Bibliographic record
Abstract
The growing number of compounds synthesized and screened in the phar-maceutical industry due to the rapid development of high-throughput chemis-try and screening technologies are a response to the need of more and better quality compounds in the industry pipelines. But these inmense collections (from several hundred thousands to several millions of compounds) pose a tremendous logistical problem to be overcome in order to harvest all their tre-mendous potential. Some reviews have appeared to deal with this topic (1,2), but an update seemed necessary due to the rapid evolution of this field. The scope of this chapter is the management of different types of compound collec-tions from both physical and electronic points of view, including some aspects of natural product-extracts collections. This management is a very difficult and demanding process involving the use of sophisticated equipment and databases. The first thing to bear in mind when implementing this process is that a spe-cialized and dedicated group should be created to be responsible for maintain-ing the collection and processing the orders or requests from the rest of the company. Failure to do so usually ends up with a chaotic situation where no samples can be retrieved in due time and proper format, with no control on the available amounts and locations of the samples. Proper management of the compound collection is the foundation of a quality screening organization. If this function does not operate properly, the most advanced assay technologies will fail to afford reproducible lead compounds. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.120 | 0.112 |
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".