EC-Conf: An Ultra-fast Diffusion Model for Molecular Conformation Generation with Equivariant Consistency

TL;DR

EC-Conf is a novel, ultra-fast diffusion model for molecular conformation generation that achieves high-quality results with significantly fewer sampling steps, reducing computational costs substantially.

Contribution

The paper introduces EC-Conf, a highly efficient equivariant diffusion model that generates molecular conformations with only one sampling step, outperforming existing models in speed.

Findings

EC-Conf achieves comparable quality with only one sampling step.

Performance is at least two orders of magnitude faster than state-of-the-art models.

Results demonstrate high efficiency in low-energy conformation generation.

Abstract

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.