.png)
.png)
.png)
Virtual cells. Real biology.
Using causal AI to turn single-cell data into interpretable mechanisms and de-risk early stage drug discovery.
Access TwinCellWhat we do
For decades, virtual cells promised faster, cheaper discovery - but most remained opaque and correlational.
TwinCell changes that. By combining patient-derived single-cell data with causal AI, it traces interpretable biological pathways, de-risks targets, and uncovers novel mechanisms.
What makes TwinCell different
Comprehensive Data Foundation
1 billion harmonized single-cell multi-omics data points from public and proprietary sources
Cell Type-Specific Precision
Generate tailored interaction networks (interactomes) for every cell type, enabling accurate pathway discovery
Causal AI-Driven Virtual Cells
DeepLife’s TwinCell model creates interpretable digital twins of cells, ranking causal targets with traceback reasoning
How DeepLife derisks drug development
and accelerates timelines
Cell-type specificity
Comprehensive single-cell omics foundation across diverse cell types implicated in disease, integrating public and proprietary multi-omics data into curated, harmonized atlases.
This provides a deep understanding of disease biology at cellular resolution, enabling precise condition modeling.
Interactome mapping
Cell-type-specific interaction networks generated from cutting-edge AI models, the world’s largest omics database and curated literature.
Filter global networks to reveal disease-relevant pathways, eliminating noise and identifying robust biomarkers while predicting drug effects on specific cell types to avoid off-market failures.
Causal target identification
DeepLife's TwinCell is a Large Causal Cell Model (LCCM) that reveals the key regulators that drive cells from diseased to healthy states. By combining single-cell embeddings with multi-omics networks, TwinCell ensures predictions follow biologically plausible pathways, producing targets that are both actionable and mechanistically interpretable.
Our virtual cells test drug or gene perturbations in silico, outperforming traditional approaches in discovering new targets and identifying repositioning opportunities.
From asset strategy to pipeline confidence
Find an indication
for your asset
Prioritize, validate, and expand indications for your drug or target.
Indications are ranked by network proximity, traced through mechanistic pathways, and validated at cellular resolution to surface where your asset is most likely to succeed and scale.
Find novel targets
for a disease
Discover intervention points capable of reversing disease states using causal inference, not correlation.
Targets are ranked by predicted impact, grounded in biological rationale, and designed to reveal opportunities missed by existing research and conventional screens.
What if you could see the cause—not just the correlation?
Traceable pathways. Interpretable predictions. Drug discovery, reimagined.
Contact usWhat our users say:
“I really liked exploring the human interactome with this new layout! I’ve never seen such a tool”
“The tool is very helpful! It's particularly interesting to have an enrichment analysis of nodes”
“Excellent tool - easy to use even without abioinformatic background”
.svg)