[HUGGINGFACE]score: 0.42
SITA: Scalable Inference-Time Annealing for Molecular Boltzmann Sampling
May 31, 2026
SITA retrains flow-based generative models to sample progressively lower-temperature Boltzmann distributions using surrogate likelihood estimators, avoiding the intractable score-field divergence computation required by prior importance-sampling methods. The approach targets scalability to larger molecular systems in computational chemistry.
paper
HOW THIS AFFECTS YOU
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researcherSITA removes a key computational bottleneck in inference-time annealing for diffusion and flow models, making the approach tractable for larger molecular systems.
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healthImproved Boltzmann sampling scalability has direct implications for drug discovery workflows that rely on accurate conformational ensemble generation.