AI Co-Scientists in Pharma: Authors, Reviewers, Results

Most pharma teams already use AI for pieces of the workflow: summarising papers, drafting code, cleaning datasets, writing protocols. What’s changed in 2025 is that AI isn’t staying in its lane.

A recent experiment from the Agents4Science community (described in a December 2025 correspondence in Nature Biotechnology) pushes a more provocative idea: AI agents can act as authors and reviewers inside a scientific conference workflow. Not “a chatbot that helps you write,” but systems that can plan tasks, search literature, use tools, and produce an end-to-end submission or critique.

For drug discovery and life sciences R&D, this matters because the bottleneck isn’t only data or compute. It’s throughput of high-quality decisions: which targets to prioritise, which assays to run, which signals are real, and which results deserve confidence. If AI can credibly help author and review scientific work, pharma can repurpose the same pattern to accelerate research design, analysis, and quality governance—without lowering the bar.

Why “AI authors and reviewers” is more than a headline

Answer first: The Agents4Science idea forces an uncomfortable but useful question: if AI can generate and evaluate scientific artefacts, how do we keep humans accountable while increasing research throughput?

The correspondence describes the rise of AI “co-scientists”—agentic systems built on large language models that can operate semi-autonomously: they call tools, retrieve literature, interact with external databases, and iterate on results. This is a step beyond classic single-task AI (for example, protein structure prediction). It’s workflow AI.

In pharma, workflow AI is the real prize. Drug discovery isn’t one model; it’s a sequence of connected choices:

• Hypothesis generation (targets, mechanisms, biomarkers)
• Study design (in vitro, in vivo, clinical endpoints)
• Analysis (omics, imaging, PK/PD, safety signals)
• Decision memos (go/no-go, portfolio allocation)
• Governance (audit trails, reproducibility, quality systems)

An “AI author” maps neatly onto producing structured scientific outputs: protocols, reports, assay rationales, statistical analysis plans, validation summaries. An “AI reviewer” maps to: challenging assumptions, verifying calculations, checking completeness, and spotting gaps.

The catch is obvious: peer review and regulated decision-making require accountability. That’s why the Agents4Science experiment is useful—it creates a controlled setting to study where AI helps, where it fails, and what guardrails actually work.

What AI agents change in research workflows (and why pharma should care)

Answer first: AI agents shift teams from “prompting” to “orchestrating,” which is closer to how real R&D operates.

Large language models are good at text. Agents add behaviour: planning, tool use, and iterative refinement. In software terms (and this post sits in our “AI in Technology and Software Development” series), think of it like moving from a code snippet generator to a CI/CD-aware engineering assistant that can:

• Open issues, propose changes, run tests
• Pull data from repositories and generate dashboards
• Document decisions and link evidence

Now translate that to life sciences:

Hypothesis generation with traceable evidence

An agent can scan internal reports plus public literature and produce:

• A ranked list of target hypotheses
• Supporting evidence summaries
• Counter-evidence and failure modes
• Suggested experiments to de-risk the top 3

The key is traceability. If your agent can’t cite which datasets, lab notebooks, or publications it relied on, it’s not ready for serious R&D.

Experimental design that’s less “creative” and more reliable

People tend to frame creativity as the core question (“How creative are AI scientist agents?”). In pharma, reliability matters more.

A well-designed agent can propose:

• Controls you forgot
• Confounders you didn’t model
• Sample size heuristics and power considerations
• Practical constraints (assay turnaround, reagent availability)

That’s not glamourous. It’s exactly where projects quietly succeed or fail.

Analysis and QC where mistakes are expensive

The most underused pattern is AI-as-second-pair-of-eyes on analysis pipelines:

• Checks for leakage in ML model training
• Detects impossible values and unit mismatches
• Flags p-hacking patterns or overfitting risks
• Validates consistency across tables, figures, and claims

This is where “AI reviewer” becomes immediately practical. If you’re running ML in drug discovery, you want automated critique before humans see a slide deck.

AI peer review: useful, risky, and still worth doing

Answer first: AI review can raise the floor on consistency, but it can also manufacture false confidence—so the review process must be designed to expose uncertainty.

Journals and conferences often prohibit AI reviewers or AI coauthors, partly because of confidentiality, bias, and accountability concerns. The Agents4Science experiment is interesting precisely because it tests these boundaries in a structured way.

For pharma and clinical research organisations, you don’t need to wait for journal policies to mature. You can deploy the pattern internally, where you control data access and governance.

Where AI “reviewers” help immediately

1. Completeness checks
   • Are endpoints defined?
   • Are inclusion/exclusion criteria explicit?
   • Are datasets described sufficiently to reproduce?

