AI for Biotech Companies: Research Knowledge, Regulatory Writing and Keeping Unpublished Data Safe

AI for biotech companies: what it means for a growing team

Most writing about AI in biotechnology focuses on AI-first drug discovery companies and the investors who fund them. This guide is for a different reader: a biotech with somewhere between a few dozen and a few hundred people, one or two lead programmes, a small regulatory and quality team, and a board that wants to know what the company is doing with AI.

For that company, AI falls into two groups:

The sections below take the document uses one at a time, then cover intellectual property, data obligations and model choice. Legal and licence sources were checked on 25 October 2026.

Research knowledge: literature, ELN notes and assay reports

A biotech’s scientific memory lives in three places: the published literature, the electronic lab notebook and the reports written after each assay or study. People leave, programmes pause, and two years later someone needs to know why a construct was dropped.

Literature. Retrieval over a curated set of papers lets a scientist ask how a target has been modulated before, which assays others used, or what toxicity findings have been published for a mechanism, with every answer pointing to the paper and passage. The research and literature review use case shows that pattern with citations.

ELN entries. Notebook text is short, informal and full of internal abbreviations. A model with access to the ELN export can answer questions such as which buffer conditions were tried for a protein or which cell lines showed the off-target effect, provided the index respects the same access rules as the notebook itself.

Assay and study reports. Summarising a stack of reports into a programme history, or comparing results across runs, is a common request before a board meeting or a partner call. The research assistant playbook describes how to combine internal reports with web sources in one private assistant.

The value comes from search that respects permissions and cites sources. A confident summary without a link back to the notebook page is worse than no summary, because a scientist cannot check it.

Target and competitor intelligence

Small teams have to watch a crowded field: competitors’ trial registrations, conference abstracts, patent publications, press releases and financing news. Language models are good at turning that stream into a weekly digest.

Typical outputs:

• a table of competing programmes against the same target, with modality, stage and latest disclosure;
• a summary of new patent publications in a technology area, with claims grouped by approach;
• an alert when a trial registry entry for a competitor changes its endpoints or enrolment target;
• a one-page profile of a potential partner before business development calls.

Public information carries little confidentiality risk, so a general assistant can do much of this. The risk sits in the questions. A search history that reveals which targets the company is exploring is itself confidential, and some companies keep competitive analysis on internal systems for that reason. The enterprise R&D chatbot use case outlines an internal assistant for innovation teams.

Grant and investor documents

Grant applications. Many early-stage biotechs depend on SBIR, STTR and other public funding. NIH’s notice NOT-OD-25-132, effective from the 25 September 2025 receipt date, says NIH will not consider applications substantially developed by AI, or containing sections substantially developed by AI, to be the applicant’s original ideas, and it warns that AI use can lead to plagiarism or fabricated citations. The same notice limits each principal investigator to six new, renewal, resubmission or revision applications per calendar year. AI can still help with formatting, checking a draft against the funding announcement and tightening language, but the science and the writing need to be the team’s own.

Investor materials. Board decks, quarterly updates and data room documents for a financing round combine scientific results with financial plans. A model can draft an update from the programme history and check that figures match the latest model. The data is among the most sensitive the company holds, because it mixes unpublished results with financial projections, so it belongs on a system with strict access controls.

Regulatory writing for IND modules

An IND follows the Common Technical Document structure set out by ICH: regional information in Module 1, summaries in Module 2, quality in Module 3, nonclinical study reports in Module 4 and clinical study reports in Module 5. FDA requires commercial INDs to be submitted in eCTD format. For a small regulatory team, the volume of writing is the bottleneck.

Where AI helps:

• Nonclinical summaries. Drafting Module 2 nonclinical overview and summary text from finished study reports, with every statement referenced to the report section.
• Quality sections. Assembling Module 3 drafts from CMC reports, batch records and specifications supplied by the CDMO, and flagging inconsistencies between documents.
• Investigator’s brochure and protocol. Keeping the brochure, the protocol and the Module 2 summaries consistent as data changes.
• Responses to FDA questions. Finding every earlier statement on a topic across the submission before drafting a reply.

FDA’s January 2025 draft guidance on AI for regulatory decision-making leaves two things out of scope: AI in drug discovery, and AI that makes work more efficient, drafting a submission included, provided it has no bearing on patient safety, product quality or the reliability of study data. That draft was still not final on 25 October 2026. Drafting help therefore sits within the company’s own quality and review process: a qualified author checks each section and owns its content. The pharma regulatory submissions playbook shows how such a drafting assistant works with source citations.

Quality and GxP documents

Once a programme approaches the clinic, quality documentation grows quickly: SOPs, training records, deviation reports, CAPAs, change controls and supplier qualification files. Much of it is outsourced to CDMOs and CROs, but the sponsor remains responsible.

AI helps by drafting SOPs from templates, checking new SOPs against existing ones for conflicts, summarising deviation trends from a CDMO, and preparing first drafts of investigation reports for a quality lead to complete. When AI output becomes a GxP record, the system that produced it falls under the company’s computerised system validation approach and, in the US, 21 CFR Part 11 controls for electronic records. Our guide to validating AI and LLMs for GxP use explains how to scale that effort to risk, while search over process and technical files is the subject of the use case on engineering and R&D knowledge.

Unpublished data, trade secrets and patent novelty

For many biotechs, unpublished data and know-how are the core asset. Three areas of law and contract decide how AI tools may touch them.

