SOS—A Sample Ordering System for Delivering “Assay-Ready” Compound Plates for Drug Screening
Bibliographic record
Abstract
Many bottlenecks in drug discovery have been addressed with the advent of new assay and instrument technologies. However, storing and processing chemical compounds for screening remains a challenge for many drug discovery laboratories. Although automated storage and retrieval systems are commercially available for medium to large collections of chemical samples, these samples are usually stored at a central site and are not readily accessible to satellite research labs. Drug discovery relies on the rapid testing of new chemical compounds in relevant biological assays. Therefore, newly synthesized compounds must be readily available in various formats to biologists performing screening assays. Until recently, our compounds were distributed in screw cap vials to assayists who would then manually transfer and dilute each sample in an “assay-ready” compound plate for screening. The vials would then be managed by the individuals in an ad hoc manner. To relieve the assayist from searching for compounds and preparing their own assay-ready compound plates, a newly customized compound storage system with an ordering software application was implemented at our research facility that eliminates these bottlenecks. The system stores and retrieves compounds in 1 mL-mini-tubes or microtiter plates, facilitates compound searching by identifier or structure, orders compounds at varying concentrations in specified wells on 96- or 384-well plates, requests the addition of controls (vehicle or reference compounds), etc. The orders are automatically processed and delivered to the assayist the following day for screening. An overview of our system will demonstrate that we minimize compound waste and ensure compound integrity and availability. (JALA 2004;9:123-7)
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.033 | 0.021 |
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".