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Straight-Line Diffusion Model for Efficient 3D Molecular Generation
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Diffusion-based models have shown great promise in molecular generation but often require a large number of sampling steps to generate valid samples. In this paper, we introduce a novel Straight-Line Diffusion Model (SLDM) to tackle this problem, by formulating the diffusion process to follow a linear trajectory. The proposed process aligns well with the noise sensitivity characteristic of molecular structures and uniformly distributes reconstruction effort across the generative process, thus enhancing learning efficiency and efficacy. Consequently, SLDM achieves state-of-the-art performance on 3D molecule generation benchmarks, delivering a 100-fold improvement in sampling efficiency.
Forward citations
Cited by 2 Pith papers
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Predictive Feature Caching for Training-free Acceleration of Molecular Geometry Generation
Predictive feature caching, borrowed from image diffusion, speeds up molecular flow-matching generation by 2-3x at near-matched quality by forecasting hidden features instead of recomputing them.
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Revisiting Sampling Strategies for Molecular Generation
A maximally stochastic reverse sampler (StoMax) improves molecule stability and validity across DDPM- and BFN-based generators, at a cost in diversity.
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