Geodesic Labs provides TargetOS, an AI‑driven reasoning engine that transforms single‑cell genomics and indication data into scored, de‑risked drug target nominations with structured evidence. The platform automates causal inference, statistical genetics, network biology, and clinical data integration through autonomous AI agents, delivering portfolio‑ready target assessments in weeks instead of months.
Funding
Funding not disclosed
Founders
Product
Problem
Pharmaceutical discovery teams struggle to translate large-scale single‑cell datasets into actionable, portfolio‑ready drug target decisions, often requiring multiple specialized analyses and extensive manual integration of genetic, molecular, and clinical evidence.
Solution
Geodesic Labs offers TargetOS, an AI‑driven reasoning engine that ingests single‑cell data and an indication to generate scored, de‑risked target nominations with structured evidence. The platform orchestrates causal inference, statistical genetics, network biology, and clinical data integration through autonomous AI agents that emulate expert scientific reasoning. Results are delivered in a portfolio‑ready format, enabling rapid evaluation and decision‑making without weeks of specialist effort. By applying rigorous scientific methods at scale, TargetOS surfaces high‑value targets—including regulatory drivers and cross‑cell‑type signals—that conventional workflows often miss.
Target Audience
TargetOS is designed for pharmaceutical and biotech discovery teams that need to evaluate and prioritize drug targets using single‑cell genomics and related clinical data.
Features
- AI agents that automatically assemble and weigh genetic, molecular, and clinical evidence for each candidate target
- Integrated causal inference and statistical genetics pipelines to identify protein‑level causal links from transcriptomic data
- Network biology analysis that uncovers cross‑cell‑type regulatory drivers and signaling pathways
- Automated scoring and de‑risking of targets, producing structured evidence packages for portfolio committees
- End‑to‑end workflow from raw single‑cell datasets to actionable target nominations, reducing manual integration time
- Scalable production‑grade machine learning infrastructure built on scientific ML and physics‑informed methods