PerturbAI uses large-scale in vivo CRISPR screens combined with AI to create perturbation atlases that map how disease genes affect cell types and neural circuits. These datasets train models that predict the impact of genetic changes and drug candidates, helping pharma and biotech teams prioritize targets and accelerate drug discovery.
Funding
Funding not disclosed
Founders
Product
Problem
Understanding how disease-associated genes influence cellular behavior and neural circuit function in living organisms is limited by traditional in vitro assays and static genetic studies, which cannot capture the dynamic, organism-wide effects needed for effective drug discovery.
Solution
PerturbAI combines large-scale in vivo CRISPR perturbation with artificial intelligence to generate comprehensive perturbation atlases that map the causal relationships between genes, cell types, and neural circuits. These organism‑scale datasets train AI models capable of predicting how genetic modifications and drug candidates will reshape biological systems. By providing mechanistic insight into gene‑phenotype links, the platform enables researchers to prioritize therapeutic targets and design next‑generation drugs with higher confidence and reduced experimental cycles.
Target Audience
Primary customers are pharmaceutical R&D teams, biotech companies, and academic research groups focused on target validation, functional genomics, and neurobiology-driven drug discovery.
Features
- Scalable in vivo CRISPR screening that perturbs thousands of disease genes across whole organisms
- High‑resolution mapping of gene effects on individual cell types and neural circuit activity
- AI training pipeline that converts perturbation atlases into predictive models of gene and drug impact
- Causal inference framework linking genetic perturbations to phenotypic outcomes at cellular and systems levels
- Integrated data platform for storing, visualizing, and querying organism‑scale perturbation datasets