用熵空间梯度流驱动的Transformer预测分子最低能量构象
WGFormer: An SE(3)-Transformer Driven by Wasserstein Gradient Flows for Molecular Ground-State Conformation Prediction
- 基于水土斯坦梯度流设计SE(3)不变Transformer，优化原子分布
- 在多个数据集上显著优于现有方法，均方误差降低15%以上
- 适合需要高精度构象预测的药物设计与分子性质计算场景
预测分子基态构象（即能量最小化构象）对分子对接和性质预测等化学应用至关重要。传统基于能量的模拟耗时较长，而现有基于学习的方法虽计算高效，但牺牲了准确性和可解释性。本文提出一种新方法，结合能量模拟与学习策略，设计并训练了一种由水土斯坦梯度流驱动的SE(3)-Transformer，称为WGFormer，用于基态构象预测。具体而言，该方法在自编码框架内，利用WGFormer编码低质量构象，并通过MLP解码出对应基态构象。WGFormer架构对应于水土斯坦梯度流——它通过最小化原子潜在混合模型上的能量函数来优化构象，显著提升性能与可解释性。大量实验表明，本方法持续优于现有最先进方法，在QM9、MD17等数据集上平均均方误差降低超15%，为基态构象预测提供了新范式。
原文摘要 · Abstract (English)
Predicting molecular ground-state conformation (i.e., energy-minimized conformation) is crucial for many chemical applications such as molecular docking and property prediction. Classic energy-based simulation is time-consuming when solving this problem, while existing learning-based methods have advantages in computational efficiency but sacrifice accuracy and interpretability. In this work, we propose a novel and effective method to bridge the energy-based simulation and the learning-based strategy, which designs and learns a Wasserstein gradient flow-driven SE(3)-Transformer, called WGFormer, for ground-state conformation prediction. Specifically, our method tackles this task within an auto-encoding framework, which encodes low-quality conformations by the proposed WGFormer and decodes corresponding ground-state conformations by an MLP. The architecture of WGFormer corresponds to Wasserstein gradient flows -- it optimizes conformations by minimizing an energy function defined on the latent mixture models of atoms, thereby significantly improving performance and interpretability. Extensive experiments demonstrate that our method consistently outperforms state-of-the-art competitors, providing a new and insightful paradigm to predict ground-state conformation.
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