Global Green Chemistry Metrics Analysis Algorithm and Spreadsheets: Evaluation of the Material Efficiency Performances of Synthesis Plans for Oseltamivir Phosphate (Tamiflu) as a Test Case
Why this work is in the frame
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Bibliographic record
Abstract
This work discloses an easy-to-use algorithm to evaluate the global material efficiency performance of any kind of synthesis plan regardless of complexity to a given target molecule according to green metrics criteria. The algorithm is robust and has been adapted to Excel spreadsheets for rapid calculation and graphing of the numerical results. In order to demonstrate the facile utility of this exceptional tool for process and synthetic chemists in the evaluation and ranking of synthetic performance, various synthesis plans for oseltamivir phosphate, a neuraminidase inhibitor used to treat the H5N1 influenza virus, have been investigated. In particular, six industrial syntheses and nine plans from academic groups have been thoroughly and rigorously evaluated according to kernel and global reaction mass efficiencies and E-factors, atom economy, and overall yield performances. In addition, all reported plans were evaluated according to new synthesis elegance parameters including fraction of sacrificial reagents by molecular weight, hypsicity (oxidation level) index, and number of target bonds made per reaction step. Target structure bond maps and profiles are introduced as convenient ways to visually describe synthetic strategy compactly. These powerful algorithms and visual aids can be used to immediately spot bottlenecks in a synthesis plan. Moreover, they allow deeper understanding and critiquing of synthesis plans, thereby assisting chemists in suggesting new directions for further optimization.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| 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