Accelerate science, from discovery through translation

Claude helps pharma companies, biotech startups, and research institutions move faster, while maintaining the accuracy your work demands.

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"At AstraZeneca, AI is a defining force reshaping how we operate across R&D end-to-end. Through R&D powered by AI, we can derive deeper biological insights that help us push the boundaries of science to deliver life-changing medicines, including novel approaches for patient selection. Claude's commitment to mechanistic interpretability provides a safe, secure, and steerable model to empower our science-first approach."

Dr. Jorge Reis Filho, Chief of AI for Science Innovation, AstraZeneca R&D

"By reducing manual burden and enabling greater scale across our operations, our partnership with Anthropic will empower our teams to focus more time on high-value scientific and strategic work, with the goal of accelerating our path to patient impact."

Tahamtan Ahmadi, M.D., Ph.D, Executive Vice President and Chief Medical Officer, Genmab

"Veeva AI is industry-specific agentic AI that leverages Veeva's deep applications, data, domain expertise, and Anthropic's Claude. This unique combination allows us to bring the transformative promise of AI to life sciences at scale."

Andy Han, Senior Vice President, Veeva AI

“Opus 4.5 is an incredible model and a great choice for computational biology. The model is excellent at coding, reasoning about biology, and understanding scientific figures.”

Andrew White, CTO, Edison Scientific

"Commercial pharma teams are entering an era where real-time clinical intelligence and AI agents fundamentally change how therapies reach the right patients at the right time. Anthropic's models are unmatched in their reasoning capabilities and safety design. This collaboration accelerates our shared mission to ensure that clinical-grade agentic AI becomes a trusted, transformative force across global pharma."

Chris Mansi, MD, CEO and co-founder of Viz.ai

“Broad Institute scientists pursue the most ambitious questions in biology and medicine, and we create and share tools to help empower scientists everywhere. We’ve been working closely with Manifold on the next generation of the Terra.bio platform—Terra Powered by Manifold. Among the most exciting of these upgrades are AI agents built on Claude that enable scientists to carry out tasks at an entirely new scale and efficiency and explore scientific domains in previously impossible ways.”

Heather Jankins, Head of Data Science Platform

“Accessing 10x’s single cell and spatial analysis capabilities has traditionally required computational expertise - from writing command-line scripts to managing high-performance computing systems. Now, these same tools can respond to questions asked in plain English. Using Claude, researchers can now perform common analytical tasks—including aligning reads, generating Feature Barcode matrices, performing clustering and other secondary analysis—through a conversational interface that complements traditional computational workflows, making it faster and easier for researchers to engage directly with their data. Claude lowers the barrier to entry for new users and scales to meet the needs of large-scale analyses for advanced research teams.”

Serge Saxonov, Co-founder and CEO

“AI in R&D only works through an ecosystem. Anthropic is doing this right, bringing together the best technologies while putting access, governance, and interoperability first. Benchling is uniquely positioned to contribute. For more than a decade, scientists have trusted us as the source of truth for experimental data and to modernize their workflows. Now we're building AI that powers this next chapter of R&D.”

Ashu Singhal, Co-founder and President

“Claude, paired with internal knowledge libraries, has become integral to Sanofi’s AI-driven transformation and is used by the majority of Sanofians on a daily basis within our internal Concierge app. We are seeing significant efficiency gains across the value-chain by optimizing our processes, while our enterprise-wide deployment has enhanced how our teams work. This collaboration with Anthropic augments human expertise to deliver life-changing medicines faster and more effectively to patients worldwide.”

Emmanuel Frenehard, Chief Digital Officer

“We’ve consistently been one of the first movers when it comes to document and content automation in pharma development. Our work with Anthropic and Claude has set a new standard—we’re not just automating tasks, we’re transforming how medicines get from discovery to the patients who need them.”

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Louise Lind Skov, Director Content Digitalisation

“Claude Code has become a powerful accelerator for us at Schrödinger. For the projects where it fits best, Claude Code allows us to turn ideas into working code in minutes instead of hours, enabling us to move up to 10x faster in some cases. As we continue to work with Claude, we are excited to see how we can further transform the way we build and customize our software.”

Pat Lorton, Executive Vice President, Chief Technology Officer, and Chief Operating Officer

“At Manifold, our mission is to power faster, leaner life sciences. Building with Claude has enabled us to develop AI agents that translate questions in the semantic space of scientists to execution in the technical space of specialized datasets and tools. Together, we’re transforming how life sciences R&D will happen in the years ahead.”

Sourav Dey, PhD, Co-founder and Chief AI Officer

“At PwC, we believe responsible AI isn’t just a technology opportunity—it’s a trust imperative. We’re proud to pair our deep sector insight with Claude’s agentic intelligence to reimagine how clinical, regulatory, and commercial teams operate. Together, we’re not just streamlining processes—we’re elevating quality, accelerating discovery, and building systems where confidence scales alongside innovation.”

