  Despite recent advancement in 3D molecule conformation generation driven by
diffusion models, its high computational cost in iterative diffusion/denoising
process limits its application. In this paper, an equivariant consistency model
(EC-Conf) was proposed as a fast diffusion method for low-energy conformation
generation. In EC-Conf, a modified SE (3)-equivariant transformer model was
directly used to encode the Cartesian molecular conformations and a highly
efficient consistency diffusion process was carried out to generate molecular
conformations. It was demonstrated that, with only one sampling step, it can
already achieve comparable quality to other diffusion-based models running with
thousands denoising steps. Its performance can be further improved with a few
more sampling iterations. The performance of EC-Conf is evaluated on both
GEOM-QM9 and GEOM-Drugs sets. Our results demonstrate that the efficiency of
EC-Conf for learning the distribution of low energy molecular conformation is
at least two magnitudes higher than current SOTA diffusion models and could
potentially become a useful tool for conformation generation and sampling. We
release our code at https://github.com/zhi520/EcConf.
