Enzyme Engineering ML Patents: Who Leads, Where the Gaps Are 2026
Enzyme Engineering with Machine Learning Patents: Who Is Filing and What They Own
47.3% concentration. The top five assignees account for 503 of 1,064 records in scope — nearly half the field sits with a small group of filers. Filings climbed from 43 in 2017 to a peak of 68 in 2024, including a +79% rise between 2021 (38) and 2024 (68). The tail into 2025-2026 (down to 2 in the latest year) reflects publication lag rather than a genuine drop in filing activity.
- 1JANSSEN BIOTECH INC193
- 2CODEXIS INC115
- 3PRESIDENT & FELLOWS OF HARVARD COLLEGE90
- 4UNITED KINGDOM RESEARCH AND INNOVATION58
- 5BASF ENZYMES LLC47
See the full enzyme engineering with machine learning analysis in Eureka
- The complete ranking, not just the top five
- Every IPC branch with its share of the corpus
- The most-cited records, and where claim space is still thin
Common questions about this patent landscape
Who are the leading patent holders in enzyme engineering with machine learning?
The assignee ranking for this dataset covers 100 companies and research institutions, with the leader holding 193 of the 1,064 records in scope. The top five combined account for 503 records, or 47.3% of the field, which is a high concentration for a technology area this broad. The leaders span industrial biotech firms, pharmaceutical companies and academic technology-transfer offices, so no single sector holds the field alone.
Is patent filing in this field growing or slowing down?
Filings grew from 38 in 2021 to a peak of 68 in 2024, a 79% increase over that three-year span, and 2024 is the most recent year that can be read as complete. The apparent drop-off in 2025 and 2026 reflects the roughly 18-month lag between filing and publication rather than a real decline in research activity. Anyone judging momentum from this dataset should treat the last one to two years as still filling in.
What technology areas dominate enzyme engineering patents?
Microorganisms and genetic engineering (IPC class C12N) appears on 51.3% of the 1,064 records, making it the dominant class, followed by peptides and proteins (C07K) at 33.8% and material analysis and testing (G01N) at 24.8%. Bioinformatics (G16B) reaches 22.3%, showing that computational prediction methods are now commonly claimed alongside wet-lab evolution techniques. Because a single record can carry multiple IPC classes, these percentages overlap rather than summing to 100%.
Disclaimer. This analysis is based on Patsnap Eureka data drawn from a limited snapshot of global patent records and is provided for general information and reference only. Patent data carries inherent limitations — recent filings are under-counted because of publication lag, counts may be on a record or family basis, classification and applicant-name data may contain errors or duplicates, and the underlying search query defines the scope shown — so the analysis may be incomplete or inaccurate and may not reflect the full technology landscape.
Nothing here is an exhaustive prior-art, novelty, freedom-to-operate or validity search, nor does it constitute legal, financial or professional advice, and it should not be relied upon as such. Verify independently and review with qualified patent and legal professionals before acting on it.
Method: Filing trend and technology composition. Derived from a Patsnap search on Enzyme Engineering with Machine Learning covering 2015–2026, data cut-off 2026-07-31. Counts reflect published records only and shift as new filings publish. Every share divides by all records in scope. Data: Patsnap Eureka. See the full landscape report.