A living map of AI-native biotech.
Companies whose core methodology is learned models — neural networks, foundation models, generative AI — applied to drug discovery, diagnostics, and clinical trial design. Editorially curated, not comprehensive. How we classify →
The AI-Native Biotech Tracker
New entries are added weekly. Get the additions, corrections and editorial calls by email. No sponsor placements in the body.
Get updatesWhere AI actually compresses drug development
Clinical compression is modest but real. McKinsey 2025 finds AI-driven site selection accelerates enrollment by 10–15%, NIH's TrialGPT cuts patient screening time by 40% at same accuracy, and AI-prevented protocol amendments save ~260 days per trial on average. Together these translate to roughly 8–12% end-to-end Phase 1–3 compression — meaningful but bounded by biology, which dictates treatment and follow-up duration. FDA review timelines have not yet moved.
| Company | Type | Modality / Focus | Region | Stage | Funding |
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Collective view of clinical-stage assets developed by AI-native biotechs. Programs are assigned to their most advanced phase. Preclinical includes lead-optimization and IND-enabling work; partnered programs run by big pharma are tracked separately under Pharma Deals.
AI-discovered or AI-optimized programs being advanced inside big pharma — either developed internally on AI platforms or in-licensed/acquired from AI-first biotechs. Includes platform partnerships where AI is the explicit basis of the collaboration.
How AI-pharma deal structures are shifting. The early market ran on discovery collaborations — milestone-and-royalty bets on AI finding molecules against a partner’s targets. As the field matured, the deal menu diversified: platform / tech-access licensing, named-asset in-licensing, and infrastructure arrangements emerged alongside the original model rather than replacing it. Every deal is categorized editorially; hover any bar segment for the count.
Category is an editorial classification stored per deal (dealCategory), not an
automated tag. Value totals are ceilings drawn from stated deal structures — many entries are
undisclosed and excluded from dollar sums, so counts are the more reliable signal.
Every tracked company positioned by method rather than money — five lenses (Therapeutic, Platform, Clinical AI, Diagnostic, and the Ecosystem stack). Click any point for its method note and AI-Reach profile. A company can appear on more than one lens.
Companies actively open to pharma partnerships, platform deals, or self-serve access — the AI-bio BD shortlist. Excludes pure-internal pipeline plays and any company in a hiring freeze or restructuring. Use the legend to filter by what kind of engagement you need.
Companies applying learned models to clinical trial operations — patient recruitment, protocol design, digital twins / synthetic control arms, site selection, and trial outcome prediction. This is the layer between drug discovery (Companies tab) and commercialization (deliberately out of scope). Many already-tracked companies (Tempus, Owkin, Dandelion) span both Companies and Clinical AI; here we include pure-play clinical-AI companies.
AI-native biotechs that have been acquired, merged, or folded into larger organisations. Their technology and teams continue inside pharma and platform companies — the exit price, where disclosed, gives a sense of how the market has valued AI-bio capability over time.
What counts as AI-native?
The line between AI-bio and traditional computational drug discovery is genuinely fuzzy, and getting fuzzier as classical platforms add machine learning layers. This tracker takes a clear editorial position to stay useful: we include companies whose core methodology is learned models — neural networks, foundation models, generative AI, large language models trained on biology — applied to drug discovery, diagnostics, gene editing, or clinical data infrastructure.
- Included. Foundation models for protein, RNA, or small-molecule design (Profluent, Generate, Isomorphic, Boltz, Iambic).
- Included. ML on multimodal clinical / hospital data, federated learning, digital pathology (Owkin, PathAI, Caris).
- Included. Generative chemistry combined with physics-based validation, as long as ML is on the critical path (Charm, Relay, Schrödinger, OpenEye post-2022).
- Included. Autonomous lab / AI-scientist agents (Medra, FutureHouse, LILA, Atomistic Insights).
- Excluded. Pure classical FEP / MD / docking platforms with no learned models in the loop.
- Excluded. Traditional CROs that have added AI capabilities as one service among many (e.g., Domainex).
- Excluded. Pure cheminformatics, structural biology, or HPC-as-a-service for chemistry (e.g., classical FEP cloud platforms).
- Excluded. Pharma marketing / commercialization tech (e.g., DTC audience targeting, KOL management SaaS) — downstream of clinical, not part of drug discovery.
- Adjacent (separate tab). Clinical trial AI (recruitment, protocol design, digital twins) — tracked separately in the Clinical AI tab to keep the main Companies list focused on discovery / biology.
- Ecosystem subtypes. The Ecosystem category covers three distinct archetypes that differ in where their moat sits: Patient Data Platforms (Tempus, Owkin) where proprietary multimodal patient data is the differentiator; Generative Bio Platforms (Cradle, Boltz, Converge) where model architecture and training compute are the differentiator; and Research Tooling (FutureHouse, LatchBio, Medra, Potato) where workflow integration with scientists is the differentiator. Selecting the Ecosystem type filter reveals subtype chips.
The classical / AI line moves over time. OpenEye and Schrödinger qualify today because their newer offerings (ROCS X, LiveDesign ML, AutoDesigner, Generative Glide, federated learning integrations) are substantively learned-model approaches, not just physics with a model bolted on. Big Tech AI labs (Meta FAIR, Google DeepMind, NVIDIA, OpenAI, Anthropic) are tracked through their spinouts and partnerships, not as parent-company entries: Isomorphic Labs covers DeepMind, the ESM lineage (originally Meta FAIR, then EvolutionaryScale) now lives at Chan Zuckerberg Biohub as of Apr 2026, NVIDIA shows up in deal records and as a recurring investor. Institutional research orgs (CZI Biohub) are tracked when their AI/bio output is consequential enough to warrant inclusion. Suggestions and rebuttals welcome.