arXiv:2606.01833v1 Announce Type: new Abstract: Generative emulators of protein dynamics produce plausible trajectories at a fraction of the cost of molecular dynamics, but they inherit their training distribution and tend to revisit known states rather than reach rare ones under long-horizon extrapolation. Inspired by classical enhanced sampling, we introduce an implicit, history-dependent bias in the generative space of a pretrained emulator. Specifically, a history-aware score estimator augments the frozen emulator with a distance-weighted bias that steers reverse-time sampling away from pr
The increasing maturity of generative AI combined with persistent computational bottlenecks in traditional molecular dynamics simulation makes this research timely.
This development could dramatically accelerate drug discovery, materials science, and bio-engineering by enabling more efficient exploration of protein dynamics.
Generative AI models can now be steered to explore novel and rare protein states, overcoming a previous limitation of reinforcing known states.
- · Pharmaceutical industry
- · Biotechnology companies
- · AI-driven drug discovery platforms
- · Materials science research
- · Companies reliant on purely traditional molecular dynamics
- · Drug discovery methods with long iteration cycles
Faster and more efficient identification of novel protein conformations and binding sites.
Reduced R&D costs and accelerated time-to-market for new drugs and functional biomaterials.
The development of entirely new classes of therapeutics and industrial enzymes, previously too complex or slow to discover.
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