Deepnote empowers biotech and healthtech with AI notebooks for proteomics, pharmacogenomics, target validation, PK/PD modeling, and clinical trial analytics.
Our customers
Keep sensitive biotech data under your control. Deepnote supports regulated workloads with flexible deployment and built-in safeguards.
Single Sign-On (SSO)
Audit logs & versioning
Project secrets & usage controls
Private cloud & on-premise
Customer-managed keys
Restricted egress
21 CFR Part 11-ready
GLP/GxP lineage
Sample Vendor in Gartner® Hype Cycle™ for Collaborative Analytics for 2025
Deepnote is the collaborative AI notebook workspace for life sciences.
Teams working on:
Proteomics
Transcriptomics
Pharmacogenomics
Biostatistical analysis
Use Deepnote to speed drug discovery, target validation, structure-based drug design, and pharmacovigilance.
Real-time co-editing, shared runtimes, built-in data connectors, and governed access help bench scientists and computational biologists move from hypothesis to validated result with less setup and lower cost:
The result is faster biotech AI workflows
Higher confidence in your biotech data
Discovery informatics
Integrate proteomics, transcriptomics, and structural data for structure‑based drug design.
Rank targets, score pathways, and prototype rational drug design heuristics with shared notebooks.
Publish interactive plots so biologists, chemists, and data scientists make decisions together.
Preclinical modeling
Build pharmacodynamics and exposure‑response models with transparent assumptions.
Run dose‑response curves and simulation studies with biostatistical analysis templates.
Compare scenarios in a single workspace so choices are traceable and reproducible.
Connect Deepnote to your data lake and public sources like NCBI. Invite a discovery squad to prototype and explore while keeping data in a single-tenant or on-prem environment.
Build proteomics pipelines and target validation steps
Run biostatistical analysis and PK/PD modeling
Collaborate in AI notebooks that shorten the path from omics to outcomes
Biotech AI
Train models for hit triage, structure–activity prediction, and transcriptomics classifiers.
Track experiments and artifacts, promote winning approaches, and expose results in data apps.
Spatial omics
Orchestrate pipelines for single‑cell and spatial proteomics with ImageJ and CellProfiler.
Standardize feature extraction, QC, and phenotype classification while keeping raw data in place.
Share notebooks that link parameters to figures for faster peer review.
Safety and real‑world evidence
Perform pharmacovigilance signal detection and literature mining across public sources and internal repositories.
Build curation dashboards that capture evidence trails for safety review boards.
Choose multi‑tenant, single‑tenant managed by you or by Deepnote, or on‑prem behind your firewall.
Bring in LIMS exports and assay results without manual driver hunts or one‑off credentials.
Zero setup in the browser. Spin up CPU or GPU machines on demand with auto‑shutdown.
Embed models for rational drug design, target validation, and pharmacodynamics exploration.
Real‑time multi‑user editing, inline comments, and shared program state for faster reviews.
Available as single‑tenant or on‑prem so trial data never leaves your control
1
Receive site data from EDC, ePRO, LIMS, and imaging into S3 inside your VPC. Use private endpoints and IP allowlisting.
2
Enforce SSO, role‑based access, project‑level secrets, and audit logs. Support customer‑managed keys and restricted egress.
3
Run biostatistical analysis, PK/PD models, and interim looks in reproducible notebooks.
4
Share read‑only reports and interactive apps with reviewers without moving data out of your environment.
Capability
Deepnote for Life Sciences
Classic Jupyter
Data connectivity
Built-in connectors to warehouses, lakes, and object stores
Python and SQL together
Central secrets vault
Drivers per user
Manual secrets
Limited schema discovery
Collaboration
Real‑time multi‑user editing
Comments, shareable projects, and shared program state
Single editor by default
Handoffs via files or Git
AI‑driven coding
In-cell AI actions for code generation, debugging, documentation, and refactors
No native AI functionality
Resource optimization
On-demand CPU and GPU with auto-shutdown
Persistent environments that restart cleanly
Manual kernel and server management
DIY shutdown scripts
Enterprise governance
SSO, RBAC, audit logs, and workspace policies
Scheduling and app publishing
Add‑ons or self‑built solutions required
Single-tenant and on-prem
Single-tenant cloud or on-prem in your AWS account for regulated workloads and data residency
DIY hosting and integration work
Publishing and apps
Promote notebooks to interactive data apps for non-coders with permissions
Not built in. Requires external tools.
Luca Naef, CTO
Maximilian Strauss, Co‑founder & CTO
Book a demo to review deployment models, governance controls, and your migration plan from Jupyter. We will help you scope a two‑week pilot that proves value on a real drug development workflow.
