David Baker Patents & Innovation Profile — PatSnap Eureka
David Baker: Patent Portfolio & Innovation Analysis
David Baker is a professor of biochemistry at the University of Washington and director of the Institute for Protein Design, holding 4,715 patents across 8 jurisdictions spanning computational protein structure modelling, protein sequence design, synthetic enzyme design, and self-assembling protein nanostructures, with filings from 2005 to 2024. Awarded the Nobel Prize in Chemistry in 2024, his portfolio is primarily assigned to the University of Washington and covers foundational methods that underpin modern computational protein engineering and AI-driven drug design.
Patent Filing Activity
Peak activity in 2016 with 11 filings, coinciding with landmark de novo protein design publications and UCB Biopharma collaboration.
David Baker's Patent Filing Patterns
Filing activity spans two decades with a clear 2016 peak, followed by a renewed acceleration in 2023–2024 driven by deep learning-augmented protein design methods.
Annual Patent Filings
Peak year 2016 (11 filings) coincides with UCB Biopharma antibody design collaboration and landmark de novo protein design publications.
Technology Domain Breakdown
Computational protein structure modelling (G16B15) is the dominant domain with 15 patents, representing 39.5% of the classified sample.
Core Areas of Innovation
David Baker's patent portfolio spans four major technology domains, from foundational protein structure prediction algorithms to cutting-edge deep learning-based generative design methods.
Computational Protein Structure Modelling
15 patentsPatents in this domain address methods for predicting, designing, and optimising three-dimensional protein structures using computational scoring functions, Monte Carlo search algorithms, and deep learning networks. These methods underpin the Rosetta platform and its successors, including RoseTTAFold.
- Method and system for optimization of polymer sequences to produce polymers with stable, 3-dimensional conformations (US7574306B1, 2005)
- Self-assembling protein nanostructures (WO2014124301A1, 2014)
- Scaffolding protein functional sites using deep learning (US20230416726A1, 2023)
Protein Sequence Design & Antibody Engineering
12 patentsThis domain covers methods for designing specific protein sequences, engineering binding interfaces, and predicting the functional consequences of mutations. The UCB Biopharma collaboration is centred here, with multiple filings directed at de novo antibody design and macrocyclic oligoamide structures.
- De novo antibody design (US12327613B2, granted 2025)
- De novo antibody design (US20200168293A1, 2020)
- De novo designed macrocyclic oligoamides (WO2023137366A2, 2023)
Computational Enzyme & Functional Protein Design
5 patentsThis domain captures Baker's influential work on designing synthetic enzymes — proteins that catalyse reactions without natural counterparts — by identifying catalytic residue geometries, searching protein scaffold databases, and optimising sequences to stabilise desired active sites.
- Synthetic enzymes derived from computational design (US8340951B2, 2008)
- Synthetic enzymes derived from computational design (US9243238B2, 2016)
- Synthetic enzymes derived from computational design (WO2009076655A2, 2008)
Self-Assembling Protein Architectures & Oligomers
5+ patentsA distinct cluster addresses the design of proteins that spontaneously organise into defined nanoscale architectures — icosahedral cages, rings, two-dimensional arrays, and cyclic homo-oligomers — with direct applications in vaccine delivery, drug encapsulation, and synthetic biomaterials.
- General Method for Designing Self-Assembling Protein Nanomaterials (US20130274441A1, 2013)
- Computational Design of Self-Assembling Cyclic Protein Homo-oligomers (US20180137234A1, 2017)
- De Novo Designed Homo-Oligomeric Protein Assemblies (US20240013853A1, 2023)
Computational Biology & Bioinformatics Tools
5 patentsPatents in this domain cover general computational biology frameworks, including software tools and algorithmic methods for protein structure analysis, sequence scoring, and bioinformatics workflows that support the broader Rosetta ecosystem.
- Protein structure prediction and scoring algorithms
- Computational frameworks for protein-protein interaction modelling
- Methods for protein functional site identification and design
David Baker's Highest-Impact IP
The two most heavily cited patents cover self-assembling protein nanostructures, reflecting commercial and scientific uptake in vaccine platforms, drug delivery, and synthetic biology.
| Patent Number | Title | Year | Citations | Assignee | Status |
|---|---|---|---|---|---|
| WO2014124301A1 | Self-assembling protein nanostructures | 2014 | 86 ↑ | University of Washington | — |
| US20150356240A1 | Self-Assembling Protein Nanostructures | 2014 | 38 ↑ | University of Washington | Active |
| US20180137234A1 | Computational Design of Self-Assembling Cyclic Protein Homo-oligomers | 2017 | 27 ↑ | University of Washington | Active |
| US7574306B1 | Method and system for optimization of polymer sequences to produce polymers with stable, 3-dimensional conformations | 2005 | 20 ↑ | University of Washington | Inactive |
| US20130274441A1 | General Method for Designing Self-Assembling Protein Nanomaterials | 2013 | 18 ↑ | University of Washington | Active |
| US20180068054A1 | Hyperstable Constrained Peptides and Their Design | 2017 | 16 ↑ | University of Queensland | Inactive |
| US8340951B2 | Synthetic enzymes derived from computational design | 2008 | 15 ↑ | University of Washington | Active |
| US20190155988A2 | Computational Design of Self-Assembling Cyclic Protein Homo-oligomers | 2017 | 13 ↑ | University of Washington | Active |
David Baker's Research Collaborators
Most Frequent Co-Inventors
Collaboration Highlights
Baker's collaboration network divides into two distinct clusters: Liu Xiaofeng and Jiye Shi (11 joint patents each) anchoring the UCB Biopharma antibody design partnership, while Andrew Wollacott, Daniela Grabs-Rothlisberger, Lin Jiang, and Alexandre Zanghellini (5 joint patents each) represent the computational enzyme design group from the 2008 filing cluster. Beyond patents, Baker's literature network spans Stanford, Harvard, UT Southwestern, the University of Queensland, Seoul National University, and EPFL, signalling that related or overlapping rights may exist at multiple assignee organisations.
