EC-Conf: A ultra-fast diffusion model for molecular conformation generation with equivariant consistency

IF 7.1 2区 化学 Q1 CHEMISTRY, MULTIDISCIPLINARY Journal of Cheminformatics Pub Date : 2024-09-03 DOI:10.1186/s13321-024-00893-2
Zhiguang Fan, Yuedong Yang, Mingyuan Xu, Hongming Chen
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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. Here, 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.

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EC-Conf:等变一致性分子构象生成超快扩散模型
尽管最近在扩散模型驱动的三维分子构象生成方面取得了进展,但其在迭代扩散/变色过程中的高计算成本限制了其应用。在此,我们提出了等变一致性模型(EC-Conf),作为低能构象生成的快速扩散方法。在 EC-Conf 中,直接使用改进的 SE (3)- 等变变换器模型来编码笛卡尔分子构象,并通过高效的一致性扩散过程来生成分子构象。结果表明,只需一个采样步骤,它就能达到与其他数千个去噪步骤的基于扩散的模型相当的质量。如果多进行几次采样迭代,其性能还能进一步提高。我们在 GEOM-QM9 和 GEOM-Drugs 集上对 EC-Conf 的性能进行了评估。我们的结果表明,EC-Conf 学习低能分子构象分布的效率比当前的 SOTA 扩散模型至少高出两个量级,有可能成为构象生成和采样的有用工具。科学贡献:在这项工作中,我们提出了一种等变一致性模型,它能显著提高基于扩散模型的构象生成效率,同时保持较高的结构质量。该方法可作为一个通用框架,并可在未来的步骤中进一步扩展到更复杂的结构生成和预测任务,包括涉及蛋白质的任务。
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来源期刊
Journal of Cheminformatics
Journal of Cheminformatics CHEMISTRY, MULTIDISCIPLINARY-COMPUTER SCIENCE, INFORMATION SYSTEMS
CiteScore
14.10
自引率
7.00%
发文量
82
审稿时长
3 months
期刊介绍: Journal of Cheminformatics is an open access journal publishing original peer-reviewed research in all aspects of cheminformatics and molecular modelling. Coverage includes, but is not limited to: chemical information systems, software and databases, and molecular modelling, chemical structure representations and their use in structure, substructure, and similarity searching of chemical substance and chemical reaction databases, computer and molecular graphics, computer-aided molecular design, expert systems, QSAR, and data mining techniques.
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