AI for Life Sciences: 6 Jobs AI Agents Do for Medical, Regulatory, Safety and BD Teams

Most writing on AI in life sciences is about the lab: protein structures, generative chemistry, image analysis. But most people in a life sciences company don't work in a lab. They answer clinicians' questions, track label changes, find trial sites, review adverse event reports, scout assets to license and look for grant funding. Each of those jobs means searching specialist databases and reading what comes back, and AI agents can now do much of it.

This guide to AI for life sciences walks through six of those jobs. For each one we ran the search on November 6, 2026, using Valyu's datasets (PubMed, ClinicalTrials.gov, FDA drug labels, FAERS adverse event data, NIH RePORTER and the web), checked the key facts at the source, and show what the team should do next. For research and development jobs such as target validation and patent scans, see our companion guide to AI for drug discovery.

If you're an agent: Set up Valyu and make your first search.

If you're a human: Create an account and get an API key.

MCP onboarding: Connect your agent to https://mcp.valyu.ai/mcp and follow the MCP setup guide for authentication.

What AI for life sciences means in practice

AI for life sciences is the use of machine learning and AI agents across pharmaceutical, biotech and medical device companies, from research to commercial launch. Most published examples focus on research: predicting structures, designing molecules, analysing omics data. Outside the lab, most of the work is evidence work: finding the right record in the right database and turning it into a decision.

Six life sciences jobs we ran on November 6, 2026, with the sources searched, what came back and the decision each one supports.

1. Medical affairs: answer a clinician's question with evidence

The problem: a cardiologist asks a medical science liaison whether tirzepatide has outcome data in heart failure with preserved ejection fraction (HFpEF). The answer needs the trial, the result and anything published since.

What we ran: one search across PubMed and ClinicalTrials.gov.

valyu.search(
    "tirzepatide heart failure with preserved ejection fraction and obesity randomized trial outcomes",
    included_sources=["valyu/valyu-pubmed", "valyu/valyu-clinical-trials"],
)

What came back: the registry record for the SUMMIT trial (NCT04847557), a 2026 meta-analysis of GLP-1 and dual GIP/GLP-1 agonists in obese patients with HFpEF, and 2026 reviews of incretin therapies and of tirzepatide with SGLT2 inhibitors in obesity-related HFpEF.

What we checked at the source: the SUMMIT results in the *New England Journal of Medicine*. Cardiovascular death or a worsening heart-failure event occurred in 9.9% of 364 patients on tirzepatide and 15.3% of 367 on placebo (hazard ratio 0.62; 95% CI, 0.41 to 0.95) over a median of 104 weeks.

The decision: the liaison can answer from the primary publication, quoting the composite endpoint and its confidence interval, and point to the newer reviews for context. Any off-label question still goes through the company's medical information process.

2. Regulatory affairs: track a product's label and approval history

The problem: a regulatory lead needs a clean timeline of Leqembi's subcutaneous approvals to brief a market access team.

What we ran: a search of FDA drug labels and the web since June 2025.

valyu.search(
    "Leqembi lecanemab label update subcutaneous autoinjector maintenance dosing",
    included_sources=["valyu/valyu-drug-labels", "web"],
    start_date="2025-06-01",
)

What came back:

• August 29, 2025: Biogen announced FDA approval of Leqembi Iqlik, a subcutaneous autoinjector for maintenance dosing.
• October 2025: Eisai and Biogen announced US availability.
• November 26, 2025: Eisai completed a rolling supplemental submission to the FDA for Leqembi Iqlik.
• July 14, 2026: FDA approved Leqembi Iqlik as an initiation dose, reported as the first subcutaneous starting dose for an Alzheimer's treatment.
• July 2026: the updated label on DailyMed.

The decision: the timeline is ready for the brief, with each step linked to the company release or label. The regulatory lead still reads the current label in full before anything goes to an external audience.

3. Clinical operations: find competing recruiting trials in a country

The problem: a clinical operations team is planning a phase 3 obesity study in Germany. They need to know which recruiting trials will compete for the same patients and sites.

What we ran: a search of ClinicalTrials.gov and the web.

valyu.search(
    "recruiting phase 3 obesity trials in Germany",
    included_sources=["valyu/valyu-clinical-trials", "web"],
)

What came back: recruiting phase 3 obesity trials from AstraZeneca (AZD6234), Roche (enicepatide), Novo Nordisk (NNC0487-0111, AMAZE 1) and Eli Lilly (eloralintide), plus trial directories listing open studies in Europe.

What we checked at the source: the ClinicalTrials.gov API lists 24 recruiting phase 3 obesity trials with German sites. Among the four the agent returned, AstraZeneca's trial has 24 German sites (2,500 participants planned), Roche's and Lilly's 12 each, and Novo Nordisk's 6.

The decision: competition for German obesity sites is heavy. The team should shortlist sites not already running these four studies, and plan enrollment timelines with that in mind.

4. Pharmacovigilance: profile adverse event reports for a product

The problem: a safety scientist preparing a periodic review wants the reporting profile for lecanemab in FAERS.

