Nature Biotechnology's AI Policy: Springer Nature Rules Meet Biotech's AI-Heavy Workflow
Nature Biotechnology follows Springer Nature's risk-based AI policy, with LLM use documented in Methods, and this page gives guidance on separating research AI from writing AI in biotech manuscripts.
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Quick answer: If you're submitting to Nature Biotechnology, there's a good chance your research already involves AI in some form.
Protein engineering with machine learning, drug discovery pipelines using generative models, CRISPR guide design with deep learning, biotechnology and AI are converging so rapidly that separating "AI as a research tool" from "AI as a writing aid" has become a genuine editorial challenge.
Nature Biotechnology AI Policy at a Glance
- AI authorship: Prohibited. AI tools cannot be listed as authors and cannot take accountability for the work.
- AI disclosure: Use of an LLM should be documented in the Methods section, and authors are asked to disclose AI use for trust and transparency.
- AI-generated images: Restricted. Creating photorealistic images (deepfakes) is not permitted, and generative AI images are not permitted for cover artwork.
- Language editing: Using an LLM to polish language is a permitted low-risk use. Extensive copy editing or writing support is permitted with verification and disclosure.
The standard Springer Nature policy
Nature Biotechnology follows the same AI policy as every other Nature Portfolio journal. It is a risk-assessment framework that applies to authors, peer reviewers and editors:
- AI can't be an author. LLMs and generative AI tools don't meet authorship criteria, they can't take accountability for published work.
- Use of an LLM should be documented in Methods. The policy asks authors to disclose AI use for trust and transparency, so describe which tool you used, how you used it, and which parts of the manuscript it touched.
- Some AI-generated images are not permitted. The policy's red list names creating photorealistic images (deepfakes), and the journal's cover-artwork guidance says images created with generative AI such as DALL-E, Stable Diffusion and Midjourney are not permitted.
- Language polishing is low risk. Using an LLM to polish or refine language is a permitted green use, and disclosure enhances trust and transparency. Extensive copy editing or writing support is an amber use that needs human oversight, verification and disclosure.
- Authors bear full responsibility for all content, including AI-assisted sections.
This policy applies across Nature Portfolio journals, and the Nature Biotechnology policy page reproduces it without journal-specific modifications.
The biotech-specific complication: research AI vs. writing AI
Here's where Nature Biotechnology diverges in practice from, say, Nature Geoscience or Nature Astronomy. A substantial fraction of papers published in Nature Biotechnology use AI/ML as part of the research itself. Your paper might describe:
- A deep learning model for predicting protein-protein interactions
- A generative model for designing novel enzyme variants
- A reinforcement learning approach to optimizing bioreactor conditions
- An NLP pipeline for mining biomedical literature
None of this is covered by the AI manuscript preparation policy. The AI you used as a research tool belongs in your standard Methods section as methodology, not as a disclosure of AI-assisted writing.
But here's where it gets confusing: what if you also used ChatGPT to polish the Discussion section of that same paper? Now you need two separate disclosures:
- Research methodology (standard Methods): "We trained a transformer model on UniProt sequences to predict binding affinity..."
- AI writing disclosure (Methods, separate paragraph): "During preparation of this manuscript, the authors used ChatGPT (GPT-4, OpenAI) to improve the clarity of the Discussion section..."
Mixing these up creates problems. If a reviewer reads a vague statement like "AI tools were used in this study" and your paper is about an AI-based drug discovery pipeline, they'll assume you're describing your research method, not your editing process. Be explicit about the distinction.
Writing the disclosure for Nature Biotechnology
For a paper where AI is the research subject:
"The deep learning model described in this study (ProteinForge v2.1) was developed using PyTorch and trained on curated datasets as described in Methods. Separately, during manuscript preparation, the authors used Claude (Claude 3.5, Anthropic) to improve the readability of the Introduction and Results sections. All AI-suggested text revisions were reviewed by the corresponding author, and the authors take full responsibility for the published content."
For a paper where AI isn't the research subject:
"During preparation of this manuscript, the authors used ChatGPT (GPT-4, OpenAI) to assist with editing the Methods and Discussion sections for language clarity. GitHub Copilot (Microsoft) was used to assist with writing data visualization scripts in Python. All outputs were reviewed and verified by the authors, who take full responsibility for the content of this article."
