Synthesis of Synthetic Musks: A Theoretical Study Based on the Relationships between Structure and Properties at Molecular Scale
Bibliographic record
Abstract
Synthetic musks (SMs), as an indispensable odor additive, are widely used in various personal care products. However, due to their physico-chemical properties, SMs were detected in various environmental media, even in samples from arctic regions, leading to severe threats to human health (e.g., abortion risk). Environmentally friendly and functionally improved SMs have been theoretically designed in previous studies. However, the synthesizability of these derivatives has barely been proven. Thus, this study developed a method to verify the synthesizability of previously designed SM derivatives using machine learning, 2D-QSAR, 3D-QSAR, and high-throughput density functional theory in order to screen for synthesizable, high-performance (odor sensitivity), and environmentally friendly SM derivatives. In this study, three SM derivatives (i.e., D52, D37, and D25) were screened and recommended due to their good performances (i.e., high synthesizability and odor sensitivity; low abortion risk; and bioaccumulation ability in skin keratin). In addition, the synthesizability mechanism of SM derivatives was also analyzed. Results revealed that high intramolecular hydrogen bond strength, electrostatic interaction, qH+ value, energy gap, and low EHOMO would lead to a higher synthesizability of SMs and their derivatives. This study broke the synthesizability bottleneck of theoretically designed environment-friendly SM derivatives and advanced the mechanism of screening functional derivatives.
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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 | 0.000 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".