Chrome Extension
WeChat Mini Program
Use on ChatGLM

RepurposeDrugs: an Interactive Web-Portal and Predictive Platform for Repurposing Mono- and Combination Therapies.

Aleksandr Ianevski, Aleksandr Kushnir, Kristen Nader, Mitro Miihkinen,Henri Xhaard,Tero Aittokallio,Ziaurrehman Tanoli

BRIEFINGS IN BIOINFORMATICS(2024)

Cited 0|Views9
No score
Abstract
RepurposeDrugs (https://repurposedrugs.org/) is a comprehensive web-portal that combines a unique drug indication database with a machine learning (ML) predictor to discover new drug-indication associations for approved as well as investigational mono and combination therapies. The platform provides detailed information on treatment status, disease indications and clinical trials across 25 indication categories, including neoplasms and cardiovascular conditions. The current version comprises 4314 compounds (approved, terminated or investigational) and 161 drug combinations linked to 1756 indications/conditions, totaling 28 148 drug-disease pairs. By leveraging data on both approved and failed indications, RepurposeDrugs provides ML-based predictions for the approval potential of new drug-disease indications, both for mono- and combinatorial therapies, demonstrating high predictive accuracy in cross-validation. The validity of the ML predictor is validated through a number of real-world case studies, demonstrating its predictive power to accurately identify repurposing candidates with a high likelihood of future approval. To our knowledge, RepurposeDrugs web-portal is the first integrative database and ML-based predictor for interactive exploration and prediction of both single-drug and combination approval likelihood across indications. Given its broad coverage of indication areas and therapeutic options, we expect it accelerates many future drug repurposing projects.
More
Translated text
Key words
computational drug repurposing,repurposing drug combinations,drug-disease associations,clinical trials,clinical trial outcome prediction,drug repositioning,drug discovery
AI Read Science
Must-Reading Tree
Example
Generate MRT to find the research sequence of this paper
Chat Paper
Summary is being generated by the instructions you defined