Scalable Inference-Time Annealing with Surrogate Likelihood Estimators
This work provides a more scalable and efficient method for sampling molecular Boltzmann distributions, which is a significant problem for computational chemists and biophysicists.
This paper addresses the challenge of efficiently sampling the Boltzmann distribution of molecules by introducing Scalable Inference-Time Annealing (SITA). SITA retrains flow-based models to generate samples at progressively lower temperatures, utilizing an energy-based model for fast surrogate likelihoods, thereby avoiding costly divergence terms. The method achieves state-of-the-art performance on Alanine Dipeptide and Alanine Tripeptide.
A long standing challenge in computational chemistry and biophysics is efficiently sampling the Boltzmann distribution of molecules. Advances in generative modeling have been proposed to address the limitations of conventional sampling techniques by eliminating the computational cost of simulation. A promising direction is iteratively finetuning diffusion models along a temperature ladder whereby training data is generated via importance sampling during inference-time annealing. Unfortunately, these methods require computing a divergence over the score field to estimate importance weights, rendering them intractable for larger systems. Here we present scalable inference-time annealing (SITA), which retrains flow-based models to generate samples at progressively lower temperatures using an energy-based model to facilitate fast surrogate likelihoods. We demonstrate state-of-the-art performance on both Alanine Dipeptide and Alanine Tripeptide while avoiding costly divergence terms. Our code is available at: https://github.com/countrsignal/sita.git