Matt Wood, US and Global Commercial Technology and Innovation Officer

“When we set out to create an AI agent that could automate bioinformatics analyses, we focused on three key factors to decide what model provider to use: top-ranking at software development, aligned with life sciences, and support for startups. We evaluated half a dozen platforms, and it became clear that Claude was the standout leader. We’re excited to continue this collaboration and bring cutting-edge AI Agents into the world of biotech research.”

Alfredo Andere, Co-founder and CEO

“Claude has been an invaluable progress multiplier for Axiom as we build AI to predict drug toxicity. We’ve used billions of tokens in Claude Code, and use it to write many of our PRs. More recently, Claude agents with MCP servers have become core tools for our scientific work. With the right MCPs, Claude agents can directly query databases and storage services to interpret, transform, and test correlations in data, and help us identify what data and features are most useful for predicting clinical drug toxicity.”

Alex Beatson, Co-founder

“At EvolutionaryScale, we’re building next-generation AI systems to model the living world. Anthropic’s frontier models accelerate our ability to reason about complex biological data and translate it into scientific insight, helping us push the boundaries of what’s possible in life science discovery.”

Sal Candido, Co-founder, Chief Technology Officer
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Built for breakthrough science

AI you can trust

Claude models are safe, secure, and reliably accurate, so you can handle clinical trial data and proprietary compounds with confidence.

Built for scientific complexity

Analyze genomic data, synthesize findings across hundreds of papers, and design experiments with full citations. Claude matches the complexity of modern drug development.

Made for R&D

Claude connects to Benchling, PubMed, 10x Genomics, and your lab platforms—so every scientist can accelerate their work from discovery through regulatory submission.

Your partner in discovery

We collaborate with pharma leaders and research institutions to make sure Claude evolves with the needs of life sciences and advances real scientific discovery.

Case study

Novo Nordisk accelerates clinical documentation and drug development with Claude

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Livestream

Tune in to watch Anthropic CEO and Co-founder Dario Amodei share his vision for AI in healthcare and life sciences, along with an executive customer panel.

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How life sciences teams use Claude

View prompt

Phase 2 Clinical Trial Protocol

ABX-7291 for Treatment of Parkinson's Disease | DRAFT v0.1

Study Synopsis

Protocol Title: A Phase 2, Randomized, Double-Blind, Placebo-Controlled Study to Evaluate the Efficacy and Safety of ABX-7291 in Patients with Early-Stage Parkinson's Disease

Sponsor: [Sponsor Name]

Phase: Phase 2

Indication: Early-stage Parkinson's Disease (Hoehn & Yahr Stage 1-2)

Study Duration: 24 weeks treatment + 4 weeks follow-up

Objectives and Endpoints

Primary Objective

Evaluate the efficacy of ABX-7291 compared to placebo in reducing motor symptoms in patients with early-stage Parkinson's disease.

Primary Endpoint

Change from baseline in MDS-UPDRS Part III (Motor Examination) score at Week 24.

Secondary Endpoints
  • Change from baseline in MDS-UPDRS Total Score at Week 24
  • Proportion of patients achieving ≥30% improvement in MDS-UPDRS Part III
  • Change in PDQ-39 (Quality of Life) score at Week 24
  • Safety and tolerability (AEs, laboratory parameters, vital signs)

Study Design

This is a Phase 2, randomized, double-blind, placebo-controlled, parallel-group study. Eligible patients will be randomized 1:1:1 to receive ABX-7291 low dose, ABX-7291 high dose, or placebo for 24 weeks.

Sample Size and Rationale

Total Enrollment: 120 patients (40 per arm)

Statistical Power: 80% power at α=0.05 (two-sided)

Expected Effect Size: 5-point difference in MDS-UPDRS Part III (SD=8)

Dropout Assumption: 15% (based on comparable Phase 2 PD trials)

Rationale: Sample size derived from analysis of 12 comparable Phase 2 trials on ClinicalTrials.gov with similar indications and endpoints.

Regulatory References

  • 21 CFR 312 (Investigational New Drug Application)
  • FDA Guidance: Parkinson's Disease: Developing Drugs for Treatment (2023)
  • ICH E6(R2): Good Clinical Practice
  • ICH E9: Statistical Principles for Clinical Trials

Generated using Clinical Protocol Generation skill

Sources: ClinicalTrials.gov (12 comparable studies), FDA guidance documents

Ready for review with biostatisticians and regulatory affairs

Protocol generation

Draft study protocols, SOPs, and consent documents directly in Benchling. Claude structures experimental workflows while you maintain control over scientific decisions.

View prompt

Research synthesis

Survey hundreds of papers in hours instead of weeks. Claude synthesizes findings across biomedical literature, identifies contradictions, and generates testable hypotheses with verifiable citations.

