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Heterogeneous networks in drug-target interaction prediction

arXiv 2504.16152 ↗First posted 22 Apr 2025q-bio.BM cs.AI cs.LGcited by 0

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Abstract (arXiv)

Drug discovery requires a tremendous amount of time and cost. Computational drug-target interaction prediction, a significant part of this process, can reduce these requirements by narrowing the search space for wet lab experiments. In this survey, we provide comprehensive details of graph machine learning-based methods in predicting drug-target interaction, as they have shown promising results in this field. These details include the overall framework, main contribution, datasets, and their source codes. The selected papers were mainly published from 2020 to 2024. Prior to discussing papers, we briefly introduce the datasets commonly used with these methods and measurements to assess their performance. Finally, future challenges and some crucial areas that need to be explored are discussed.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 23 of its 149 references that are in the map. First posted on arXiv 22 Apr 2025.

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23 of 149 references.

References · 23

  1. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,282
  2. 2017Graph Attention Networkscited by 8,854
  3. 2017Inductive Representation Learning on Large Graphscited by 6,284
  4. 2018How Powerful are Graph Neural Networks?cited by 4,355
  5. 2013Efficient Estimation of Word Representations in Vector Spacecited by 4,346
  6. 2016node2vec: Scalable Feature Learning for Networkscited by 1,913
  7. 2014DeepWalk: Online Learning of Social Representationscited by 1,543
  8. 2019Simplifying Graph Convolutional Networkscited by 1,470
  9. 2018Predict then Propagate: Graph Neural Networks meet Personalized PageRankcited by 987
  10. 2014Embedding Entities and Relations for Learning and Inference in Knowledge Basescited by 929
  11. 2019Graph Transformer Networkscited by 520
  12. 2020Masked Label Prediction: Unified Message Passing Model for Semi-Supervised Classificationcited by 500
11 more
  1. 2020Learning from Protein Structure with Geometric Vector Perceptronscited by 308
  2. 2015A Survey of Heterogeneous Information Network Analysiscited by 109
  3. 2021Node Dependent Local Smoothing for Scalable Graph Learningcited by 45
  4. 2020SkipGNN: Predicting Molecular Interactions with Skip-Graph Networkscited by 44
  5. 2022Interpretable bilinear attention network with domain adaptation improves drug-target predictioncited by 39
  6. 2024The Interpretable and Effective Graph Neural Additive Networkscited by 19
  7. 2020GEFA: Early Fusion Approach in Drug-Target Affinity Predictioncited by 16
  8. 2018PADME: A Deep Learning-based Framework for Drug-Target Interaction Predictioncited by 8
  9. 2018Recurrent Neural Network-Based Semantic Variational Autoencoder for Sequence-to-Sequence Learningcited by 4
  10. 2023Towards a more inductive world for drug repurposing approachescited by 4
  11. 2015Diffusion Component Analysis: Unraveling Functional Topology in Biological Networkscited by 3

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