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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

A standard database for drug repositioning.

Adam S Brown¹, Chirag J Patel¹

¹Department of Biomedical Informatics, Harvard Medical School, 10 Shattuck St, Boston, Massachusetts 02115, USA.

Scientific Data

|March 15, 2017

Summary

This summary is machine-generated.

Drug repositioning uses computational methods to find new uses for old drugs. A new database, repoDB, provides true positives and negatives to improve method validation and reproducibility.

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

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Published on: December 11, 2016

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Biosensor-based High Throughput Biopanning and Bioinformatics Analysis Strategy for the Global Validation of Drug-protein Interactions

Biosensor-based High Throughput Biopanning and Bioinformatics Analysis Strategy for the Global Validation of Drug-protein Interactions

Published on: December 1, 2020

Area of Science:

Drug discovery and development
Computational biology
Pharmacology

Background:

Drug repositioning offers a cost-effective alternative to de novo drug development.
Computational methods accelerate the identification of potential drug candidates for new indications.
Current validation methods for computational repositioning lack standardization, hindering progress.

Purpose of the Study:

To address the challenge of inconsistent validation in computational drug repositioning.
To introduce a gold standard database, repoDB, for evaluating computational repositioning methods.
To improve the reproducibility and reliability of drug repositioning predictions.

Main Methods:

Development of repoDB, a comprehensive database containing true positive (approved drugs) and true negative (failed drugs) examples.
Publicly releasing the repoDB database and all associated preparation code.
Creating a web application for interactive browsing of repoDB subsets.

Main Results:

repoDB establishes a benchmark for assessing the performance of computational drug repositioning tools.
The database facilitates a more rigorous and reproducible evaluation of prediction algorithms.
Availability of repoDB and associated code promotes transparency and collaboration in the field.

Conclusions:

repoDB provides a crucial resource for advancing computational drug repositioning.
Standardized validation using repoDB will enhance the accuracy and reliability of drug repurposing efforts.
The open-access nature of repoDB and tools encourages wider adoption and innovation.