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
Where 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.