What we ran: a search of Valyu's FAERS dataset, followed by a count at the source.

valyu.search(
    "lecanemab amyloid related imaging abnormalities adverse event reports",
    included_sources=["valyu/valyu-openfda-drug-events"],
)

What came back: individual FAERS reports listing amyloid-related imaging abnormalities (ARIA), several with hospitalisation and some with seizures, each with report date, age, sex, concomitant drugs and outcome.

What we checked at the source: openFDA, with data last updated October 28, 2026, holds 3,898 reports naming Leqembi or lecanemab. The most frequent reactions are ARIA with oedema or effusion (530 reports), headache (498), ARIA with microhaemorrhages and haemosiderin deposits (489), infusion-related reactions (338) and chills (255).

The decision: the reporting profile matches the boxed warning, which is for ARIA. The safety scientist can read the individual hospitalisation and seizure reports in detail. FAERS counts are spontaneous reports, not incidence rates, so they belong in a signal review, not a frequency estimate.

5. Business development: scout assets in a mechanism

The problem: a business development team wants oral small-molecule GLP-1 agonists in phase 2 that might be available to license, now that the first one is approved.

What we ran: a search of ClinicalTrials.gov and the web since January 2026.

valyu.search(
    "oral small molecule GLP-1 receptor agonist phase 2 obesity licensing",
    included_sources=["valyu/valyu-clinical-trials", "web"],
    start_date="2026-01-01",
)

What came back: news that the FDA approved Lilly's orforglipron for obesity on April 1, 2026. The registry results included phase 2 trials of:

• Aleniglipron (GSBR-1290) from Structure Therapeutics, with three phase 2 studies including a body composition trial.
• RGT001-075 from Regor Pharmaceuticals.
• DA-302168S from Chengdu DIAO Pharmaceutical Group.

What we checked at the source: the sponsors and status of each trial on ClinicalTrials.gov. The four phase 2 trials we checked are all marked completed.

The decision: with an oral GLP-1 now approved, the bar for these assets is clinical differentiation over orforglipron. The team should request phase 2 data from the three sponsors and compare weight loss and tolerability.

6. Grants and academic: find funded work in a field

The problem: a translational scientist at a biotech wants to know who NIH is funding on CAR-T and related cell therapies for lupus, to find collaborators and academic licensing leads.

What we ran: a search of NIH RePORTER.

valyu.search(
    "CAR-T lupus",
    included_sources=["valyu/valyu-nih-reporter"],
)

What came back: 25 NIH grants with titles, organisations, investigators and summaries, including:

• an SBIR grant to MuCommune for in vivo engineering of dual CD19/CD138 CAR-T cells for systemic lupus erythematosus;
• a Celdara Medical project on CAR-NK therapy against autoreactive B cells;
• a University of Texas Health Science Center at Houston project on CD19-redirected NK cells in systemic autoimmunity.

The catch: not every grant was on target. Some were broader lupus immunology. A longer, natural-language query returned irrelevant results; the short topic query worked.

The decision: shortlist the three cell-therapy projects and their investigators for outreach. For grant searches, keep the query to the core topic words.

Go further with DeepResearch: a medical information standard response

Each job above answers one question. Many life sciences deliverables are documents: a standard response letter, a regulatory intelligence brief, a periodic literature review. DeepResearch produces these. It plans the research, searches the datasets you choose, reads the results and writes a cited document in your format, with files such as docx or xlsx.

Here is the medical affairs question from job 1 as a full standard response:

from valyu import Valyu
 
valyu = Valyu()
 
task = valyu.deepresearch.create(
    query="Prepare a medical information standard response on the evidence for tirzepatide in heart failure with preserved ejection fraction (HFpEF) and obesity.",
    mode="standard",
    research_strategy="Use the SUMMIT primary publication and registry record as the core evidence, then meta-analyses and reviews since 2024, then the current US prescribing information for approved indications. Prefer PubMed, ClinicalTrials.gov and FDA labels over news.",
    report_format="A standard response letter: Summary, Approved indications, Pivotal evidence with numbers and confidence intervals, Supporting evidence, Safety, References. Neutral scientific tone.",
    search={"included_sources": ["valyu/valyu-pubmed", "valyu/valyu-clinical-trials", "valyu/valyu-drug-labels", "web"]},
    deliverables=["A docx standard response letter with numbered references"],
)
 
result = valyu.deepresearch.wait(task.deepresearch_id)
print(result.output)  # cited report in markdown
for file in result.deliverables:
    print(file.type, file.url)  # signed download link

The result is a cited letter in your template, plus the Word file, ready for medical review before it is used. The same pattern works for any document your team writes repeatedly: set the research strategy and report format once, then change only the question.