For a paper with no AI use in writing:
No disclosure needed. You don't have to state "no AI tools were used in manuscript preparation." If your paper uses AI as the research subject, that's described in Methods as methodology, it's not an AI disclosure.
What about computational biology outputs?
A frequent question from Nature Biotechnology authors: do AlphaFold predictions, molecular dynamics visualizations, or other computational biology outputs count as "AI-generated images"?
The answer is no, with caveats.
Computational outputs from real data (not generated imagery):
- AlphaFold or ESMFold protein structure predictions rendered as figures
- Molecular dynamics simulation snapshots
- Phylogenetic trees generated by ML-based alignment tools
- Heatmaps, volcano plots, and other standard data visualizations created with AI-assisted tools (seaborn, plotly)
These are computational outputs from scientific analysis. They represent real data processed through computational methods, which is fundamentally different from asking DALL-E to generate a figure.
Generative imagery the policy restricts or an editor is likely to question:
- Using Midjourney to create a graphical abstract illustrating your research concept
- Using DALL-E to generate a schematic of a biological pathway
- Using any generative AI to create an illustration that doesn't derive from actual experimental or computational data
The line is between computational results (allowed, even if AI-assisted) and generative images. If the image derives from your data, it's a result. If it was generated from a text prompt to create something visual, expect the journal to treat it as restricted: the policy bars photorealistic deepfakes and generative AI cover art, and an editor should confirm anything else before you include it.
How Nature Biotechnology's policy compares to the publisher-wide rules
Aspect | Springer Nature policy | Nature Biotechnology in practice |
|---|---|---|
Policy text | Standard | Identical |
Research AI vs. writing AI distinction | Not explicitly addressed | Important to clarify for authors |
Computational biology figure treatment | General guidance | Needs interpretation for protein structures, simulations |
AI/ML methodology reporting | Standard Methods | Often requires extended Methods for model details |
Code and data availability | Data availability statement required for original research; central code shared with editors and reviewers | Strongly expected for AI/ML papers |
Reviewer expertise | General pool | Reviewers often have AI/ML background |
The last point matters. Nature Biotechnology's reviewer pool includes people who build and use AI/ML models daily. They can tell the difference between human-written and AI-polished text, and they're more likely to notice if your Methods section conflates research AI with writing AI. Getting the disclosure right isn't just about policy compliance, it's about not confusing your reviewers.
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Comparison with other biotech and life science journals
Feature | Nature Biotechnology | Nature Methods | Cell | Science | Nucleic Acids Research |
|---|---|---|---|---|---|
Publisher | Springer Nature | Springer Nature | Cell Press (Elsevier) | AAAS | Oxford University Press |
AI authorship | Prohibited | Prohibited | Prohibited | Prohibited | Prohibited |
Disclosure location | Methods | Methods | STAR Methods | Acknowledgments + Methods | Methods |
AI image restrictions | Deepfakes and generative AI cover art | Deepfakes and generative AI cover art | Yes | Yes | Yes |
Copy editing exemption | Light polishing is low risk; extensive copy editing needs disclosure | Light polishing is low risk; extensive copy editing needs disclosure | Implicit | Yes | Yes |
AI/ML as research common? | Very common | Very common | Common | Common | Very common |
Science (AAAS) initially banned all AI-generated text in January 2023 before switching to a disclosure model later that year. Nature Biotechnology's current policy is the Springer Nature risk-assessment framework with green, amber and red uses, not a blanket ban. Check the journal's AI policy page again before you submit, because the policy text is reviewed periodically.
Consequences of non-disclosure
Enforcement follows Springer Nature's existing integrity processes:
Springer Nature's AI policy says that existing research integrity, ethics and editorial governance processes apply to all AI-related concerns, and editors are asked to intervene where accountability or transparency is unclear. It does not publish a fixed set of consequences. For Nature Biotechnology specifically, a problem with undisclosed AI use in an AI/ML paper would be particularly damaging, because it would undermine confidence in both the writing and the underlying computational work, even if the research itself is sound.
The reputational multiplier: Nature Biotechnology is a small community journal. If you publish AI/ML research and get flagged for undisclosed AI use in writing, the irony isn't lost on anyone. Researchers who build AI tools are expected to understand and follow AI disclosure policies.
