Instance provides an end‑to‑end platform that converts millions of candidate biological sequences into high‑quality, training‑optimized functional datasets. Users submit sequences and specify desired readouts (e.g., binding affinity, expression levels, variant effects); Instance runs automated wet‑lab experiments, annotates each result with rich metadata, and delivers curated datasets ready for machine‑learning model training or validation. This enables biotech, pharma, synthetic biology labs, and AI research teams to generate large‑scale functional data without expanding their own laboratory infrastructure.
Funding
Funding not disclosed
Founders
Product
Problem
Machine learning models for biology are limited by the scarcity, noise, and small scale of existing functional datasets, making it difficult to train large language models or validate generative designs effectively.
Solution
Instance offers an end‑to‑end platform that transforms millions of candidate biological sequences into high‑quality, training‑optimized functional datasets. Users submit the sequences and specify the desired functional readouts (e.g., binding affinity, expression levels, variant effects). Instance conducts the wet‑lab experiments, annotates each sequence with rich metadata, and returns a curated dataset ready for model training or validation. By handling experimental execution at scale, the platform enables researchers to fine‑tune protein language models, evaluate generative designs, and build proprietary datasets without expanding their laboratory footprint.
Target Audience
Primary customers are biotech and pharmaceutical companies, synthetic biology labs, and AI research teams that require large, high‑quality functional datasets to train or fine‑tune biological machine‑learning models.
Features
- Scalable generation of functional data across binding, expression, and variant‑effect modalities
- Supports thousands to millions of candidate sequences per project
- Automated wet‑lab execution and high‑throughput validation pipeline
- Returns fully annotated datasets with extensive metadata for ML integration
- Optimized data formats and labeling schemes tailored for large‑scale model training
- Reduces the need for in‑house experimental resources while maintaining data quality