Straight-Line Diffusion Model for Efficient 3D Molecular Generation
This work addresses the computational bottleneck of slow sampling in 3D molecular generation for drug discovery and materials science, offering a significant efficiency gain.
The paper tackles the problem of slow sampling in diffusion-based molecular generation by introducing a Straight-Line Diffusion Model that follows a linear trajectory, achieving a 100-fold improvement in sampling efficiency and state-of-the-art performance on 3D molecule generation benchmarks.
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.