CellType

Simulate biology.
Predict how your drugs work.

CellType’s AI platform gives R&D teams the power to evaluate compounds computationally. From initial screen to human translation, we identify the drugs most likely to succeed.

Yale UniversityY Combinator

One AI platform. Three critical R&D decisions.

Screen
Virtual compound screening

Predict cellular responses to compounds your lab has never seen. Screen millions of counterfactuals computationally, run physical assays only on the highest-conviction hits.

Safety
Cross-species transfer

Translate biological signals across species. Map animal and in-vitro evidence directly to predicted human toxicity, and kill unsafe programs before they enter the clinic.

Translate
Clinical outcomes

Connect preclinical interventions to patient evidence. Responder signatures, resistance mechanisms and biomarkers to guide patient selection and trial design.

Trained on your data. Deployed in your environment.

Pre-trained on the world’s largest repository of single-cell, spatial and bulk RNA data, then fine-tuned, securely, on your proprietary assays and historical outcomes.

Deployed fully on-prem or in your VPC. Your data never leaves your environment, and is never sent to third-party APIs like OpenAI or Anthropic.

CT-1 deployed

Demonstrated predictive power.

The underlying architecture of CellType has already identified novel, physically validated biology.

To prove its predictions hold in living tissue, the model was tasked with evaluating 4,266 oncology compounds. It prioritized 23 candidates, leading to the physical validation of 3 novel hits in ex-vivo human tumor tissue.

Built by the inventors of Cell2Sentence at Yale, in collaboration with Google DeepMind. Published at ICML.

Case study: virtual screen, patient tumor context 4,266 screened p = 0.05 3 validated hits Predicted antigen presentation score -log10(p)

Published science.

The models behind CellType come out of peer-reviewed work on single-cell biology, language models and causal inference.

One model reads the compound and the cell The compound, as a string O OH NH Cl N N OC(=O)c1ccc2c(c1)nc(Nc 1cccc(Cl)c1)c1ccncc21 The cell, as a sentence of genes TP53 CD74 TPT1 IRF1 LGALS1 MDM2 S100A6 B2M XBP1 ACTB TP53 ACTB FTL XBP1 MALAT1 STAT1 NFKBIA KRT18 B2M TP53 GAPDH HSPA5 CD74 TAP1 CD74 TMSB4X MALAT1EEF1A1TMSB4XACTB RPL13FTLGAPDHB2M VIMCD74KRT18MYC C2S foundation model fine-tuned on your screen Predicted response HLA-A B2M TAP1 CXCL10 STAT1 MYC TOP2A MKI67 CCNB1 Antigen presentation ↑ Cell cycle ↓

The team.

David van Dijk

David van Dijk, PhD

Co-founder & CEO · Yale

Yale Professor of Medicine and Computer Science. 11,000+ citations. Published in Nature, Cell, NeurIPS and ICML. Invented Cell2Sentence.

Ivan Vrkic

Ivan Vrkic

Co-founder · EPFL, Yale

Co-developed Cell2Sentence at Yale, published at ICML and NeurIPS. Led large-scale foundation model training in industry. Built software to control CERN’s Large Hadron Collider.

Lucas Carey

Lucas Carey, PhD

Chief Strategy & Partnerships Officer · Pompeu Fabra, Peking University

Led R&D at Pando Bioscience and an ML team at Ginkgo Bioworks. 19+ years building AI-guided, high-throughput biology platforms.

Shawn Wahi

Shawn Wahi

Founding Research Engineer · Georgia Tech, Yale

Co-developed C2S-Scale at Yale. Led cardiology, oncology and aging research at Yale School of Medicine, published in Circulation and European Heart Journal.

Sravan Ravi

Sravan Ravi, PhD

Founding Research Engineer · Columbia, GE HealthCare

PhD from Columbia University. Built foundation models for medical imaging as Senior AI Scientist at GE HealthCare.

Ready to predict your next program?

Give us a held-out dataset, a quantitative endpoint, and the baseline to beat. We define the evaluation before we ask you to buy anything.