NeuroFold2
Design next-generation enzymes by co-optimizing for catalytic activity, stability, and solubility.
Overview
NeuroFold2 leverages a state-of-the-art hybrid engine, combining generative AI with physics and evolutionary principles. This allows for the simultaneous, high-fidelity optimization of multiple enzyme properties, delivering novel candidates with unparalleled performance.
Run NeuroFold2 on Neurosnap
The NeuroFold2 online webserver allows anybody with a Neurosnap account to run and access NeuroFold2, no downloads required. Information submitted through this webserver is kept confidential and never sold to third parties as detailed by our strong Terms of Use and Privacy Policy.
Features
- Powered by our proprietary NeuroFold2 model.
- Simultaneously optimizes for thermostability, pH stability, solubility, and catalytic reaction rate.
- Uses a hybrid architecture of AI, physics-based simulations, and evolutionary signals.
- Unlocks innovative enzyme designs with significant mutations that lead to enhanced performance.
- De-risks R&D by computationally screening thousands of variants to identify top candidates for wet-lab validation.
- Specialized for active site engineering with full awareness of ligand and substrate interactions.
Statistics
Neurosnap periodically calculates runtime statistics based on job execution data. These estimates provide a general guideline for how long your job may take, but actual runtimes can vary significantly depending on factors like input size or settings used.
| Statistic | Value |
|---|---|
| Credit Usage Rate | loading... |
| Estimated Total Cost | loading... |
| Runtime Mean | loading... |
| Runtime Median | loading... |
| Runtime Standard Deviation | loading... |
| Runtime 90th Percentile | loading... |
| Runtime Longest | loading... |
API Request
Access NeuroFold2 using the Neurosnap API by sending a request using any programming language with HTTP support. To safely generate an API key, visit the API tab of your overview page.
Similar Tools
Explore tools with similar features, categories, and use cases.
Citations
Please cite the original work when using NeuroFold2 in publications or research outputs.
|
Keaun Amani, Michael Fish, Matthew D. Smith, Christian Danve M. Castroverde bioRxiv 2024.03.12.584504; doi: https://doi.org/10.1101/2024.03.12.584504 |
|
Amani, K. Prot2Prop: structure-aware fine-tuning of protein language models for joint prediction of multiple developability properties from protein inputs. bioRxiv (2026). https://www.biorxiv.org/content/10.64898/2026.06.28.735009v1 |
|
Neurosnap Inc. (2022). Neurosnap: An online platform for computational biology and chemistry. Available at: https://neurosnap.ai/ |