Comprehensive Analysis of Single‐ and Multi‐Target Activity Cliffs Formed by Currently Available Bioactive Compounds
Bibliographic record
Abstract
Activity cliffs are formed by structurally similar compounds having large potency differences. Their study is a focal point of SAR analysis. We present a first systematic survey of single- and multitarget activity cliffs contained in currently available bioactive compounds. Approximately 12% of all active compounds were involved in the formation of activity cliffs. Perhaps unexpectedly, activity cliffs were found to be similarly distributed over different protein target families. Moreover, only approximately 5% of all activity cliffs were multitarget cliffs. Importantly, we also found that only very few multitarget cliffs were formed by compounds having different target selectivity. In addition, 'polypharmacological cliffs', i.e., multitarget activity cliffs involving targets from different protein families, were also only rarely found. Taken together, our findings reveal that only approximately 2% of all pairs of structurally similar compounds sharing the same biological activity form activity cliffs but that, on average, approximately one of 10 active compounds is involved in the formation of one or two single-target cliffs of large magnitude (with at least 100-fold difference in potency). These compounds provide a rich source of SAR information and can be identified across many different target families.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".