Proposes generative pseudo-force fields trained on quadratic pseudo-potentials from noisy equilibria as a time-step-agnostic diffusion variant for efficient molecular conformation generation with high validity on QM9.
Thorben Frank, Oliver T
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A review summarizing AI techniques for protein conformation generation, trajectory modeling, Boltzmann generators, machine learning potentials, and related challenges in scalability and physical consistency.
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Generative Pseudo-Force Fields for Molecular Generation
Proposes generative pseudo-force fields trained on quadratic pseudo-potentials from noisy equilibria as a time-step-agnostic diffusion variant for efficient molecular conformation generation with high validity on QM9.
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Learning Structure, Energy, and Dynamics: A Survey of Artificial Intelligence for Protein Dynamics
A review summarizing AI techniques for protein conformation generation, trajectory modeling, Boltzmann generators, machine learning potentials, and related challenges in scalability and physical consistency.