From a single cell
to the right target
Cell-to-Target is a multi-model, agentic pipeline that reads the biology of disease at single-cell resolution and returns de-risked, tractable drug targets, so the right therapies reach patients years sooner.
- 6
- Specialized agents
- Days
- Not years, to a shortlist
- 3
- Therapeutic areas
Most drug programs fail because they start on the wrong target - not the wrong molecule.
Choosing a target is still an act of intuition, buried in fragmented data and years of manual review. Cell-to-Target treats it as a system: specialized AI agents read the disease directly from patient cells, argue over mechanism, and hand you a shortlist you can defend with the evidence attached.
Immune and inflammatory diseases are combinatorial by nature, driven by many cell types, signals, and pathways acting together.
Yet most drug discovery still hunts for one target at a time, which is part of why so many programs stall in complex disease. RN.AI Therapeutics uses Cell-to-Target, our multi-model agentic AI platform, to identify and prioritise novel combination drug targets across inflammation and immune-mediated disease turning complex biological data into actionable therapeutic hypotheses.
Cell To Target: AI for drug discovery & computational biology.
Cell-to-Target is a multi-model, agentic platform identifying disease-driving populations, screening for druggability, checking candidates against the literature, and perturbation prediction. Every prioritised target comes with the reasoning behind it, so scientists stay in the loop and in control.
Data Ingestion
Cellular and molecular datasets are brought in and harmonised into a high quality curated data foundation layer.
High Attention Modelling
A multiple-instance learning model reads patterns across whole cell populations rather than individual cells, surfacing signals associated with disease state.
Drug Modality Screening
Candidate targets are filtered by whether they can realistically be drugged, and by which modality, focusing effort on tractable biology.
LLM Classification
Language-model agents classify and annotate candidates against the published literature and structured knowledge, adding evidence-based context.
Perturbation Engine
An ensemble of perturbation models predicts how interfering with each target, alone or in combination, is likely to shift cellular behaviour.
Target Prioritisation
Everything is scored and ranked into a prioritised set of targets, each carrying the evidence trail behind it.
The answer was always in the tissue, written at the resolution of a single cell.
Every lung holds thousands of distinct cell states. Read them one at a time, and the disease stops being a mystery and becomes a map you can navigate.
Two ways to find a medicine.
From atlas to clinic.
-
012023–24
Foundation
Platform built. Lung atlas v1. AI models trained on tissue.
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022024–25
Discovery
Targets identified. Combinations found. Validated in vitro.
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032025–26
Development
Leads optimized. Preclinical studies. IND-enabling work.
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042026–27
Translation
Phase I. Clinical validation. Biomarker development.
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052027+
Impact
Approved therapies. Patient access. Global reach.
Let's transform
respiratory medicine.
Whether you're a partner, an investor, or a researcher, we'd like to hear from you.