What Does the Accuracy of the Equilibrium Dissociation Constant of Affinity Complexes Depend on Fundamentally?
Bibliographic record
Abstract
The equilibrium dissociation constant (Kd) characterizes stability of binding complexes. The classic way of Kd determination involves finding the dependence of a fraction of unbound ligand on the total concentration of target (T0) when the total concentration of ligand (L0) remains constant. It is known that Kd determination for highly stable complexes is notoriously inaccurate; however, what the accuracy of Kd depends on fundamentally, i.e., method-independently, is largely unknown. Here we present an error-propagation analysis that answers this question in detail. This analysis explains the critical importance of the L0/Kd value for the accuracy of Kd and allows one to define the range of L0/Kd values required for accurate Kd determination. Our analysis creates a theoretical foundation for improving the accuracy of Kd determination.
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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 | 0.017 | 0.101 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.004 | 0.011 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".