AI for patent sequences: Avoiding costly sequence errors

At Evolve we build our own AI agents for pharmaceutical IP. In biotech patents, few things matter more than getting the sequences of your molecules right, and sequence errors in patent applications are far more common than most people expect. Often the error sits in a sequence that is not part of a key embodiment and does no harm. Sometimes, though, it sits in the lead embodiment that is supposed to protect your blockbuster product, and that can be a very expensive mistake. We have therefore built a set of AI agents for working with patent sequences. Athena, our strategy agent, keeps the sequence work aligned with the IP strategy for the case or family. Darwin, our drafting agent, turns the sequences in an application into a WIPO Standard ST.26 sequence listing. Gilbert, our quality checking agent, checks that the claims, the listing and the specification really cover the lead candidate, at the drafting stage and throughout prosecution. Albert extracts and checks the sequences in granted patents.

Why getting patent sequences right matters

For a biologic, the sequence is often the invention. Antibodies, CARs, T cell engagers, mRNA constructs and gene therapy vectors are defined in the claims by the sequence of their various domains. If the sequence listing is wrong by a single letter, the claims can therefore define something other than what the applicant meant to protect. In the worst case, this can mean that the patent does not cover the commercial product.

The case law shows not only how expensive a sequence error can be but also how difficult it is to put right once the application has been filed.

In the Soliris saga, Alexion’s claim to eculizumab defined the light chain as “consisting of” SEQ ID NO: 4. That sequence included a 22-amino-acid leader sequence, which is cleaved off and is not part of the marketed antibody. Alexion asked the EPO to correct the claim to SEQ ID NO: 4 without the leader, and the Board of Appeal refused (T 1515/20). The Board accepted that antibody chains usually carry a signal peptide, but said the error was not obvious from the application itself. An amendment excluding the leader sequence was also refused for added matter, so the patent was granted with the uncorrected sequence. In a sister application the same correction was refused and the application failed altogether (T 1435/20).

The uncorrected sequence then decided the litigation. The High Court held that the claim did not cover eculizumab, so the biosimilars did not infringe (Samsung Bioepis v Alexion [2025] EWHC 1240 (Pat)). The UPC Court of Appeal also declined to correct the error by interpretation, because neither the error nor the correct version was certain enough to the skilled person, and it thought the claims were more likely than not insufficiently disclosed (UPC_CoA_402/2024 and UPC_CoA_405/2024, 20 December 2024). Alexion is far from the only patentee to get a sequence wrong (T 2126/08, T 1111/02, T 1805/06), and even a successful correction does not always save the patent (T 0580/05).

Sequence errors can also cost you your priority date. In T 1213/05, the priority document contained inadvertent sequence errors. The Board held, following T 70/05, that a priority claim to an incorrect sequence cannot be maintained, whatever the reason for the mistake, and the claims to the corrected sequences fell to intervening prior art (more on T 1213/05).

These errors are easy to make and hard to spot. A listing can contain hundreds of sequences, many of them hundreds or thousands of residues long, and a single wrong amino acid halfway along a 400-residue chain is very easy for a proof-reader to miss.

Getting sequences right is about much more than turning the sequences in the specification into a sequence listing. A listing can only be as good as the specification it comes from, and the harder work starts earlier and goes on for longer. We need to know that the sequence in the application is the lead in the form that will actually be made and sold, without a leader sequence, tag or linker that the product does not have. The CDRs, chains and constructs have to fit together the way the claims say they do, and any nucleotide sequence has to encode the protein it is said to encode. The sequence in the patent should match the one in the priority filing, the INN submission and the regulatory dossier, and the claims need to keep covering that molecule after each amendment during prosecution. For that reason our Sequence Agents are used throughout the life of the patent, and not only at filing.

The limits of WIPO Sequence

Since July 2022, sequence listings have been filed in the ST.26 XML format, usually prepared in WIPO’s own desktop tool, WIPO Sequence. As we have discussed before, it is an improvement on what came before, but it still has real limitations, and in a number of situations sequences still have to be added by hand. The XML files that the patent offices require are also not designed to be read by people, which makes proof-reading a listing against the specification slow and indirect.

