Deterministic error propagation in ITC: Revealing multi-fold errors in Kd values under standard conditions
Why this work is in the frame
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Bibliographic record
Abstract
Accurate determination of the equilibrium dissociation constant ( K d ) is essential in fields such as drug discovery and molecular diagnostics, where a rigorous understanding of molecular interactions drives critical decisions. Among established techniques, isothermal titration calorimetry (ITC) is highly valued for its ability to directly capture binding thermodynamics without the need for labeling or immobilization. However, while ITC is often praised for its precision, potential inaccuracies due to the systematic errors in experimental variables (analyte concentrations and measured heat) are frequently overlooked. One key reason for this oversight is the lack of a deterministic framework that explicitly demonstrates how ITC-derived K d values can be affected by these errors. To address this gap, we derived a closed-form mathematical model for error propagation in ITC-based K d determination, quantifying the impact of inaccuracies in analyte concentrations and measured heat on the resulting K d . This framework provides a robust foundation for understanding and predicting the influence of these systematic errors on K d accuracy. Using this solution, we demonstrate that even within the conventionally recommended c -value range of 10–100, expected systematic errors in concentrations and heat can potentially lead to significant multi-fold deviations in K d . These findings underscore the need for quantitative accuracy assessments in ITC experiments and highlight the importance of developing practical tools to support such evaluations.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it