# Drug-Drug Interaction Prediction Based on Knowledge Graph Embeddings and Convolutional-LSTM Network

arXiv: https://arxiv.org/abs/1908.01288 · HTML: https://mlsanity.com/paper/1908.01288/

## Key facts

- Cited by 29 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Aug 2019.

## Built on by (most cited first, 29 of 29)

- [Emerging Drug Interaction Prediction Enabled by Flow-based Graph Neural Network with Biomedical Network](https://mlsanity.com/paper/2311.09261/) (2023)
- [Graph Neural Networks for Molecules](https://mlsanity.com/paper/2209.05582/) (2022)
- [BERT Based Clinical Knowledge Extraction for Biomedical Knowledge Graph Construction and Analysis](https://mlsanity.com/paper/2304.10996/) (2023)
- [A Review on Knowledge Graphs for Healthcare: Resources, Applications, and Promises](https://mlsanity.com/paper/2306.04802/) (2023)
- [Comprehensive evaluation of deep and graph learning on drug-drug interactions prediction](https://mlsanity.com/paper/2306.05257/) (2023)
- [Accurate and interpretable drug-drug interaction prediction enabled by knowledge subgraph learning](https://mlsanity.com/paper/2311.15056/) (2023)
- [Knowledge-augmented Graph Machine Learning for Drug Discovery: A Survey](https://mlsanity.com/paper/2302.08261/) (2023)
- [Biomedical Knowledge Graph: A Survey of Domains, Tasks, and Real-World Applications](https://mlsanity.com/paper/2501.11632/) (2025)
- [BioBLP: A Modular Framework for Learning on Multimodal Biomedical Knowledge Graphs](https://mlsanity.com/paper/2306.03606/) (2023)
- [Learning to Describe for Predicting Zero-shot Drug-Drug Interactions](https://mlsanity.com/paper/2403.08377/) (2024)
- [Refining Diagnosis Paths for Medical Diagnosis based on an Augmented Knowledge Graph](https://mlsanity.com/paper/2204.13329/) (2022)
- [Question Answering Over Biological Knowledge Graph via Amazon Alexa](https://mlsanity.com/paper/2210.06040/) (2022)
- [Towards Unified AI Drug Discovery with Multiple Knowledge Modalities](https://mlsanity.com/paper/2305.01523/) (2023)
- [Medical Imaging with Deep Learning for COVID- 19 Diagnosis: A Comprehensive Review](https://mlsanity.com/paper/2107.09602/) (2021)
- [A Biomedical Knowledge Graph for Biomarker Discovery in Cancer](https://mlsanity.com/paper/2302.04737/) (2023)
- [Towards Interpretable Drug-Drug Interaction Prediction: A Graph-Based Approach with Molecular and Network-Level Explanations](https://mlsanity.com/paper/2507.09173/) (2025)
- [Catch Me If You Can: Semi-supervised Graph Learning for Spotting Money Laundering](https://mlsanity.com/paper/2302.11880/) (2023)
- [Benchmarking drug-drug interaction prediction methods: a perspective of distribution changes](https://mlsanity.com/paper/2410.18583/) (2024)
- [OpenDDI: A Comprehensive Benchmark for DDI Prediction](https://mlsanity.com/paper/2602.00539/) (2026)
- [DDI Prediction via Heterogeneous Graph Attention Networks](https://mlsanity.com/paper/2207.05672/) (2022)
- [Predicting Drug-Drug Interactions Using Knowledge Graphs](https://mlsanity.com/paper/2308.04172/) (2023)
- [Devil in the Tail: A Multi-Modal Framework for Drug-Drug Interaction Prediction in Long Tail Distinction](https://mlsanity.com/paper/2410.12249/) (2024)
- [Drug Similarity and Link Prediction Using Graph Embeddings on Medical Knowledge Graphs](https://mlsanity.com/paper/2110.13047/) (2021)
- [Graph Regularized Probabilistic Matrix Factorization for Drug-Drug Interactions Prediction](https://mlsanity.com/paper/2210.10784/) (2022)
- [Drug Interaction Vectors Neural Network: DrIVeNN](https://mlsanity.com/paper/2308.13891/) (2023)
- [RGDA-DDI: Residual graph attention network and dual-attention based framework for drug-drug interaction prediction](https://mlsanity.com/paper/2408.15310/) (2024)
- [A Hybrid Computational Intelligence Framework with Metaheuristic Optimization for Drug-Drug Interaction Prediction](https://mlsanity.com/paper/2510.09668/) (2025)
- [MolBridge: Atom-Level Joint Graph Refinement for Robust Drug-Drug Interaction Event Prediction](https://mlsanity.com/paper/2510.20448/) (2025)
- [Asymmetric Focal Loss Improves Graph Neural Network Prediction of Drug-Drug Interactions](https://mlsanity.com/paper/2607.07611/) (2026)

## Abstract (arXiv)

Interference between pharmacological substances can cause serious medical injuries. Correctly predicting so-called drug-drug interactions (DDI) does not only reduce these cases but can also result in a reduction of drug development cost. Presently, most drug-related knowledge is the result of clinical evaluations and post-marketing surveillance; resulting in a limited amount of information. Existing data-driven prediction approaches for DDIs typically rely on a single source of information, while using information from multiple sources would help improve predictions. Machine learning (ML) techniques are used, but the techniques are often unable to deal with skewness in the data. Hence, we propose a new ML approach for predicting DDIs based on multiple data sources. For this task, we use 12,000 drug features from DrugBank, PharmGKB, and KEGG drugs, which are integrated using Knowledge Graphs (KGs). To train our prediction model, we first embed the nodes in the graph using various embedding approaches. We found that the best performing combination was a ComplEx embedding method creating using PyTorch-BigGraph (PBG) with a Convolutional-LSTM network and classic machine learning-based prediction models. The model averaging ensemble method of three best classifiers yields up to 0.94, 0.92, 0.80 for AUPR, F1-score, and MCC, respectively during 5-fold cross-validation tests.