Harnessing emerging paradigms in chemical engineering to accelerate the development of pharmaceutical products
Bibliographic record
Abstract
Abstract We advanced the concept of biosynthonics five years ago as a solution for resuscitating the productivity of small‐molecule drug pipelines across the pharmaceutical industry. Biosynthonics employs metagenomics and metabolic engineering to mine pharmacoactive structures that are otherwise beyond the reach of synthesis platforms that are currently utilized for drug discovery and lead optimization. Biosynthonics can be harnessed to generate unprecedented quantities of truly novel pharmacoactive structures, which, in turn, can have a game changing effect on the productivity and success rate of drug discovery and early clinical testing in the pharmaceutical industry. However, the impact of biosynthonics on the productivity and cost of product development is somewhat constrained owing to pervading inefficiencies in other parts of the drug discovery and development workflow. Herein, we update our vision for improving the productivity and diminishing the cost of drug discovery and development. Although biosynthonics remains a key piece in our strategy, we discuss how its integration with emerging paradigms in chemical engineering such as information science and materials science, as well as innovations in pharmaceutical manufacturing will drive the next generation of projects in the pharmaceutical industry. In many regards, our updated vision is more pragmatic and leverages the competencies of the pharmaceutical industry more effectively. We have already implemented this vision in our laboratory and early results have been very encouraging. Some of these examples have been detailed in the current work.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".