Vol. 5 No. 1 (2025): Issue 5
Articles

Graph Neural Network-based Drug-Target Interaction Prediction for Precision Medicine

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  • Yuhan Dai,
  • Li Wei,
  • Chen Yu
DOI: https://doi.org/10.71070/oaml.v5i1.145

Published 2025-11-02

How to Cite

Dai, Y., Wei, L., & Yu, C. (2025). Graph Neural Network-based Drug-Target Interaction Prediction for Precision Medicine. Optimizations in Applied Machine Learning, 5(1). https://doi.org/10.71070/oaml.v5i1.145

Copyright (c) 2025 Yuhan Dai, Li Wei, Chen Yu

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

Abstract

Drug-target interaction (DTI) prediction is central to precision medicine because it supports target prioritization, drug repurposing, and patient-specific therapeutic design. Existing computational approaches, however, often struggle with sparse interaction labels, heterogeneous biological evidence, and the nonlinear topology of drug-target networks. This paper proposes a Graph Neural Network (GNN)-based DTI prediction framework that represents drugs and targets as nodes in a bipartite biological graph and learns interaction-aware embeddings through message passing. By integrating drug descriptors, target features, structural similarity, and graph connectivity, the framework captures relationships that are difficult for similarity-based, matrix-factorization, or purely tabular models to express. A case study using simulated biochemical descriptors illustrates the modeling workflow, evaluation metrics, and limitations of baseline prediction. The results emphasize that graph-based representation learning can provide a more flexible foundation for DTI screening and precision-medicine decision support.

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