View prompt
  • Now let me run the QC analysis on the raw feature matrix:
  • Bash(python3 scripts/qc_analysis.pypbmc_test_data/raw_feature_bc_matrix.h5)
    ↳===========================================================
    Single-Cell RNA-seq Quality Control AnalysisFollowing scVerse Best Practices
    … +60 lines (ctrl+o to expand)
  • Excellent! The QC analysis completed successfully. Let me update the visualizations:
  • Read(qc_metrics_before_filtering.png)↳ Read image (732.3KB)
  • Read(qc_filtering_thresholds.png)↳ Read image (210KB)
  • Read(qc_metrics_after_filtering.png)↳ Read image (1.1MB)
  • Perfect! I've successfully completed the QC analysis on your raw feature matrix using the scRNA-qc skill. Here's a summary of the results:QC Analysis SummaryDataset: 2,700 cells × 32,738 genes (raw)Quality Metrics Detected:
    • 13 mitochondrial genes
    • 106 ribosomal genes
    • 13 hemoglobin genes
    MAD-Based Filtering Thresholds:
    • Total counts: 709 - 6,812
    • Genes detected: 367 - 1,821
    • Mitochondrial %: MAD threshold 3.65%, Hard threshold 8.0%
    Filtering Results:
    • 266 cells removed as outliers (9.9%)
    • 2,434 cells retained (90.1%)
    • Gene filtering reduced from 32,738 to 9,487 genes (29.0%)
    Key Observations from Visualizations:
    1. Before Filtering: The data shows typical scRNA-seq quality with most cells having 1,000-2,000 counts and detecting 500-1,000 genes. Mitochondrial content is generally low (<5%), which is excellent.
    2. Thresholds: The MAD-based thresholds are permissive (5 MADs for counts/genes, 3 MADs for MT%), following scverse best practices to avoid over-filtering.
    3. After Filtering: The filtered dataset shows cleaner distributions with tighter clustering, lower mitochondrial content (mostly <3%), and good cell-gene correlation.
    Output Files Created:
    • raw_feature_bc_matrix_filtered.h5ad - Clean filtered dataset ready for downstream analysis
    • raw_feature_bc_matrix_with_qc.h5ad - Original data with QC annotations
    • QC visualization PNGs (before/thresholds/after)
    The data quality looks excellent! You can now proceed with downstream analysis like normalization, dimensionality reduction, and clustering using the filtered dataset.

Bioinformatics and data analysis

Process genomic data and optimize analysis workflows with Claude Code. Claude presents results clearly so you can easily communicate with cross-functional teams.

View prompt

Clinical Safety Summary

Module 2.7.4 | IND-2024-0892

Study Overview

Drug: ABX-4721 (anti-inflammatory biologic)

Phase: Phase 1

Subjects: 48 healthy volunteers

Duration: 12-week duration

Adverse Event Summary

Total subjects with AEs: 23 (47.9%)

Serious adverse events: 2 (4.2%)

Discontinuations due to AEs: 1 (2.1%)

Deaths: 0

Safety Signals for Phase 2 Monitoring

⚠ Hepatic

Elevated ALT (>3x ULN): 3 subjects (6.3%)

All resolved without intervention within 14 days

→ Recommend: LFT monitoring at weeks 2, 4, 8

⚠ Injection site reactions

Mild erythema: 8 subjects (16.7%)

No treatment required

→ Recommend: Document severity grading in Phase 2

Regulatory References

  • 21 CFR 312.32 (IND Safety Reporting)
  • ICH E2A: Clinical Safety Data Management
  • FDA Guidance: Premarketing Risk Assessment

Generated from uploaded AE reports and lab data

All source documents linked for audit trail

Clinical and regulatory strategy and compliance

Generate regulatory summaries and compile safety data across modules with full audit trails. Focus on scientific decisions, not document formatting.

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Claude connects to your research ecosystem

Literature, lab notebooks, analysis platforms, all working together in one place.

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Pro

For everyday productivity
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  • Life sciences connectors and skills
  • Access to file creation (docs, slides, spreadsheets, and PDFs)
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  • Life sciences guides and docs

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  • For organizations purchasing fewer than 20 seats
  • Life sciences connectors and skills
  • Access to file creation (docs, slides, spreadsheets, and PDFs)
  • Single sign-on (SSO)
  • Central billing and administration
  • Claude Code available with premium seats
  • Life sciences guides and docs

Enterprise

For businesses operating at scale
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Everything in Team, plus:
  • More usage*
  • HIPAA-ready infrastructure (added cost)
  • Enhanced context window
  • Compliance API for observability and monitoring
  • Single sign-on (SSO) and domain capture
  • Custom data retention controls

Additional usage limits apply. Prices shown don’t include applicable tax.

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