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Drug discovery is a multifaceted process involving extensive screening, testing, and optimization of lead compounds to identify potential new drugs for therapeutic use. It combines several approaches, including screening large numbers of natural products, chemical modification of known active molecules, identification of new drug targets, and rational design based on biological mechanisms and drug-receptor structure. These approaches are carried out in both academic research laboratories and...

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Related Experiment Video

Updated: Oct 8, 2025

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

Network-Based Approaches for Drug Repositioning.

Tao Song^1,2, Gan Wang¹, Mao Ding³

¹College of Computer Science and Technology, China University of Petroleum, Qingdao, 266580, China.

Molecular Informatics

|December 31, 2021

Summary

This summary is machine-generated.

This review summarizes network-based computational methods for drug repositioning, highlighting their development and comparing models. It provides insights into cutting-edge deep learning approaches for identifying new drug uses.

Keywords:

Deep learning Drug repositioning Heterogeneous network Network-based

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Area of Science:

Computational biology
Pharmacology
Data science

Background:

Deep learning and big data are advancing drug repositioning.
Existing reviews lack a summary of model and data development in computational drug repositioning.
Network-based methods are crucial in computational drug repositioning.

Purpose of the Study:

To interpret and compare important network-based computational methods for drug repositioning.
To outline the development process of these network-based methods.
To offer valuable information for researchers in the field.

Main Methods:

Review and interpretation of existing literature on network-based computational methods.
Comparative analysis of different network-based models.
Discussion of the evolution of these methods and their associated data.

Main Results:

Identification of key network-based computational methods in drug repositioning.
Comparison of the strengths and weaknesses of various approaches.
Understanding the developmental trajectory of these methods.

Conclusions:

Network-based computational methods are vital for drug repositioning.
This review provides a comprehensive overview and comparison of these methods.
The findings will aid researchers in understanding current technologies and future directions.