Assessing Accuracy of an Analytical Method <i>In Silico</i> : Application to “Accurate Constant via Transient Incomplete Separation” (ACTIS)
Bibliographic record
Abstract
Analytical methods may not have reference standards required for testing their accuracy. We postulate that the accuracy of an analytical method can be assessed in the absence of reference standards in silico if the method is built upon deterministic processes. A deterministic process can be precisely computer-simulated, thus allowing virtual experiments with virtual reference standards. Here, we apply this in silico approach to study “Accurate Constant via Transient Incomplete Separation” (ACTIS), a method for finding the equilibrium dissociation constant (Kd) of protein–small-molecule complexes. ACTIS is based on a deterministic process: molecular diffusion of the interacting protein–small-molecule pair in a laminar pipe flow. We used COMSOL software to construct a virtual ACTIS setup with a fluidic system mimicking that of a physical ACTIS instrument. Virtual ACTIS experiments performed with virtual samples—mixtures of a protein and a small molecule with defined rate constants and, thus, Kd of their interaction—allowed us to assess ACTIS accuracy by comparing the determined Kd value to the input Kd value. Further, the influence of multiple system parameters on ACTIS accuracy was investigated. Within multifold ranges of parameter values, the values of Kd did not deviate from the input Kd values by more than a factor of 1.25, strongly suggesting that ACTIS is intrinsically accurate and that its accuracy is robust. Accordingly, further development of ACTIS can focus on achieving high reproducibility and precision. We foresee that in silico accuracy assessment, demonstrated here with ACTIS, will be applicable to other analytical methods built upon deterministic processes.
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How this classification was reachedexpand
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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".