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

arXiv: https://arxiv.org/abs/2306.05257 · HTML: https://mlsanity.com/paper/2306.05257/

## 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 (most cited first, 30 of 37)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [Neural Message Passing for Quantum Chemistry](https://mlsanity.com/paper/1704.01212/) (2017)
- [Sequence to Sequence Learning with Neural Networks](https://mlsanity.com/paper/1409.3215/) (2014)
- [BioBERT: a pre-trained biomedical language representation model for biomedical text mining](https://mlsanity.com/paper/1901.08746/) (2019)
- [node2vec: Scalable Feature Learning for Networks](https://mlsanity.com/paper/1607.00653/) (2016)
- [DeepWalk: Online Learning of Social Representations](https://mlsanity.com/paper/1403.6652/) (2014)
- [Variational Graph Auto-Encoders](https://mlsanity.com/paper/1611.07308/) (2016)
- [Convolutional Networks on Graphs for Learning Molecular Fingerprints](https://mlsanity.com/paper/1509.09292/) (2015)
- [A Survey on Knowledge Graphs: Representation, Acquisition and Applications](https://mlsanity.com/paper/2002.00388/) (2020)
- [LINE: Large-scale Information Network Embedding](https://mlsanity.com/paper/1503.03578/) (2015)
- [Graph Transformer Networks](https://mlsanity.com/paper/1911.06455/) (2019)
- [Modeling polypharmacy side effects with graph convolutional networks](https://mlsanity.com/paper/1802.00543/) (2018)
- [ChemRL-GEM: Geometry Enhanced Molecular Representation Learning for Property Prediction](https://mlsanity.com/paper/2106.06130/) (2021)
- [Knowledge Graph Convolutional Networks for Recommender Systems](https://mlsanity.com/paper/1904.12575/) (2019)
- [GeoMol: Torsional Geometric Generation of Molecular 3D Conformer Ensembles](https://mlsanity.com/paper/2106.07802/) (2021)
- [MolTrans: Molecular Interaction Transformer for Drug Target Interaction Prediction](https://mlsanity.com/paper/2004.11424/) (2020)
- [GeomGCL: Geometric Graph Contrastive Learning for Molecular Property Prediction](https://mlsanity.com/paper/2109.11730/) (2021)
- [Graph Embedding on Biomedical Networks: Methods, Applications, and Evaluations](https://mlsanity.com/paper/1906.05017/) (2019)
- [SkipGNN: Predicting Molecular Interactions with Skip-Graph Networks](https://mlsanity.com/paper/2004.14949/) (2020)
- [Multi-view Graph Contrastive Representation Learning for Drug-Drug Interaction Prediction](https://mlsanity.com/paper/2010.11711/) (2020)
- [A Review of Biomedical Datasets Relating to Drug Discovery: A Knowledge Graph Perspective](https://mlsanity.com/paper/2102.10062/) (2021)
- [SumGNN: Multi-typed Drug Interaction Prediction via Efficient Knowledge Graph Summarization](https://mlsanity.com/paper/2010.01450/) (2020)
- [Drug-Drug Interaction Prediction Based on Knowledge Graph Embeddings and Convolutional-LSTM Network](https://mlsanity.com/paper/1908.01288/) (2019)
- [GoGNN: Graph of Graphs Neural Network for Predicting Structured Entity Interactions](https://mlsanity.com/paper/2005.05537/) (2020)
- [OpenBioLink: A benchmarking framework for large-scale biomedical link prediction](https://mlsanity.com/paper/1912.04616/) (2019)

## Built on by (most cited first, 10 of 10)

- [BioLORD-2023: Semantic Textual Representations Fusing LLM and Clinical Knowledge Graph Insights](https://mlsanity.com/paper/2311.16075/) (2023)
- [Learning to Denoise Biomedical Knowledge Graph for Robust Molecular Interaction Prediction](https://mlsanity.com/paper/2312.06682/) (2023)
- [LLMs for Drug-Drug Interaction Prediction: A Comprehensive Comparison](https://mlsanity.com/paper/2502.06890/) (2025)
- [Customized Subgraph Selection and Encoding for Drug-drug Interaction Prediction](https://mlsanity.com/paper/2411.01535/) (2024)
- [A Multi-Scale Graph Neural Process with Cross-Drug Co-Attention for Drug-Drug Interactions Prediction](https://mlsanity.com/paper/2509.15256/) (2025)
- [CADGL: Context-Aware Deep Graph Learning for Predicting Drug-Drug Interactions](https://mlsanity.com/paper/2403.17210/) (2024)
- [ADEP: A Novel Approach Based on Discriminator-Enhanced Encoder-Decoder Architecture for Accurate Prediction of Adverse Effects in Polypharmacy](https://mlsanity.com/paper/2406.00118/) (2024)
- [GraphLAMA: Enabling Efficient Adaptation of Graph Language Models with Limited Annotations](https://mlsanity.com/paper/2506.21559/) (2025)
- [Oxytrees: Model Trees for Bipartite Learning](https://mlsanity.com/paper/2511.12713/) (2025)
- [DDIAgents: Mechanism-Conditioned Context Flow for Drug-Drug Interaction Prediction](https://mlsanity.com/paper/2606.31085/) (2026)

## 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.