Accelerating the Generation of Molecular Conformations with Progressive Distillation of Equivariant Latent Diffusion Models
Track: Machine learning: computational method and/or computational results
Keywords: molecular structure, diffusion models, geometric deep learning, accelerated sampling, progressive distillation
TL;DR: Accelerating the generation of molecular conformations from equivariant latent diffusion models through progressive distillation leads to up to 7.5x speed gains while maintaining generation quality.
Abstract: Recent advances in fast sampling methods for diffusion models have demonstrated significant potential to accelerate generation on image modalities. We apply these methods to 3-dimensional molecular conformations by building on the recently introduced GeoLDM equivariant latent diffusion model (Xu et al., 2023). We evaluate trade-offs between speed gains and quality loss, as measured by molecular conformation structural stability. We introduce Equivariant Latent Progressive Distillation, a fast sampling algorithm that preserves geometric equivariance and accelerates generation from latent diffusion models. Our experiments demonstrate up to 7.5x gains in sampling speed with limited degradation in molecular stability. These results suggest this accelerated sampling method has strong potential for high-throughput _in silico_ molecular conformations screening in computational biochemistry, drug discovery, and life sciences applications.
Submission Number: 56
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