arXiv — Machine Learning · · 3 min read

Predicting Quantifiability from Primary Screens to Prioritize Dose-Response Profiling

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Computer Science > Machine Learning

arXiv:2608.26538 (cs)
[Submitted on 27 Aug 2026]

Title:Predicting Quantifiability from Primary Screens to Prioritize Dose-Response Profiling

Authors:Sean Lim
View a PDF of the paper titled Predicting Quantifiability from Primary Screens to Prioritize Dose-Response Profiling, by Sean Lim
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Abstract:High-throughput drug screening relies on low-cost primary assays to prioritize compounds for more expensive dose-response profiling, where potency is ultimately quantified. Current screening strategies largely focus on identifying compounds that will confirm biological activity on follow-up, implicitly assuming that confirmed activity will also yield a usable potency estimate. However, confirmed biological activity in screening does not necessarily translate into a quantifiable potency, because active compounds can still fail to produce a reportable dose-response estimate. We therefore present a framework for modeling quantifiability, whether follow-up testing will yield a usable potency estimate, as a distinct triage objective from biological activity. Quantifiability was strongly predictable from the preceding low-cost screen, with most predictive information arising from the observed screening features rather than molecular structure. Response-based predictors remained robust on previously unseen chemical scaffolds and generalized across held-out assay-mechanism families, while the probability of successful quantification varied strongly with response amplitude and assay context. These findings establish experimental measurability, distinct from biological activity, as a predictable property of screening outcomes and show that quantifiability-aware triage can improve the allocation of costly dose-response profiling capacity.
Subjects: Machine Learning (cs.LG); Biomolecules (q-bio.BM)
Cite as: arXiv:2608.26538 [cs.LG]
  (or arXiv:2608.26538v1 [cs.LG] for this version)
  https://doi.org/10.48550/arXiv.2608.26538
arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Sean Lim [view email]
[v1] Thu, 27 Aug 2026 02:18:21 UTC (114 KB)
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