BIO.01
Literature & Patent Intelligence
Monitoring of papers, patent filings and competitor pipelines for biotech, every finding linked to its source passage and anything touching freedom to operate routed to patent counsel.
Open pageIndustry
Biotech AI,
cited to the source.
Every claim traceable. A scientist decides. Literature and patent intelligence, lab protocol agents, protein design pipelines and regulatory dossiers for biotech teams. Every claim traces to a source, unpublished data stays on your infrastructure, and a scientist makes every call.
Last reviewed 30 October 2026 · Engineering guidance, not legal or regulatory advice.
In context
AI in biotech means using language, vision and structure models to read the literature and patents, drive lab records, propose candidate molecules and draft dossiers, faster than a team can by hand. It is useful only when every output can be traced to its source and a scientist signs off.
Where it breaks today
01
The evidence outruns the team
Papers, preprints, patent filings and competitor trial registrations arrive faster than any group can read them. The ones that matter are usually phrased in vocabulary nobody thought to search for.
02
Lab knowledge lives in free text
Protocols, deviations and results sit in electronic lab notebooks as prose. The context a new scientist needs, or an AI model would need, is in there but not in a form any system can query.
03
Unpublished data cannot leave
Pre-filing sequences, assay results and draft submissions are the company’s value. Pasting them into a public chat tool is a confidentiality and patentability risk, so most of the useful work stays manual.
OUR APPROACH
AI drug discovery is mostly plumbing
The headline stories in AI drug discovery are about structure prediction and generative chemistry. The daily reality of a biotech is different: weeks lost to reading, reformatting, reconciling and re-typing. We build for that reality first, because it is where a small team gets time back, and because a design model is only as good as the data pipeline feeding it.
Two rules hold across everything we build. An assertion without a citation does not reach the scientist; the system is constructed so that a reference which does not resolve to a real passage, record or filing is dropped, not displayed. And the model proposes while a person decides: which candidate goes to the bench, which paragraph goes into a dossier, which result is real. Where GxP applies, validation and Part 11 or Annex 11 determinations stay with your quality function.
HOW IT DEPLOYS
PHASE 01
Find the slow step
We sit with the scientists and map where time actually goes: literature, notebook entries, data reformatting, dossier assembly.
PHASE 02
Build on your systems
Agents read and write through the tools you already run, such as Benchling, your LIMS, PubMed and patent databases, on infrastructure you control.
PHASE 03
Measure on your data
Recall and citation accuracy are measured on a sample your scientists label, before anyone relies on the output.
What we build
01
Literature & Patent Intelligence
Monitoring of papers, preprints, patent filings and trial registries, with competitor pipelines and freedom-to-operate questions mapped to the exact source passage.
SEE LITERATURE & PATENTS →02
Lab Protocol & ELN Agents
Agents that turn notebook prose into structured, searchable records, draft protocols from prior runs and flag deviations, writing back to your ELN rather than beside it.
SEE LAB AUTOMATION →03
Protein & Molecular Design Pipelines
Orchestration around structure prediction and generative design models, so candidates are generated, filtered, ranked and tracked reproducibly before anything reaches the bench.
SEE DESIGN PIPELINES →04
Regulatory & Grant Dossiers
Drafting support for CTD sections, briefing documents and non-dilutive funding applications, assembled from your own source data with every statement traceable.
SEE DOSSIER DRAFTING →Controls we implement
UNPUBLISHED DATA ON YOUR INFRASTRUCTURE
CITATION ON EVERY CLAIM
NO TRAINING ON YOUR DATA
AUDIT TRAIL ON EVERY RECORD
VERSIONED MODELS AND PROMPTS
These are engineering controls we build into the architecture, not certifications. QQuantum.ai holds no security or GxP certifications, and computer system validation remains your quality function’s to run — see our trust centre.
Life sciences use cases
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BIO.02
Lab Protocols & ELN Agents
Agents that structure electronic lab notebook entries, draft protocols from prior runs and flag deviations, working inside your ELN and never signing or altering a record.
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BIO.03
Protein & Molecular Design Pipelines
Reproducible orchestration of structure prediction, generative design and scoring models, with full candidate lineage and the wet lab as the final judge.
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BIO.04
Regulatory & Grant Dossiers
CTD sections, briefing documents and grant applications drafted only from your own source data, every statement traceable and a writer signing off.
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Questions
Questions about Life Sciences & Biotech.
How is AI used in drug discovery?
In three layers. Structure prediction and generative models propose targets, proteins and molecules. Language models read papers, patents and trial registries so a team knows what is already known and claimed. And agents handle the plumbing: notebook records, data reformatting and dossier drafts. Most of the time a small biotech gets back comes from the second and third layers, and the first layer depends on them for clean data.
What is the role of generative AI in biotech?
Generative AI drafts and proposes: candidate sequences and molecules, protocol drafts, literature summaries, sections of a submission. It does not decide. Every candidate still goes through computational filters and wet-lab validation, and every written claim needs a source a scientist can check. We design systems so the proposal and the evidence for it arrive together, which is what makes generative output usable in a regulated setting.
Can AI work on our unpublished research data?
Yes, if it runs where your data already lives. We deploy models on infrastructure you control, including open-weight models where that is the right trade, so pre-filing sequences and assay data are not sent to a public endpoint or used to train anyone’s model. That matters for confidentiality and for patentability, since an uncontrolled disclosure can count against novelty.
Is AI-generated work acceptable under GxP?
It can be part of a GxP process, but the system then needs validation proportionate to its risk, and the records it touches fall under 21 CFR Part 11 or EU GMP Annex 11. We build audit trails, versioning and human sign-off into the architecture so validation is possible. Whether and how a given system is validated is your quality function’s determination, not ours.
How is this different from your life-sciences research work in healthcare?
Our healthcare life-sciences page covers clinical evidence: extracting cohorts, endpoints and adverse events from trial reports. This section is for the biotech R&D side: patent landscapes and competitor pipelines, the lab notebook, design pipelines and dossiers. The citation architecture is shared; the sources and the users differ.
