A Fundamental Source of Misclassification in Single-Concentration Binding Screens.

Authors

Tong Ye Wang, Toby Chan, Svetlana M Krylova, Sergey N Krylov

Published in

Analytical chemistry. Jul 27, 2026. Epub Jul 27, 2026.

Abstract

High-throughput screening of large compound libraries is central to drug discovery and chemical probe development. Primary screens typically test each compound at a single concentration and classify hits according to whether the measured signal crosses a preset threshold. Only hits advance to multiconcentration assays for binding-isotherm construction and determination of equilibrium dissociation constants, K_d. Incorrect hit calling is usually attributed to chemistry-specific nuisance behavior, platform-dependent artifacts, screening concentration, and threshold selection. Here, we identify an additional fundamental vulnerability in single-concentration binding screens. First-principles analysis shows that when the reported compound-target signal is nonadditive, meaning that the mixture signal cannot be expressed as a weighted sum of free-target and bound-complex signals, small errors in nominal compound and target concentrations can be amplified into large errors in the single-point readout used for hit calling. Nonadditivity can arise from nonlinear signal formation during measurement or nonlinear transformations during postacquisition processing. Controlled end-to-end simulations, in which true K_d values and signal transformations are known, map the operating regimes where this amplification is most severe. The resulting distortions can produce irreversible false negatives because missed binders do not advance to secondary characterization; in some regimes, true high-affinity binders are preferentially penalized. We translate these findings into a practical workflow emphasizing definition of the desired K_d range, ligand-excess design when feasible, concentration control, end-to-end testing of readout additivity and monotonicity, and pilot- or simulation-based assessment of hit-misclassification risk.

PMID:
42504400
Bibliographic data and abstract were imported from PubMed on 27 Jul 2026.

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