# Deep learning for drug repurposing: methods, databases, and applications

arXiv: https://arxiv.org/abs/2202.05145 · HTML: https://mlsanity.com/paper/2202.05145/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 147 references that are in the map.
- First posted on arXiv 8 Feb 2022.

## Builds on (most cited first, 17 of 17)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Auto-Encoding Variational Bayes](https://mlsanity.com/paper/1312.6114/) (2013)
- [A Comprehensive Survey on Graph Neural Networks](https://mlsanity.com/paper/1901.00596/) (2019)
- [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)
- [Therapeutics Data Commons: Machine Learning Datasets and Tasks for Drug Discovery and Development](https://mlsanity.com/paper/2102.09548/) (2021)
- [Recent Trends in Deep Learning Based Natural Language Processing](https://mlsanity.com/paper/1708.02709/) (2017)
- [Learning to Plan Chemical Syntheses](https://mlsanity.com/paper/1708.04202/) (2017)
- [DeepAffinity: Interpretable Deep Learning of Compound-Protein Affinity through Unified Recurrent and Convolutional Neural Networks](https://mlsanity.com/paper/1806.07537/) (2018)
- [DeepConv-DTI: Prediction of drug-target interactions via deep learning with convolution on protein sequences](https://mlsanity.com/paper/1811.02114/) (2018)
- [Network Medicine Framework for Identifying Drug Repurposing Opportunities for COVID-19](https://mlsanity.com/paper/2004.07229/) (2020)
- [AffinityNet: semi-supervised few-shot learning for disease type prediction](https://mlsanity.com/paper/1805.08905/) (2018)
- [A Collective Variational Autoencoder for Top-N Recommendation with Side Information](https://mlsanity.com/paper/1807.05730/) (2018)
- [Repurpose Open Data to Discover Therapeutics for COVID-19 using Deep Learning](https://mlsanity.com/paper/2005.10831/) (2020)

## Built on by (most cited first, 16 of 16)

- [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)
- [On the Role of Edge Dependency in Graph Generative Models](https://mlsanity.com/paper/2312.03691/) (2023)
- [Improving Molecule Generation and Drug Discovery with a Knowledge-enhanced Generative Model](https://mlsanity.com/paper/2402.08790/) (2024)
- [KGExplainer: Towards Exploring Connected Subgraph Explanations for Knowledge Graph Completion](https://mlsanity.com/paper/2404.03893/) (2024)
- [Y-Mol: A Multiscale Biomedical Knowledge-Guided Large Language Model for Drug Development](https://mlsanity.com/paper/2410.11550/) (2024)
- [Healthcare Knowledge Graph Construction: State-of-the-art, open issues, and opportunities](https://mlsanity.com/paper/2207.03771/) (2022)
- [Deep Learning in Computational Biology: Advancements, Challenges, and Future Outlook](https://mlsanity.com/paper/2310.03086/) (2023)
- [Graph Representation Learning for Interactive Biomolecule Systems](https://mlsanity.com/paper/2304.02656/) (2023)
- [Analysing Biomedical Knowledge Graphs using Prime Adjacency Matrices](https://mlsanity.com/paper/2305.10467/) (2023)
- [Computational Approaches for Predicting Drug-Disease Associations: A Comprehensive Review](https://mlsanity.com/paper/2309.06388/) (2023)
- [Improving Targeted Molecule Generation through Language Model Fine-Tuning Via Reinforcement Learning](https://mlsanity.com/paper/2405.06836/) (2024)
- [HGTDR: Advancing Drug Repurposing with Heterogeneous Graph Transformers](https://mlsanity.com/paper/2405.08031/) (2024)
- [DynamicDTA: Drug-Target Binding Affinity Prediction Using Dynamic Descriptors and Graph Representation](https://mlsanity.com/paper/2505.11529/) (2025)
- [MCPI: Integrating Multimodal Data for Enhanced Prediction of Compound Protein Interactions](https://mlsanity.com/paper/2306.08907/) (2023)
- [A Geometric Graph-Based Deep Learning Model for Drug-Target Affinity Prediction](https://mlsanity.com/paper/2509.13476/) (2025)

## Abstract (arXiv)

Drug development is time-consuming and expensive. Repurposing existing drugs for new therapies is an attractive solution that accelerates drug development at reduced experimental costs, specifically for Coronavirus Disease 2019 (COVID-19), an infectious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). However, comprehensively obtaining and productively integrating available knowledge and big biomedical data to effectively advance deep learning models is still challenging for drug repurposing in other complex diseases. In this review, we introduce guidelines on how to utilize deep learning methodologies and tools for drug repurposing. We first summarized the commonly used bioinformatics and pharmacogenomics databases for drug repurposing. Next, we discuss recently developed sequence-based and graph-based representation approaches as well as state-of-the-art deep learning-based methods. Finally, we present applications of drug repurposing to fight the COVID-19 pandemic, and outline its future challenges.