The Evolving Landscape of Industrial Biocatalysis in Perspective from the ACS Green Chemistry Institute Pharmaceutical Roundtable
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide As the ACS Green Chemistry Institute Pharmaceutical Roundtable (GCIPR) approached its 20th anniversary, the Biocatalysis Focus Team surveyed its member companies to better understand how biocatalysis is currently being leveraged across their pipelines. This article presents an analysis of the dataset collected from pharmaceutical and agrochemical companies, highlighting the evolving biocatalysis landscape with expanding impact of enzyme catalysis driven by protein engineering. The increasing complexity of active pharmaceutical ingredients (APIs) demands efficient and sustainable synthesis routes, prompting the pharmaceutical industry to adopt innovative methodologies. In this context, biocatalysis has emerged as a particularly attractive solution, as it enables streamlined syntheses under mild reaction conditions with intrinsically safer reaction profiles compared with conventional chemistry. Over the past two decades, numerous API manufacturing processes have integrated biocatalysis, leveraging a wide range of enzymes in early drug discovery and route scouting activities. Advances in directed evolution, computational tools, and adjacent technologies now allow for the rapid discovery and optimization, further expanding the use of biocatalysis in pharmaceutical manufacturing.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".