Breaking timescales with generative sampling of conformational transitions
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TL;DR - A generative, committor-guided path-sampling framework reconstructs rare biomolecular conformational transitions without predefined collective variables or brute-force simulation. It enables analysis of otherwise inaccessible transition pathways, thermodynamics, and kinetics at an acceptable computational cost.
- Uses generative sampling to overcome the long timescales associated with rare conformational events.
- Guides path sampling with the committor rather than relying on predefined collective variables.
- Recovers both transition mechanisms and their underlying thermodynamic and kinetic properties.
- Reduces the need for computationally prohibitive brute-force sampling.
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Breaking timescales with generative sampling of conformational transitions
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TL;DR - A generative, committor-guided path-sampling framework reconstructs rare biomolecular conformational transitions without predefined collective variables or brute-force simulation. It enables analysis of otherwise inaccessible transition pathways, thermodynamics, and kinetics at an acceptable computational cost.
- Uses generative sampling to overcome the long timescales associated with rare conformational events.
- Guides path sampling with the committor rather than relying on predefined collective variables.
- Recovers both transition mechanisms and their underlying thermodynamic and kinetic properties.
- Reduces the need for computationally prohibitive brute-force sampling.