Start from a ready-made workflow

Each team above has a matching curated DeepResearch workflow, with set inputs, a recommended mode and file deliverables:

To browse the full library, open Workflows in the Valyu dashboard. Each workflow's Run button opens the DeepResearch playground with the inputs ready to fill in. To run one from code, pass its slug as workflow_id instead of a query:

task = valyu.deepresearch.create(
    workflow_id="ls-trial-readout",
    workflow_params={"trial": "SUMMIT (NCT04847557)", "context": "tirzepatide in HFpEF with obesity"},
)
result = valyu.deepresearch.wait(task.deepresearch_id)

For worked examples with full requests, see Biotech and pharma workflows and the clinical trial landscape guide in the docs. The workflows quickstart shows how to list workflows, check their inputs and save your own team templates.

Other DeepResearch workflows for life sciences teams:

• Regulatory intelligence brief. Track a product's label changes, approvals and regulator communications across a date range, delivered as a dated timeline in docx.
• Periodic literature review for pharmacovigilance. Run a monthly review of new case reports and studies for a product with start_date set to the last review date, and a csv of new references for the safety team to screen.
• Competitive intelligence pack. Ask for a competitor's pipeline, trial readouts and launch timing, with a pptx summary for the brand team.
• Business development dossiers in bulk. Use the DeepResearch Batch API to run one asset-assessment brief across a shortlist of licensing candidates in parallel.

How to run these jobs yourself

If you don't want to write code, there is another option to connect Valyu to Claude or ChatGPT as a custom connector at https://mcp.valyu.ai/mcp, then ask in plain English, for example: "Search FAERS for lecanemab reports and summarise the most frequent reactions." Our guide to running a literature review in Claude or ChatGPT covers setup.

To build it into a workflow, call the Search API with included_sources set to the datasets each team relies on. Three habits made the examples above reliable:

• Check key facts at the source. We confirmed the SUMMIT result in NEJM, the German site counts in the registry and the FAERS totals in openFDA. Agents find the record fast; a person confirms the number before it is used.
• Match the query to the dataset. Registries and papers handle full questions well. NIH RePORTER worked best with short topic words.
• Use date filters for anything that changes. Labels, approvals and pipelines move monthly. start_date keeps the timeline current.

FAQ

What is AI in life sciences?

AI in life sciences, also called AI for life sciences, is the use of machine learning and AI agents in pharmaceutical, biotech and medical device work. It covers research, such as structure prediction and molecule design, and the evidence work of medical affairs, regulatory, clinical operations, pharmacovigilance and business development.

How is AI used in medical affairs?

AI agents search the literature and trial registries to answer clinicians' questions, then return the key trial and its result with citations. A medical reviewer checks the primary publication before the answer is used.

Can AI help with pharmacovigilance?

Yes. Agents can pull FAERS reports for a product, summarise the most frequent reactions and surface serious cases for review. FAERS counts are spontaneous reports, not incidence, so they support signal detection rather than frequency estimates.

How does AI help clinical operations teams?

Agents can list recruiting trials by condition, phase and country, with sponsors and site counts, so teams can see which studies will compete for patients and sites before they choose countries.

What data sources does AI for life sciences need?

PubMed and preprint servers for literature, ClinicalTrials.gov for trials, FDA drug labels and FAERS for regulatory and safety data, NIH RePORTER for grants, and the web for regulator and company announcements. Valyu searches all of these through one API.

What is the best search API for life sciences AI agents?

Choose one that reaches the records each team relies on: PubMed and preprints, ClinicalTrials.gov, FDA labels, FAERS adverse events and NIH RePORTER, plus the web for regulator and company news. Valyu searches all of these through one API and returns registry, label and adverse event records as structured data. In our 60-question test of search APIs, Valyu, Exa and Parallel were close on accuracy for clinical trials and life sciences questions, so coverage and structured records are what separate them.

Which life sciences team should start with AI agents?

Start with the team that spends the most time searching databases each week. In most companies that is medical information or pharmacovigilance, because their questions are frequent, well defined and answered from a small set of sources: the literature, the registry, the label and FAERS.

How do I connect an AI agent to PubMed, FAERS and ClinicalTrials.gov?

Connect Valyu to Claude or ChatGPT at https://mcp.valyu.ai/mcp for a no-code setup, or call the Search API with included_sources set to the datasets you need, for example valyu/valyu-pubmed, valyu/valyu-openfda-drug-events and valyu/valyu-clinical-trials.

Can AI draft medical information or regulatory documents?

Yes, as a first draft for expert review. Valyu's DeepResearch searches PubMed, ClinicalTrials.gov, FDA labels and the web, then writes a cited document in your format, such as a standard response letter or a regulatory timeline, with a docx deliverable. Medical, regulatory and safety reviewers approve it before use.

What is the difference between AI for life sciences and AI for drug discovery?

AI for drug discovery focuses on finding and developing new medicines: targets, compounds, patents and early safety. AI for life sciences is broader and includes the commercial, medical, regulatory and safety work around products already in development or on the market.

Start with one team's job

Pick the job that costs one team the most reading time each week, run it on a product you know well, and compare the agent's output with what your team found by hand.

Create a Valyu account for free credits, connect it to Claude or ChatGPT at https://mcp.valyu.ai/mcp, or browse the data source catalogue.