Revealing Atropisomer Axial Chirality in Drug Discovery
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
An often overlooked source of chirality is atropisomerism, which results from slow rotation along a bond axis due to steric hindrance and/or electronic factors. If undetected or not managed properly, this time-dependent chirality has the potential to lead to serious consequences, because atropisomers can be present as distinct enantiomers or diastereoisomers with their attendant different properties. Herein we introduce a strategy to reveal and classify compounds that have atropisomeric chirality. Energy barriers to axial rotation were calculated using quantum mechanics, from which predicted high barriers could be experimentally validated. A calculated rotational energy barrier of 20 kcal mol(-1) was established as a suitable threshold to distinguish between atropisomers and non-atropisomers with a prediction accuracy of 86%. This methodology was applied to subsets of drug databases in the course of which atropisomeric drugs were identified. In addition, some drugs were exposed that were not yet known to have this chiral attribute. The most valuable utility of this tool will be to predict atropisomerism along the drug discovery pathway. When used in concert with our compound classification scheme, decisions can be made during early discovery stages such as "hit-to-lead" and "lead optimization," to foresee and validate the presence of atropisomers and to exercise options of removing, further stabilizing, or rendering the chiral axis of interest more freely rotatable via SAR design, thereby decreasing this potential liability within a compound series. The strategy can also improve drug development plans, such as determining whether a drug or series should be developed as a racemic mixture or as an isolated single compound. Moreover, the work described herein can be extended to other chemical fields that require the assessment of potential chiral axes.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 it