ML models predict lifespan-extending compounds in C. elegans with up to 80.36% accuracy
Dr_Singularity · x · 2026-10-28
Researchers Divyam Acharya and Alex Freitas at the University of Kent published a bioRxiv preprint applying supervised machine learning to predict which compounds extend lifespan in C. elegans, reaching up to 80.36% cross-validation accuracy.
- Features derived from compound-protein interactions in the STITCH database, comparing continuous probabilities, binary features at a fixed 0.5 threshold, and optimized thresholds chosen within cross-validation
- Random Forest and XGBoost evaluated with stratified 10-fold CV; Random Forest peaked at 78.84% accuracy, AUROC 0.7391, AUPRC 0.5202
- Model interpretation via MDI, SHAP, TreeSHAP and LIME, with Jaccard similarity comparing top protein features
Since experimental screening is costly and slow, such models can help researchers prioritize promising candidates and allocate lab resources where they're most likely to pay off.
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