Trade secrets

The US Defend Trade Secrets Act protects information as a trade secret only when two conditions hold: its owner took reasonable steps to keep it confidential, and it has value because others do not know it. Sending assay results, sequences or process parameters to a third-party tool does not automatically destroy that protection, but a company should be able to show that its measures were reasonable: approved tools only, contracts that exclude training and limit retention, and access controls on who can upload what. A written AI policy and logs of what went where are part of that evidence.

Patent novelty

In the US, 35 U.S.C. 102 bars a patent if the claimed invention was patented, described in a printed publication, in public use, on sale or otherwise available to the public before the effective filing date, with a one-year grace period for disclosures made by the inventor or obtained from them. Under Article 54 of the European Patent Convention, the state of the art includes everything made available to the public before the filing date, and Article 55 excuses only disclosures from evident abuse or display at certain official exhibitions within six months before filing. There is no general European grace period.

Whether entering invention details into a particular AI service counts as making them available to the public depends on that service’s terms, who can access the data and the facts of the case. This is a question for patent counsel, not for a blog post. The practical rule many biotechs follow is simple: draft patent material, invention disclosures and pre-filing data stay on systems the company controls.

NIH data and partner agreements

Companies that use controlled-access human genomic data from NIH repositories face specific security terms. Since 25 January 2025, NIH has expected approved users to attest that their systems meet NIST SP 800-171, and to provide the same attestation for any third-party IT system or cloud service used to analyse or store that data, with ISO/IEC 27001/27002 accepted for non-US users unable to attest to NIST SP 800-171. An AI service that processes such data is part of that system.

Partner agreements raise similar questions. Collaboration, licensing and material transfer agreements often restrict how a partner’s data may be used and who may process it. Check them before a partner’s data enters any AI tool, and record which collections hold partner data so access can be limited.

AI tools for biotechnology: scientific models versus general LLMs

The models that predict structures and the models that write documents are different tools with different licences.

AlphaFold 3. The inference code is published under the Apache 2.0 licence. The model parameters are obtained directly from Google and come with separate terms: they and their output are available only for non-commercial use by non-commercial organisations, and the terms prohibit their use in commercial activities, including research on behalf of commercial organisations, and prohibit using the output to train similar structure prediction models. AlphaFold Server is also non-commercial. For commercial use, the AlphaFold 3 README points to Google Cloud.

ESM. The ESM repository, now maintained by Chan Zuckerberg Biohub, lists the current ESMC and ESMFold2 models under the MIT licence. Licences differ between model families and releases, so check the terms attached to the exact version you download.

General LLMs. Open-weight language models can run on the company’s own servers and handle literature questions, ELN search, drafting and summarisation. They do not predict structures or replace scientific models, and their answers about biology need the same scrutiny as any secondary source. Their value is in the documents around the science.

A sensible first 90 days

A biotech does not need an AI programme office to get started. A sequence that works for a small team looks like this:

1. Write a one-page data map. List the categories of information the company holds, such as published literature, ELN entries, assay reports, partner data, NIH controlled-access data, draft patent material, investor documents and GxP records, and state which tools may receive each one.
2. Pick one research knowledge project. Search across one programme’s reports and notebook exports is a good first choice, because scientists can judge the answers quickly and the benefit shows up in weeks.
3. Pick one writing project. A Module 2 nonclinical summary or a set of SOP updates gives the regulatory or quality team a concrete test with clear review criteria.
4. Check every specialist model licence. Before computational staff use a structure or protein model on a company target, confirm that its terms allow commercial research.
5. Log and review. Keep a record of who used which tool on which data, and review it with counsel and the quality lead after the first quarter.

The point of this order is that the policy comes before the pilots, so nobody has to unwind a habit of pasting notebook pages into a consumer chatbot.

How VDF AI fits a biotech company

VDF AI is an enterprise AI platform that a biotech installs on its own servers, in a private cloud tenancy or in an isolated environment. When it is set to use local models only, ELN exports, assay reports, draft IND sections, retrieval indexes, chat history and logs stay within the company’s environment, and no unpublished data reaches an external model provider. The platform carries no regulatory approval or certification itself, so validation and compliance remain the job of the company’s quality system.

Each programme, partner collaboration or financing can get its own knowledge collection, and permission-aware retrieval returns only passages from documents a user is entitled to see. That keeps partner data separate from internal data and investor materials separate from the lab. Governed agents can handle repeatable work such as competitor digests, IND section drafts and deviation summaries, with tools granted per role and a human approval step before any draft becomes a controlled document. Every interaction leaves a log entry naming the user, the model, the source documents and any tool calls, and those logs double as evidence of the reasonable secrecy measures discussed above.

Through the VDF AI Router, the company can run open-weight models on Ollama, vLLM or another OpenAI-compatible server, and switch on an approved cloud model only for work its policy permits, such as summarising public competitor news, with spending limits and a log of each call. Staff can sign in through Microsoft Entra ID, supported natively in on-premises installs; a company standardised on Okta, Keycloak or another SAML or OIDC service connects it via an SSO-aware reverse proxy. All plans include role-based access control. Any seat bundle can open with a three-month on-premises platform trial, long enough to put one programme’s documents through it before a wider rollout.

The healthcare and life sciences solution covers the wider setup, and the guide to private AI for pharmaceutical and life sciences organisations covers the larger-company view. The free AI readiness assessment is a quick way to find out whether data, governance or skills are the likely bottleneck. Treat this article as general information; it is not legal advice on patents, trade secrets or any particular agreement.