A Benchmark Set of Bioactive Molecules for Diversity Analysis of Compound Libraries and Combinatorial Chemical Spaces
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Sources for commercially available compounds have been experiencing continuous growth for several years, reaching their peak in billion- to trillion-sized combinatorial Chemical Spaces. To assess the quality of a compound collection to provide relevant chemistry, a benchmark set of pharmaceutically relevant structures is required that enables an unbiased comparison. For this purpose, the ChEMBL database was mined for molecules displaying biological activity, and three benchmark sets of successive orders of magnitude were created by systematic filtering and processing: Set L (“large-sized,” 379k), Set M (“medium-sized,” 25k), and Set S (“small-sized,” 3k). Tailored for broad coverage of the physicochemical and topological landscape, the benchmark Set S was then employed to analyze the chemical diversity capacities of commercial combinatorial Chemical Spaces and enumerated compound libraries. Among the three utilized search methods─FTrees (pharmacophore features), SpaceLight (molecular fingerprints), and SpaceMACS (maximum common substructure)─eXplore and REAL Space consistently performed best. In general, each Chemical Space was able to provide a larger number of compounds more similar to the respective query molecule than the enumerated libraries, while also individually offering unique scaffolds for each method.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| 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".