024
Publisher: 
Oxford University Press
Project: 
info:eu-repo/grantAgreement/AEI/Proyectos de I+D+I (Generación de Conocimiento y Retos Investigación)/PID2021-126718OA-I00/[ES]/NUEVA APROXIMACION COMPUTACIONAL PARA LA CARACTERIZACION DE LOS MECANISMOS DE REGULACION DE CELULAS CANCERIGENAS DESDE DATOS DE SINGLE-CELL
ISSN: 
1367-4811
Note: 
This is an Open Access article distributed under the terms of the Creative Commons Attribution License
Citation: 
Veleiro, U. (Uxía); Fuente-Arias, J. (Jesús) de la; Serrano-Sanz, G. (Guillermo); et al. "GeNNius: an ultrafast drug-target interaction inference method based on graph neural networks". Bioinformatics. 40 (1), 2024,
Abstract
Motivation: Drug-target interaction (DTI) prediction is a relevant but challenging task in the drug repurposing field. In-silico approaches have drawn particular attention as they can reduce associated costs and time commitment of traditional methodologies. Yet, current state-of-the-art methods present several limitations: existing DTI prediction approaches are computationally expensive, thereby hindering the ability to use large networks and exploit available datasets and, the generalization to unseen datasets of DTI prediction methods remains unexplored, which could potentially improve the development processes of DTI inferring approaches in terms of accuracy and robustness. Results: In this work, we introduce GeNNius (Graph Embedding Neural Network Interaction Uncovering System), a Graph Neural Network (GNN)-based method that outperforms state-of-the-art models in terms of both accuracy and time efficiency across a variety of datasets. We also demonstrated its prediction power to uncover new interactions by evaluating not previously known DTIs for each dataset. We further assessed the generalization capability of GeNNius by training and testing it on different datasets, showing that this framework can potentially improve the DTI prediction task by training on large datasets and testing on smaller ones. Finally, we investigated qualitatively the embeddings generated by GeNNius, revealing that the GNN encoder maintains biological information after the graph convolutions while diffusing this information through nodes, eventually distinguishing protein families in the node embedding space. Availability and implementation: GeNNius code is available at https://github.com/ubioinformat/GeNNius.

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