Why we use both code and LLMs

A large language model is the wrong tool for copying a sequence. LLMs are probabilistic, so they can drop, add or invent residues in a long string of letters, and they tend to simplify and tidy up text, which is the last thing you want in a sequence listing. As we have argued before, sequence listings should be prepared with code, not with an LLM. Public sequence databases worked out how to handle large volumes of annotated sequence data years ago, and patent practice has been slow to catch up.

Code cannot do the whole job either. Finding every sequence in a patent application means reading it, because sequences turn up in figure legends, tables, claims, examples and the text itself. Whether a sequence needs its own SEQ ID NO, whether a construct counts as a synthetic construct, and whether a claim preamble requires a transmembrane domain that the lead does not have are all questions of understanding the document. That is the kind of reading the latest models now do well.

• Code handles the residues. It builds the ST.26 XML, validates it against the Standard, compares every residue with the specification, checks the sequences against each other and translates coding sequences. The results are exact and can be repeated.
• The LLM handles the reading. It finds every sequence in the application, works out what each one is, reads the claims against the lead candidate and explains each issue in terms the attorney can act on.

Agents that understand biological molecules

Our agents work with the biology behind a sequence and know how the parts of a biologic fit together. An antibody heavy chain is a variable domain followed by constant domains. A CAR runs from a signal peptide through a binding domain, hinge and transmembrane domain to its signalling domains. An mRNA has a cap, untranslated regions, a coding sequence and a poly(A) tail. Knowing this is what allows them to spot a leader sequence left in a mature chain, or a CDR in the wrong place.

Between them, the language model and the code can pick out:

• Molecule type and format. Whether a sequence is DNA, RNA or protein, and whether the molecule is an antibody, scFv, bispecific, T cell engager, CAR, fusion protein, ADC, mRNA, plasmid or viral vector.
• Domains and features. Signal peptides and leader sequences, variable and constant domains, CDRs and framework regions, linkers, hinges, transmembrane and intracellular signalling domains, purification tags and common engineered changes such as Fc mutations.
• Coding relationships. Translation in every reading frame, open reading frames, stop codons in the wrong place, reverse complements, and whether a nucleotide sequence really encodes the protein it is said to encode.
• How sequences relate to each other. Alignments and percentage identity, where a CDR or domain sits within a longer chain, which sequences are exact concatenations of others, and which differ by a single residue.
• Sequence motifs and annotation. Glycosylation motifs, unpaired cysteines, ambiguity codes, modified residues and non-standard nucleotides, and the ST.26 features needed to record them.
• Comparison against a reference. The lead candidate against the claims, the specification against the listing, and the priority application against the later filing.

The language model reasons about the biology, and the residue-level work is done in code that builds on decades of development of bioinformatic sequence analysis tools, so every alignment, translation and comparison is exact and repeatable. The attorney checks every call that matters.

How our Sequence Agents work, step by step

In: sequences, in any form

A FASTA file, a Word or Excel table, a draft specification, a PDF, or an existing ST.26 listing to be checked.

1. Athena aligns the work with the IP strategy

Athena, our strategy agent, keeps a record of the IP strategy for the case or family, so that the sequences, the claims and any later amendments stay in line with what the business needs the patent to cover. Darwin, Gilbert and Albert all work from that strategy.

2. Darwin finds and numbers the sequences

Darwin reads the source material and identifies every sequence that has to be listed, as well as those that should not be. Any SEQ ID NOs already used in a draft are kept, so the claims and description do not need renumbering. The attorney confirms the bibliographic data and any judgement calls.

3. Darwin builds and validates the listing in code

The ST.26 XML is generated by code rather than written by a language model, and is validated against the Standard, including rules that the official schema itself does not check.

4. Every residue is checked

Each sequence is compared with the specification residue by residue. The listing is also checked against itself, for example that a heavy chain is made up of exactly its variable and constant domains and that each CDR sits within its variable domain. Where the text says one sequence encodes another, the translation is tested.

Output: the listing and a readable version

The ST.26 XML, a human-readable listing to proof-read against the specification, and a table of every sequence and its SEQ ID NO.

