Introducing Quantitative Assessment of Accuracy for the Equilibrium Constant of Affinity Complexes
Bibliographic record
Abstract
The equilibrium constant of affinity complexes (Kd) is among the most frequently determined physicochemical parameters, with thousands of papers reporting Kd values monthly. Kd is classically computed via nonlinear regression of a binding isotherm and, while it can be precise, it is often inaccurate due to error amplification. Currently, no method exists for quantitatively assessing the accuracy of Kd — here, we fill this knowledge gap. We introduce the accuracy confidence interval (ACI): a range within which the accurate value of a parameter lies with a defined probability. We also present the “ACI-Concept”: a general approach for determining the ACI of parameters computed with correct nonlinear regression models. The ACI-Concept combines regression-stability and error-propagation analyses. We apply the ACI-Concept to develop a workflow for determining the ACI of Kd from a single binding isotherm. We verify this workflow with computer-simulated and experimental binding isotherms. Finally, we implement this workflow in a user-friendly web application (https://aci.sci.yorku.ca) to facilitate its fast adoption by the broad research community. Knowing the ACI of Kd and other parameters computed through nonlinear regression will help researchers avoid misconceptions that can arise when relying solely on precision.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.036 | 0.146 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".