Pairwise-difference QSAR with quantile aggregation extrapolates beyond known potency
- Standard QSAR struggles to predict analogs more potent than anything in its training set, which is exactly the lead-optimization question.
- Pairwise-Difference QSAR learns activity changes from descriptor differences between compound pairs, then aggregates the predictions.
- On ChEMBL-derived benchmarks across about a hundred targets with the most potent compounds held out, a low-quantile aggregation beat mean and kNN aggregation, direct QSAR and Chemprop.
💡 Points
Every optimization team has felt this: the model ranks the series fine, then fails at the top end where you actually need it. Holding out the most potent decile is the right stress test. My caution is that public ChEMBL tails differ from a live program's SAR cliffs, so I'd trial it prospectively on one of our own series before trusting it to prioritize synthesis.
Breaking the Potency Barrier: A Practical Recipe for QSAR Extrapolation ↗