Practical advice for Nature Biotechnology submissions
For AI/ML research papers:
- Clearly separate your research methodology (the AI model you built) from your writing tools (ChatGPT, Claude, etc.) in the Methods section
- Use distinct subheadings if needed: "Machine Learning Model" for your research and "AI-Assisted Manuscript Preparation" for your writing disclosure
- If your model's architecture is described in detail, reviewers won't confuse it with a writing tool, but only if you keep the disclosures in separate paragraphs
For experimental biotech papers:
- If you used AI tools only for language editing, a standard one-paragraph disclosure in Methods is sufficient
- If AI helped with bioinformatics code (sequence alignment scripts, structural analysis pipelines), disclose this and confirm independent validation
- Make sure your code is available on GitHub or a similar repository, Nature Biotechnology expects code availability for computational methods
For reviews and perspectives:
- AI can help organize literature and structure arguments, but Nature Biotechnology's editors expect original insight that differentiates your review from a literature summary
- Disclose AI assistance in literature organization if applicable
- Don't use AI to generate the "future directions" section, that's where the editor expects your expertise, not a language model's predictions
Before submission checklist:
- [ ] Research AI and writing AI clearly separated in Methods
- [ ] AI disclosure includes tool name, version, and use case
- [ ] No generative AI images (computational outputs from real data are fine)
- [ ] All code deposited in public repository with documentation
- [ ] Co-authors aware of AI disclosure
- [ ] Computational biology figures clearly labeled as derived from data, not generated
For code and data sharing:
For AI/ML papers, the following is good practice alongside the journal's code and data availability requirements:
- Source code on GitHub, GitLab, or Zenodo
- Trained model weights where feasible
- Training and test datasets (or clear descriptions if proprietary)
- Environment specifications (conda environment files, Docker containers)
This isn't part of the AI disclosure policy per se, but it's closely related. The journal's policies require a code availability statement and peer-review access to custom code that is central to the paper. If you used AI to help write your analysis code, make the code available so reviewers can evaluate what was AI-generated and what was human-written.
A Nature Biotechnology submission readiness check can help you check whether your Nature Biotechnology submission meets the journal's editorial and disclosure requirements before you submit.
What should you do about Nature Biotechnology's's AI policy?
Comply proactively if:
- You used any AI tool (ChatGPT, Grammarly, Copilot) during manuscript preparation
- The journal requires AI use disclosure in the methods or acknowledgments
- Your institution has its own AI use policy that may be stricter
Less concerned if:
- You used AI only for grammar/spell checking (most journals exempt this)
- The journal does not have a formal AI policy yet
- Your use was limited to literature search or reference management
Frequently asked questions
Yes, under Springer Nature's risk-based AI policy. Low-risk assistive uses such as polishing language are permitted, and disclosure is encouraged for trust and transparency. Uses that influence interpretation, or extensive copy editing and writing support, are permitted with human oversight, verification and disclosure. Uses that replace scholarly judgement, fabricate content or lack transparency are not permitted. AI can't be listed as an author, and use of an LLM should be documented in the Methods section.
No. Nature Biotechnology's AI policy page carries the same Springer Nature policy that appears on the Nature Portfolio editorial policies page, so the rules are the same across Nature Portfolio journals: no AI authorship, human accountability, disclosure for trust and transparency, and the green, amber and red risk levels.
Separate your disclosures. The AI/ML model you developed or studied belongs in the standard Methods section as your research methodology. Any AI tools used for manuscript preparation (ChatGPT for editing, Copilot for non-research code) go in a distinct AI disclosure statement within Methods. Keep these clearly separate so reviewers don't confuse your research tools with your writing tools.
The policy does not list computational outputs such as structure predictions among its restricted uses. The red list names creating photorealistic images (deepfakes), and the cover-artwork guidance says images created with generative AI are not permitted. Structure predictions are computational results, like any other model output, and you should describe the prediction method in detail in Methods.
Springer Nature's AI policy says that existing research integrity, ethics and editorial governance processes apply to all AI-related concerns. It does not publish a fixed set of consequences for undisclosed AI use.
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Not a co-author, not an acknowledged contributor with a byline, not "with assistance from". What they do require is a disclosure, and the wording matters. The guide has the statement filled in, with what each publisher asks for beside it.
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