Gen-COMPAS: Breaking Time Scales with Generative Sampling
Molecular transitions, including protein folding, are notoriously difficult to simulate due to their rarity and the computational demands of enhanced-sampling methods. A new framework called Gen-COMPAS has been introduced, which uses a denoising diffusion probabilistic model to reconstruct transition pathways without predefined collective variables. This approach allows for the identification of…
1 source primary source
Key points
- Gen-COMPAS uses a denoising diffusion model to reconstruct transition pathways.
- It identifies transition states without predefined collective variables.
- Applicable to a range of molecular systems, including a pentameric ion channel.
Read the original at Nature Machine Learning primary sourceOpen source ↗
The headline, key points and digest above were generated by Digest AI's editorial model from the linked sources. Automated summaries can contain errors: the sources are the record. Spotted a mistake? Tell us. Published by Martin K., who runs Digest AI and handles corrections.
More in Research
All →- PyTorch tutorial shows how to cut drone data usage by 94% · 1 src
- Article outlines five practices for building robust Python AI libraries · 1 src
- Vector databases explain how AI retrieves similar data at scale · 1 src
- PhAI Labs introduces JEPA-Anything framework for cross-domain world models · 2 src
- Researchers introduce VIMA for finding disease patterns in tissue data · 2 src
Comments
via GitHub Discussions