5. Gilbert checks the claims cover the lead, at every stage

Gilbert Sequence QE starts by checking that the type of molecule in the claims matches the lead candidate, for example that the claims do not require a CAR when the lead is a soluble T cell engager. It then checks the CDRs, the SEQ ID NOs, basis and data support, and grades each issue as critical, high, medium or low. Gilbert repeats the check whenever the claims change, whether before filing, with each amendment and office action response during prosecution, or after grant, for example in opposition or before the patent is enforced.

6. Albert extracts and checks the sequences in granted patents

Albert Sequence reads a granted patent or published application, whether ours or a competitor’s, and pulls out the lead candidates and preferred binders. Rather than relying on the numbering, it maps each one to its SEQ ID NOs by finding where the sequences actually appear, cross-checks any construct tables and looks for sequence errors across the family. It covers antibodies, scFvs, bispecifics, ADCs, CARs and nucleic acid constructs, and we use it for freedom to operate reviews and due diligence.

Next: attorney review and filing

The attorney decides what needs correcting, and the listing is opened in WIPO Sequence for the official validation before it is filed.

The process also works in reverse. Given an existing listing, whether yours or the other side’s, Darwin validates it, checks it against the application and its bibliographic data, and produces a readable version and a report. A filed listing is part of the application as filed, so it is never edited. Instead, Darwin separates defects in the listing itself from defects in the application that the listing simply reproduces, and says which of them can still be corrected.

Do our Sequence Agents replace attorney review?

Getting sequences right is much easier when your patent attorney is part of the team. As your fractional IP counsel, we work directly with your scientists to check that the sequences in the patent match the clinical lead and the sequences in your regulatory filings, such as the INN submission and the BLA, and that the claims keep covering the right molecule throughout prosecution. Alexion got the eculizumab sequence wrong in both its INN submission and its patent, and a mismatch like that is far cheaper to catch early than to argue about in court.

How our Sequence Agents handle your files and client confidentiality

Our AI runs inside the files and software we and our clients already use, so documents do not need to be copied into and out of a separate piece of software.

Our Sequence Agents are built directly on the latest foundational models rather than on a third-party product, which means we can update them as the models improve. Any Evolve attorney can call on them by name when working on a client project.

As with the rest of our AI, there is no storing of users’ data, no training on inputs and enterprise-grade security.

Evolve’s AI agents for pharmaceutical IP

Our Sequence Agents are part of a wider set of AI agents we have built for pharmaceutical IP, each covering a different part of the work:

The Experts

Rose Hughes, MA (Cantab), PhD, EPA

Rose is a biotech and pharmaceutical patent specialist with over a decade of experience in intellectual property. At Evolve, Rose leverages our unique fractional in-house model to provide clients with deep patent law expertise combined with the strategic commercial oversight of senior in-house counsel. Rose’s clients include late-stage cell therapy and clinical stage biotech companies.

With a PhD in Immunology from UCL, Rose applies her technical background to complex innovations in biologics, cell and gene therapies, and the rapidly emerging field of AI-assisted drug development. Previously, Rose held the role of Director, Patents at AstraZeneca, where she was responsible for global IP portfolios and IP strategy at every stage of the pharmaceutical pipeline, from platform development and on-market commercialisation to SPCs and patent term extensions.

A recognised thought leader in the field, Rose has been a regular contributor to IPKat since 2018, offering practical insights into European patent law developments. She is Editor of the CIPA Guide to the Patents Act (10th Edition), a frequent speaker on the epi podcast, a guest lecturer for the Brunel University IP law Postgrad Certificate, and a contributing author to published books A User’s Guide to Intellectual Property in Life Sciences (2021) and Developments and Directions in Intellectual Property Law (2023).

Oliver Lam, CPA, EPA

Oliver provides tailored strategic and commercial advice on a diverse range of technologies, including pharmaceuticals, immunotherapies, gene therapies, RNA therapeutics and diagnostic biomarkers. He has considerable experience in mRNA technology, particularly vaccine formulation, having supported a large international litigation relating to COVID-19 vaccines.

Oliver has experience in providing in-house counsel, having been seconded to a large multinational pharmaceutical and biotechnology company. Oliver also possesses valuable industrial experience having worked in antibody discovery at UCB prior to becoming a patent attorney.

Clients value his energy, pragmatism and ability to deliver bespoke patenting strategies resulting in optimum protection for their businesses.