Assessing Accuracy of an Analytical Method in silico: 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 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 ( K d ) 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, K d of their interaction — allowed us to assess ACTIS accuracy by comparing the determined K d value to the input K d value. Further, the influence of multiple system parameters on ACTIS accuracy was investigated. Within multi-fold ranges of parameters, the values of K d did not deviate from the input K d 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 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 | 0.015 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".