# Computational Approaches for Predicting Drug-Disease Associations: A Comprehensive Review

arXiv: https://arxiv.org/abs/2309.06388 · HTML: https://mlsanity.com/paper/2309.06388/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 86 references that are in the map.
- First posted on arXiv 10 Sep 2023.

## Builds on (most cited first, 3 of 3)

- [Deep learning for drug repurposing: methods, databases, and applications](https://mlsanity.com/paper/2202.05145/) (2022)
- [Repurpose Open Data to Discover Therapeutics for COVID-19 using Deep Learning](https://mlsanity.com/paper/2005.10831/) (2020)
- [SBSM-Pro: Support Bio-sequence Machine for Proteins](https://mlsanity.com/paper/2308.10275/) (2023)

## Built on by (most cited first, 1 of 1)

- [KEDRec-LM: A Knowledge-distilled Explainable Drug Recommendation Large Language Model](https://mlsanity.com/paper/2502.20350/) (2025)

## Abstract (arXiv)

In recent decades, traditional drug research and development have been facing challenges such as high cost, long timelines, and high risks. To address these issues, many computational approaches have been suggested for predicting the relationship between drugs and diseases through drug repositioning, aiming to reduce the cost, development cycle, and risks associated with developing new drugs. Researchers have explored different computational methods to predict drug-disease associations, including drug side effects-disease associations, drug-target associations, and miRNAdisease associations. In this comprehensive review, we focus on recent advances in predicting drug-disease association methods for drug repositioning. We first categorize these methods into several groups, including neural network-based algorithms, matrixbased algorithms, recommendation algorithms, link-based reasoning algorithms, and text mining and semantic reasoning. Then, we compare the prediction performance of existing drug-disease association prediction algorithms. Lastly, we delve into the present challenges and future prospects concerning drug-disease associations.