Fan Zhiguang, Yang Yuedong, Xu Mingyuan, Chen Hongming
School of Computer Science and Engineering, Sun Yat-Sen University, Guangzhou, 510006, China.
Guangzhou National Laboratory, Guangzhou, 510005, China.
J Cheminform. 2024 Sep 3;16(1):107. doi: 10.1186/s13321-024-00893-2.
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. SCIENTIFIC CONTRIBUTIONS: In this work, we proposed an equivariant consistency model that significantly improves the efficiency of conformation generation in diffusion-based models while maintaining high structural quality. This method serves as a general framework and can be further extended to more complex structure generation and prediction tasks, including those involving proteins, in future steps.
尽管最近由扩散模型驱动的3D分子构象生成取得了进展，但其在迭代扩散/去噪过程中的高计算成本限制了其应用。在此，提出了一种等变一致性模型（EC-Conf）作为一种用于低能量构象生成的快速扩散方法。在EC-Conf中，直接使用改进的SE(3)等变变压器模型对笛卡尔分子构象进行编码，并进行高效的一致性扩散过程以生成分子构象。结果表明，仅通过一步采样，它就已经可以达到与运行数千步去噪的其他基于扩散的模型相当的质量。通过更多的采样迭代，其性能可以进一步提高。在GEOM-QM9和GEOM-Drugs数据集上评估了EC-Conf的性能。我们的结果表明，EC-Conf学习低能量分子构象分布的效率比当前的最优扩散模型至少高两个数量级，并且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，该模型在保持高结构质量的同时，显著提高了基于扩散的模型中构象生成的效率。该方法作为一个通用框架，在未来的步骤中可以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。
