mlsanityData to 14 Oct 2026

Comprehensive evaluation of deep and graph learning on drug-drug interactions prediction

arXiv 2306.05257 ↗First posted 8 Jun 2023cs.LG q-bio.QMcited by 10

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Abstract (arXiv)

Recent advances and achievements of artificial intelligence (AI) as well as deep and graph learning models have established their usefulness in biomedical applications, especially in drug-drug interactions (DDIs). DDIs refer to a change in the effect of one drug to the presence of another drug in the human body, which plays an essential role in drug discovery and clinical research. DDIs prediction through traditional clinical trials and experiments is an expensive and time-consuming process. To correctly apply the advanced AI and deep learning, the developer and user meet various challenges such as the availability and encoding of data resources, and the design of computational methods. This review summarizes chemical structure based, network based, NLP based and hybrid methods, providing an updated and accessible guide to the broad researchers and development community with different domain knowledge. We introduce widely-used molecular representation and describe the theoretical frameworks of graph neural network models for representing molecular structures. We present the advantages and disadvantages of deep and graph learning methods by performing comparative experiments. We discuss the potential technical challenges and highlight future directions of deep and graph learning models for accelerating DDIs prediction.

Key facts. Cited by 10 papers in the map (data through 14 Oct 2026). Builds on 37 of its 147 references that are in the map. First posted on arXiv 8 Jun 2023.

Builds on

37 of 147 references.

References · 37

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  2. 2020Language Models are Few-Shot Learnerscited by 32,419
  3. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 30,186
  4. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,361
  5. 2017Graph Attention Networkscited by 8,923
  6. 2017Inductive Representation Learning on Large Graphscited by 6,320
  7. 2017Neural Message Passing for Quantum Chemistrycited by 3,487
  8. 2014Sequence to Sequence Learning with Neural Networkscited by 2,986
  9. 2019BioBERT: a pre-trained biomedical language representation model for biomedical text miningcited by 1,947
  10. 2016node2vec: Scalable Feature Learning for Networkscited by 1,918
  11. 2014DeepWalk: Online Learning of Social Representationscited by 1,547
  12. 2016Variational Graph Auto-Encoderscited by 1,382
25 more
  1. 2015Convolutional Networks on Graphs for Learning Molecular Fingerprintscited by 754
  2. 2020A Survey on Knowledge Graphs: Representation, Acquisition and Applicationscited by 643
  3. 2015LINE: Large-scale Information Network Embeddingcited by 582
  4. 2019Graph Transformer Networkscited by 523
  5. 2018Modeling polypharmacy side effects with graph convolutional networkscited by 237
  6. 2021ChemRL-GEM: Geometry Enhanced Molecular Representation Learning for Property Predictioncited by 204
  7. 2019Knowledge Graph Convolutional Networks for Recommender Systemscited by 121
  8. 2021GeoMol: Torsional Geometric Generation of Molecular 3D Conformer Ensemblescited by 107
  9. 2020MolTrans: Molecular Interaction Transformer for Drug Target Interaction Predictioncited by 65
  10. 2021GeomGCL: Geometric Graph Contrastive Learning for Molecular Property Predictioncited by 61
  11. 2019Graph Embedding on Biomedical Networks: Methods, Applications, and Evaluationscited by 50
  12. 2020SkipGNN: Predicting Molecular Interactions with Skip-Graph Networkscited by 44
  13. 2020Multi-view Graph Contrastive Representation Learning for Drug-Drug Interaction Predictioncited by 41
  14. 2021A Review of Biomedical Datasets Relating to Drug Discovery: A Knowledge Graph Perspectivecited by 38
  15. 2020SumGNN: Multi-typed Drug Interaction Prediction via Efficient Knowledge Graph Summarizationcited by 33
  16. 2019Drug-Drug Interaction Prediction Based on Knowledge Graph Embeddings and Convolutional-LSTM Networkcited by 29
  17. 2020GoGNN: Graph of Graphs Neural Network for Predicting Structured Entity Interactionscited by 29
  18. 2019OpenBioLink: A benchmarking framework for large-scale biomedical link predictioncited by 28
  19. 2019MR-GNN: Multi-Resolution and Dual Graph Neural Network for Predicting Structured Entity Interactionscited by 24
  20. 2019CASTER: Predicting Drug Interactions with Chemical Substructure Representationcited by 24
  21. 2019Drug-Drug Adverse Effect Prediction with Graph Co-Attentioncited by 20
  22. 2022Deep learning for drug repurposing: methods, databases, and applicationscited by 16
  23. 2020Predicting Biomedical Interactions with Higher-Order Graph Convolutional Networkscited by 7
  24. 2021STNN-DDI: A Substructure-aware Tensor Neural Network to Predict Drug-Drug Interactionscited by 7
  25. 2022Molecular Substructure-Aware Network for Drug-Drug Interaction Predictioncited by 4

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 2; 2024: 3; 2025: 4; 2026: 1.

10 papers.

  1. 2023BioLORD-2023: Semantic Textual Representations Fusing LLM and Clinical Knowledge Graph Insightscited by 7
  2. 2023Learning to Denoise Biomedical Knowledge Graph for Robust Molecular Interaction Predictioncited by 5
  3. 2025LLMs for Drug-Drug Interaction Prediction: A Comprehensive Comparisoncited by 5
  4. 2024Customized Subgraph Selection and Encoding for Drug-drug Interaction Predictioncited by 4
  5. 2025A Multi-Scale Graph Neural Process with Cross-Drug Co-Attention for Drug-Drug Interactions Predictioncited by 2
  6. 2024CADGL: Context-Aware Deep Graph Learning for Predicting Drug-Drug Interactionscited by 1
  7. 2024ADEP: A Novel Approach Based on Discriminator-Enhanced Encoder-Decoder Architecture for Accurate Prediction of Adverse Effects in Polypharmacycited by 0
  8. 2025GraphLAMA: Enabling Efficient Adaptation of Graph Language Models with Limited Annotationscited by 0
  9. 2025Oxytrees: Model Trees for Bipartite Learningcited by 0
  10. 2026DDIAgents: Mechanism-Conditioned Context Flow for Drug-Drug Interaction Predictioncited by 0