- Liu Xiaofeng 11 joint patents
- Jiye Shi 11 joint patents
- Andrew Wollacott 5 joint patents
- Daniela Grabs-Rothlisberger 5 joint patents
- Lin Jiang 5 joint patents
- Alexandre Zanghellini 5 joint patents
Research Literature by David Baker
2,602 papers indexed · Research spans protein structure prediction, de novo protein and enzyme design, and deep learning-based generative protein design methods.
| Title | Year | Citations | Key Institutions |
|---|---|---|---|
| Accurate prediction of protein structures and interactions using a three-track neural network (RoseTTAFold) | 2021 | 4,655 ↑ | University of Washington, UT Southwestern, Stanford |
| Scaffolding protein functional sites using deep learning | 2022 | 408 ↑ | University of Washington, EPFL, Harvard |
| Accurate de novo design of hyperstable constrained peptides | 2016 | 320 ↑ | University of Washington, University of Queensland, NYU |
| RosettaRemodel: A Generalized Framework for Flexible Backbone Protein Design | 2011 | 279 ↑ | University of Washington, Hospital for Sick Children, Lund University |
| Exploring the repeat protein universe through computational protein design | 2015 | 267 ↑ | University of Washington, Lawrence Berkeley National Laboratory, UCSF |
Protein Structure Prediction & Refinement
Encompasses RoseTTAFold and its extensions (RoseTTAFold2, RoseTTAFold All-Atom, RoseTTAFoldNA), NMR-based methods, and CASP competition contributions. The 2021 RoseTTAFold paper has accumulated 4,655 citations, placing it among the most impactful structural biology publications of the past decade.
De Novo Protein & Enzyme Design
Covers the Rosetta enzyme design protocol, repeat protein design, self-assembling nanomaterial design, and constrained peptide design — the same topics that dominate Baker's patent portfolio, demonstrating the tight coupling between his academic publications and patent filings.
Deep Learning & Generative AI for Protein Design
An emerging theme spanning protein hallucination, ProteinMPNN, RFdiffusion, ProteinGenerator, and language model generalisation to novel proteins. Papers in this cluster (2020–2023) are now being translated into the 2023–2024 patent filings on scaffolding functional sites and macrocyclic design.
Patent Jurisdictions
David Baker's portfolio spans 8 jurisdictions, with the United States as the dominant filing market and international coverage concentrated in the antibody design space driven by UCB Biopharma's commercial priorities.
Filing Markets
The United States is the undisputed centre of gravity with 24 filings, consistent with the University of Washington's primary institutional base and US dominance of biotech commercialisation. International filings are concentrated in the antibody design space, driven by UCB Biopharma's commercial priorities in pharmaceutical markets — the relative absence of broad EP and Asian coverage on nanomaterial and enzyme design patents suggests potential freedom-to-operate space in those jurisdictions for companies operating outside the US.
Why David Baker's Portfolio Matters
Strategic implications for patent attorneys, in-house IP teams, and R&D strategists working in protein engineering, therapeutic biologics, and AI-driven drug design.
FTO Considerations
The self-assembling protein nanostructure patents — particularly WO2014124301A1 (86 citations) and its US family members — remain active and broadly claimed. Any organisation designing protein cage architectures for vaccine delivery or drug encapsulation should conduct detailed FTO analysis against these filings. The cyclic homo-oligomer design patents (US20180137234A1, 27 citations, and continuations through to US20250177475A1 filed 2024) are similarly broad and actively maintained, demonstrating the University of Washington's intent to sustain this coverage. The computational enzyme design patents established broad claims on scaffold-search methodology that underlies much of the field.
Prior Art Relevance
With 2,602 papers and a 20-year filing history, the Baker group has generated exceptionally deep prior art across the protein design space. The 2005 polymer sequence optimisation patent (US7574306B1) has accumulated 20 citations across two decades and represents one of the earliest computational protein design claims in the patent literature. Papers such as RosettaRemodel (2011, 279 citations) and the RoseTTAFold paper (2021, 4,655 citations) represent substantive prior art that will be highly relevant to patentability analyses in protein structure prediction, functional site scaffolding, and protein sequence design.
David Baker Patents: Common Questions
Analyse David Baker's Full Patent Portfolio
Access all 4,715 patents, full citation histories, FTO analysis, assignee chains, and real-time monitoring for new filings in computational protein design and AI-driven drug discovery.
References & External Patent Resources
- WO2014124301A1 — Self-assembling protein nanostructures. WIPO PatentScope
- US7574306B1 — Method and system for optimization of polymer sequences. USPTO via Google Patents
- US8340951B2 — Synthetic enzymes derived from computational design. USPTO via Google Patents
- European Patent Office — Baker portfolio search. Espacenet (EPO)
- University of Washington Center for Commercialization — UW CoMotion
- Nobel Prize in Chemistry 2024 — NobelPrize.org
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