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Updated: Jun 27, 2026

Drug Repurposing Hypothesis Generation Using the "RE:fine Drugs" System

Drug Repurposing Hypothesis Generation Using the "RE:fine Drugs" System

Published on: December 11, 2016

Reviewing the Computational Landscape of Drug Repurposing: Evolution from Structure-Based Methods to LLM-Based

Zengyun Mou¹, Zhiqing Tian¹, Jiaqi Jin¹

¹School of Artificial Intelligence, Beijing Normal University, Beijing 100088, China.

|June 26, 2026

Summary

This summary is machine-generated.

Drug repurposing accelerates drug discovery by finding new uses for existing drugs. This review surveys computational methods, from mechanism-based to AI-driven approaches, to guide future research and development.

Keywords:

Large Language Models (LLMs)computational methods drug repurposing review

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Area of Science:

Computational drug discovery
Pharmacology
Bioinformatics

Background:

Traditional drug discovery is lengthy, expensive, and high-risk.
Drug repurposing offers a faster, cheaper, and safer alternative by identifying new uses for approved drugs.
Computational strategies are crucial for efficient drug repurposing.

Purpose of the Study:

To provide a comprehensive survey of computational drug repurposing methodologies.
To clarify the principles, applications, and limitations of various computational approaches.
To offer insights into future research directions in drug repurposing.

Main Methods:

Categorization of methods into biological mechanism-driven (structure-based, omics-based, fuzzy logic-based, adverse event-based), network-based (graph mining, matrix factorization/completion), and data-driven (text mining, large language models).
Elaboration on the principles, advantages, and challenges of each methodological category.
Discussion on the integration of multi-source data and the evolution of text mining to LLM-based methods.

Main Results:

Biological mechanism-driven methods offer deep mechanistic insights.
Network-based methods facilitate systematic prediction and integration of diverse data.
Data-driven methods, especially LLMs, enhance information extraction from literature.
Each method has unique strengths and limitations.

Conclusions:

The future of drug repurposing relies on the intelligent integration of diverse computational methodologies.
Network-based and data-driven methods are poised to enable large-scale drug repurposing.
Biological mechanism-driven methods remain essential for rigorous validation and explanation of repurposed drugs.