ForgeBio specializes in AI-based protein engineering to design and optimize proteins for various applications. The company leverages deep understanding of protein structure and function to enhance performance in therapeutic development, diagnostics, and industrial processes. They offer comprehensive services from initial design through advanced characterization to address complex protein engineering challenges.
Funding
Funding not disclosed
Founders
Product
Problem
Biopharmaceutical and industrial developers often face lengthy, costly cycles to design, express, and validate proteins with desired functional properties, limiting speed to market and increasing R&D risk. Traditional protein engineering relies on manual mutagenesis and trial‑and‑error testing, which struggles to explore the vast sequence space efficiently.
Solution
ForgeBio offers an AI‑driven protein engineering service that automates the end‑to‑end workflow from computational design to experimental validation. Their proprietary machine‑learning models generate candidate sequences optimized for stability, activity, or specificity, which are then synthesized, expressed, and characterized using high‑throughput assays. The platform iteratively refines designs based on empirical data, accelerating convergence on high‑performance variants. In addition, ForgeBio provides IP strategy support, helping clients protect novel protein assets. The consultancy model enables partners to integrate these capabilities into their own development pipelines without building in‑house AI expertise.
Target Audience
Primary customers are biotech startups, pharmaceutical R&D divisions, diagnostic developers, and industrial biotechnology firms seeking to accelerate protein‑based product development or improve existing protein assets.
Features
- Deep‑learning sequence generation that predicts structural stability and functional hotspots across diverse protein families
- Integrated in silico screening pipeline combining AlphaFold‑style structure prediction with ligand‑binding and enzymatic activity models
- Automated gene synthesis and high‑throughput expression in bacterial, yeast, or mammalian hosts with real‑time yield monitoring
- Multi‑modal biophysical characterization (DSC, SPR, NMR, activity assays) linked to a data lake for rapid model retraining
- Closed‑loop optimization loop that updates AI models with experimental results to improve subsequent design cycles
- Patent drafting assistance and freedom‑to‑operate analysis for newly engineered protein sequences
- Secure client portal for project tracking, data sharing, and API access to integrate results into existing LIMS or ELN systems
- Flexible engagement models ranging from single‑project contracts to long‑term co‑development partnerships