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GESSE: Predicting Drug Side Effects from Drug-Target Relationships
Violeta I Pérez-Nueno1, Michel Souchet1, Arnaud S Karaboga1
1Harmonic Pharma , Espace Transfert, 615 rue du Jardin Botanique, 54600 Villers-les-Nancy, France.
Journal of Chemical Information and Modeling
|August 8, 2015
Summary
A new computational method, GESSE, predicts potential drug side effects (SEs) by analyzing drug properties and target relationships. This approach aids early identification of SEs, reducing pharmaceutical development costs.
Area of Science:
- Computational chemistry
- Pharmacology
- Drug discovery
Background:
- Predicting drug side effects (SEs) is crucial for pharmaceutical development.
- Early identification of SEs minimizes costly late-stage failures.
- Current methods involve extensive computational and experimental screening.
Purpose of the Study:
- Introduce GESSE, a novel in silico method for predicting drug SEs.
- Utilize physicochemical properties and predicted drug-target relationships for SE prediction.
- Improve early-stage drug safety assessment.
Main Methods:
- GESSE employs canonical correlation analysis on drug-target and drug-SE matrices.
- Bayesian discriminant analysis calculates the probability of a drug having a specific SE.
- Performance validated using retrospective analysis and literature data.
Main Results:
- GESSE demonstrated robust performance in retrospective validation.
- Key metrics included ROC curve analysis, top hit rates, and misclassification rates.
- Numerous predicted SEs were experimentally corroborated.
Conclusions:
- GESSE successfully predicts potential drug-SE profiles from drug-target relationships.
- The method offers a promising tool for early identification of drug safety concerns.
- GESSE can aid in de-risking drug candidate development.
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