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

1 Aug 2023arXiv:2308.00237archive 2025-07-28

Zhiguang Fan, Yuedong Yang, Mingyuan Xu, Hongming Chen

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.

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Atoms_to_Idx deeplearningps/ecconf/EcConf/graphs/mol.py official repository ran Apache-2.0 (permissive) · 8d4c20476da4cb96 · report
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timesteps_schedule deeplearningps/ecconf/EcConf/model/consistency/consistency_models.py official repository ran fingerprinted Apache-2.0 (permissive) · 39e629bdee9e75df · report
update_ema_model deeplearningps/ecconf/EcConf/model/consistency/consistency_models.py official repository ran Apache-2.0 (permissive) · ece13d42e1169655 · report
Adjs_to_Onek deeplearningps/ecconf/EcConf/graphs/mol.py official repository unverified Apache-2.0 (permissive) · 90939d7c019d88a4 · report
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Tasks

Denoising

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Diffusion

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