1. Consistency checks

   • Do the claims match the figures?
   • Do the numbers match across tables and summaries?

2. Methodological red flags

   • Confounding variables not addressed
   • Over-interpretation of weak signals
   • Lack of proper negative controls

Where AI “reviewers” can hurt

• Confident nonsense: A fluent critique that’s wrong is worse than no critique.
• Hidden policy violations: An agent might inadvertently include restricted content or unapproved claims.
• Bias laundering: If training data encodes bias, the review may penalise unconventional but valid approaches.

If you’re serious about this, treat AI review as structured QA, not as “peer judgement.” You’re building a quality gate, not replacing human responsibility.

A good internal AI reviewer doesn’t try to sound authoritative. It tries to be checkable.

A practical blueprint: how to deploy AI co-scientists in pharma

Answer first: Start with narrow, high-value workflows and instrument them like software: versioning, tests, access controls, and audit logs.

Here’s what works in practice when teams try to bring agentic AI into regulated or high-stakes environments.

1) Pick one workflow with measurable throughput

Good starting points:

• Literature surveillance for a therapeutic area
• Drafting and validating assay protocols
• Generating first-pass statistical analysis plans
• Automating CSR appendices and consistency checks
• Manufacturing deviation triage and CAPA drafting (with strict guardrails)

Bad starting points:

• “Replace scientific strategy”
• “Run discovery end-to-end”

2) Define roles: author vs reviewer vs operator

Use a separation-of-duties model (borrowed from software security and GxP thinking):

• AI Author Agent: produces the artefact (protocol, report, code, memo)
• AI Reviewer Agent: challenges it with a checklist and adversarial prompts
• Human Operator: approves, signs, and owns the decision

In software development terms: author = developer, reviewer = CI checks + code reviewer, human = maintainer.

3) Require tool-backed outputs

If the agent claims “this biomarker is validated,” it should:

• Pull the supporting study summary from approved sources
• Extract the relevant numbers
• Store them with provenance

A simple rule: no tool call, no trust. Free-text-only answers belong in brainstorming, not governance.

4) Build “tests” for scientific artefacts

Teams already accept unit tests in code. Do the same for R&D documentation:

• Unit checks: units, ranges, missing fields
• Regression checks: do results change unexpectedly between dataset versions?
• Consistency checks: do methods align with reported results?

This is where Ireland’s tech sector strengths—software automation, cloud optimisation, data analytics—map directly onto life sciences adoption. The capability isn’t only scientific; it’s engineering discipline.

5) Put confidentiality and compliance at the centre

If you work in pharma, you’re dealing with IP, patient data, and partner obligations. Your agent architecture should include:

• Role-based access controls (RBAC)
• Data minimisation (only what the task needs)
• Prompt and output logging for audit
• Secure retrieval (approved document stores)
• Clear “no external sharing” boundaries

Even in internal settings, assume every output could be discoverable later. That mindset improves quality.

What to watch in 2026: the metrics that will decide trust

Answer first: Adoption will hinge on evaluation: not vibes, not demos—repeatable metrics tied to error rates and cycle time.

If you’re considering AI co-scientists (or AI agents for drug discovery), track a few hard numbers from day one:

• Cycle time reduction: e.g., protocol draft turnaround from 10 days to 2
• Error interception rate: how many inconsistencies the AI reviewer catches before human review
• Rework rate: how often humans have to redo AI-generated sections
• Reproducibility score: can another team reproduce the analysis from artefacts and logs?
• Compliance pass rate: fewer missing fields, fewer deviations in required sections

One opinionated stance: if your vendor or internal team can’t tell you the model’s error modes and measured failure rates, you’re buying theatre.

Where this fits in the “AI in Technology and Software Development” series

This post is really about a pattern that software teams know well: automate what’s repeatable, instrument what’s risky, and keep humans accountable for judgement calls. Agents4Science puts that pattern into a scientific publication setting. Pharma can apply the same approach to drug discovery, translational research, clinical operations, and even manufacturing quality.

If you’re building AI-enabled workflows in life sciences, the question isn’t whether AI will write and review. It already does, informally. The real question is: will you operationalise it with engineering-grade controls, or let it stay as invisible “prompting” that nobody can audit?

If you’re exploring AI agents for drug discovery or clinical trial operations, a sensible next step is to pilot an internal “author + reviewer” pair on a single artefact type (protocols, SAPs, or analysis reports), measure error interception, then expand.

What would change in your org if every critical scientific document shipped with an automated critique, a provenance trail, and a reproducibility checklist—before it ever reached a human approver?