LabGenius
Accelerating protein-based drug discovery through an autonomous, closed-loop robotic platform.
Reimagining drug discovery by modeling cell behavior through high-dimensional AI.
Cellarity is a clinical-stage biotechnology company that leverages a proprietary AI-driven platform to design medicines based on cellular behavior rather than single molecular targets. By 2026, Cellarity has solidified its position as a leader in 'Digital Biology,' utilizing high-dimensional single-cell transcriptomics to map how disease disrupts cell states. Unlike traditional target-based approaches that focus on a single protein, Cellarity's architecture uses deep learning to understand the 'cell state transition'—the complex interplay of gene networks that define health versus disease. This allows for the discovery of small molecules that can reprogram diseased cells back to a healthy state. The platform integrates massive biological datasets with predictive algorithms, enabling the identification of novel therapeutic candidates across diverse therapeutic areas including hematology, metabolic diseases, and oncology. Their approach significantly reduces the biological uncertainty inherent in drug discovery by focusing on the fundamental unit of life: the cell. As a Flagship Pioneering company, Cellarity represents a paradigm shift toward systems-level pharmacology, where AI acts as the primary bridge between complex biological data and actionable drug leads.
Uses deep generative models to define the vector between a disease cell state and a healthy cell state in high-dimensional space.
Accelerating protein-based drug discovery through an autonomous, closed-loop robotic platform.
Deciphering the gut-brain axis through AI-driven drug discovery for transformative therapeutics.
Accelerating drug discovery through an end-to-end generative AI pipeline for target identification, molecular design, and clinical trial prediction.
Engineering biology at scale to discover and develop next-generation therapeutics.
Verified feedback from the global deployment network.
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In silico modeling of how billions of molecules might interact with a specific cell's transcriptomic network.
AI models that map mouse or non-human primate cell states to human counterparts to improve translatability.
Ability to layer transcriptomic, proteomic, and epigenomic data into a single unified cell representation.
Unsupervised learning algorithms that identify novel disease drivers without pre-existing biological hypotheses.
Models off-target transcriptomic effects in healthy cell populations to predict side effects early.
Simultaneously optimizes potency, selectivity, and cell-state correction properties of a molecule.
Developing treatments for NASH/MASH where traditional single-target approaches have failed due to disease complexity.
Registry Updated:2/7/2026
Validate results in organoid models.
Identifying therapies for rare blood disorders by targeting the root cellular malfunction.
Overcoming tumor resistance to existing therapies by targeting the 'resistant' cell state.