Streamlined Data Processing for Determination of Equilibrium Dissociation Constants with Accurate Constant via Transient Incomplete Separation (ACTIS)
Bibliographic record
Abstract
The determination of accurate equilibrium dissociation constants, K d, of protein–small molecule complexes is important but challenging as all established methods have inherent sources of inaccuracy. Accurate Constant via Transient Incomplete Separation (ACTIS) is a new method for K d determination using transient incomplete separation of the complex from the unbound small molecule in a pressure-driven flow inside a capillary. ACTIS is accurate, and its accuracy is invariant to variations in geometries of both the fluidic system and the flow. Furthermore, ACTIS is implemented using a simple fluidic system supporting its accuracy and providing a simple-to-follow/copy template for instrumentation. Despite the simple and robust instrumentation/acquisition, the current data processing workflow is cumbersome, time consuming, and prone to hard-to-trace human errors therefore hindering ACTIS’ ability to become a practical reference method for K d determination. This technical note describes a streamlined workflow for processing ACTIS data; the workflow is implemented as a set of open-source software tools called prACTISed ( https://github.com/prACTISedProgram/prACTISed ). These tools allow all steps of data processing to be performed in a fast and straightforward fashion. These practical software tools complement the simple instrumentation serving both developers and users of ACTIS.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.028 | 0.029 |
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".