Exazyme is an AI-driven protein design tool that utilizes advanced algorithms to predict protein evolutions, enabling biotech companies to achieve superior outcomes with up to 100 times fewer experiments than traditional screening methods. The platform addresses the inefficiencies in protein optimization by allowing users to evaluate multiple protein properties simultaneously while ensuring data security through on-site deployment or API connections.
Funding
$2.1M raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Founders
Product
Problem
Traditional protein engineering methods, such as directed evolution and rational design, are often inefficient, requiring numerous iterative experiments and extensive resources to achieve desired protein properties. Optimizing multiple protein characteristics simultaneously further compounds these challenges, leading to prolonged development cycles and increased costs.
Solution
Exazyme offers an AI-driven protein design platform that accelerates protein engineering by predicting protein evolutions and optimizing multiple properties concurrently. The platform leverages machine learning algorithms to analyze protein data and identify promising candidates with significantly fewer experiments compared to conventional methods. By distilling data patterns into actionable insights, Exazyme enables researchers to evaluate a broader search space and discover proteins with enhanced catalytic speed, stability, affinity, and specificity. The platform can be deployed on-site or connected via API, ensuring data security and intellectual property protection.
Target Audience
Exazyme primarily targets biotech and pharmaceutical companies, as well as research institutions, seeking to accelerate protein engineering, improve protein properties, and reduce the number of experiments required for protein optimization.
Features
- AI-based algorithm predicts protein evolutions from user-provided data sets.
- Supports optimization of multiple protein properties simultaneously, including catalysis speed, stability, affinity, and specificity.
- Built-in data sufficiency test to assess the suitability of input data for AI-driven predictions.
- Offers random mutations, digital deep mutation scans, and fixed candidate list options for job configuration.
- On-site deployment or API connection options for secure data handling.
- Algorithms learn from both successful and unsuccessful experimental results to improve prediction accuracy.