Novo Nordisk accelerates clinical documentation and drug development with Claude
Accelerate science, from discovery through translation
Claude helps pharma companies, biotech startups, and research institutions move faster, while maintaining the accuracy your work demands.
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.
Made for R&D
Claude excels at tasks like bioinformatics and lab protocol drafting, and connects to the tools you use every day—so every scientist on your team can accelerate their work.
Your partner in discovery
We collaborate with pharma leaders and research institutions to make sure Claude evolves with life sciences needs and advances real scientific discovery.
How life sciences teams use Claude

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.

Protocol generation
Draft study protocols, SOPs, and consent documents directly in Benchling. Claude structures experimental workflows while you maintain control over scientific decisions.
- 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
- Total counts: 709 - 6,812
- Genes detected: 367 - 1,821
- Mitochondrial %: MAD threshold 3.65%, Hard threshold 8.0%
- 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%)
- 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.
- Thresholds: The MAD-based thresholds are permissive (5 MADs for counts/genes, 3 MADs for MT%), following scverse best practices to avoid over-filtering.
- After Filtering: The filtered dataset shows cleaner distributions with tighter clustering, lower mitochondrial content (mostly <3%), and good cell-gene correlation.
- 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)


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.

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.
Claude connects to your research ecosystem
Literature, lab notebooks, analysis platforms, all working together in one place.
Bring Claude to your team
Enterprise
Accelerate R&D with pre-built connections to Benchling, PubMed, 10x Genomics, and more. Dedicated life sciences support for qualified customers.
Claude Developer Platform
Build custom applications to automate drug discovery workflows and data analysis pipelines.
Claude Code
Optimize bioinformatics pipelines and accelerate single-cell data processing. Turn computational biology challenges into working code in minutes.
Watch how life sciences organizations use Claude