arXiv Machine Learning August 28, 2026 By Sean Lim

Predicting Quantifiability from Primary Screens to Prioritize Dose-Response Profiling

Read the original on arXiv Machine Learning →

The paper introduces a framework to predict whether a compound’s potency can be quantified in dose‑response profiling, treating quantifiability as a separate triage goal from biological activity. It shows that features from low‑cost primary screens, rather than molecular structure, strongly predict quantifiability, and that this prediction holds across new chemical scaffolds and assay families. The authors argue that incorporating quantifiability predictions can better allocate expensive dose‑response resources.

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