Design of Diverse and Focused Combinatorial Libraries Using an Alternating Algorithm
Bibliographic record
Abstract
There is considerable research in chemistry to develop reaction conditions so that any of a very large number of reactants will successfully form new compounds, e.g. for two components, A(i) + B(j) --> A-B(ij). The numbers of A's and B's usually make it impossible to make all the possible products; with multicomponent reactions, there could easily be millions to billions of possible products. There is a need to identify subsets of reagents so that the resulting products have desirable predicted properties. Our idea is to select reactants sequentially and iteratively to optimize the evolving candidate library. The new Alternating Algorithm, AA, can be used for diversity, a space-filling design, or for a focused design, using either a near neighborhood or structure-activity relationship, SAR. A diversity design seeks to select compounds different from one another; a focused design seeks to find compounds similar to an active compound or compounds that follow a structure activity relationship. The benefit of the method is rapid computation of diversity or focused combinatorial chemical libraries.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".