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Time-resolved evaluation of compound repositioning predictions on a text-mined knowledge network
Michael Mayers1, Tong Shu Li1, Núria Queralt-Rosinach1
1The Scripps Research Institute, 10550 N Torrey Pines Rd, La Jolla, CA, 92037, USA.
BMC Bioinformatics
|December 13, 2019
Summary
Computational compound repositioning shows promise but faces challenges. A new time-resolved evaluation framework revealed that focusing on drug-drug and disease-disease similarities can improve predictions for identifying new drug uses.
Area of Science:
- Computational pharmacology
- Drug discovery and development
- Bioinformatics
Background:
- Computational compound repositioning aims to find new uses for existing drugs.
- Current algorithms show promise but real-world success remains limited.
- Improved evaluation strategies are needed to better assess repositioning potential.
Purpose of the Study:
- To develop and evaluate a time-resolved framework for assessing computational compound repositioning algorithms.
- To identify factors influencing algorithm performance in a more realistic setting.
- To guide future optimizations for improved drug repurposing predictions.
Main Methods:
- A network-based computational repositioning algorithm was applied to a text-mined database.
- A time-resolved evaluation framework was constructed using historical data.
- Performance was assessed by predicting on indications discovered after network construction.
Main Results:
- Cross-validation yielded high performance (0.95 AUROC).
- The time-resolved framework showed reduced performance, peaking at 0.797 AUROC with a 1985 network.
- Removing drug-drug and disease-disease similarity metrics significantly impacted performance.
Conclusions:
- Evaluating algorithms on future, unknown indications better reflects real-world drug repurposing.
- Improving algorithmic performance within a time-resolved paradigm is crucial for predicting emerging drug indications.
- Acquiring more drug-drug and disease-disease similarity data may enhance computational repositioning accuracy.
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