A Browser‐Based Tool for Assessing Accuracy of Isothermal Titration Calorimetry‐Derived Parameters: <i>K</i><sub>d</sub>, Δ<i>H</i>°, and <i>n</i>
Bibliographic record
Abstract
Accurate determination of equilibrium dissociation constants (Kd) is essential for decision‐making in drug discovery and diagnostics development. Isothermal titration calorimetry (ITC), which requires no reactant labeling or immobilization, is commonly used to validate Kd values from high‐throughput screens. Yet, like other methods, ITC results can be skewed by systematic errors in reactant concentrations, a fact that is often overlooked, potentially leading to misinformed decisions. To address this, accuracy confidence intervals (ACI)‐ITC is developed, a browser‐based tool that calculates ACI for ITC‐derived parameters, offering probabilistic ranges for the true values of Kd, enthalpy change (ΔH°), and binding stoichiometry (n). Unlike traditional confidence intervals that consider only random errors, ACI‐ITC explicitly accounts for systematic errors, providing a more accurate framework to assess experimental reliability. Alongside a user‐friendly interface, it offers detailed guidance for determining uncertainties in concentrations and heat, which are critical inputs for assessing measurement accuracy. The tool's browser‐based accessibility (https://aci.sci.yorku.ca) eliminates the need for specialized installation, enabling cross‐platform compatibility and streamlining accuracy assessments. By highlighting the importance of systematic errors and providing a structured approach for their evaluation, ACI‐ITC supports more robust conclusions, fosters better‐informed decisions based on ITC measurements, and enhances the reliability of research findings.
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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 | 0.008 | 0.028 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.071 | 0.038 |
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".