用最优传输加速分子构象预测，训练快且精度更高。
EquiFlow: Equivariant Conditional Flow Matching with Optimal Transport for 3D Molecular Conformation Prediction
- 基于条件流匹配与等变网络，无需模拟直接训练。
- 在QM9数据集上优于当前最先进模型，构象预测更准确。
- 采用ODE求解器，推理速度比扩散模型更快。
分子三维构象决定了其与其他分子或蛋白表面的相互作用。近年来深度学习虽提升了构象预测性能，但训练速度慢、难以利用高阶特征仍限制效果。我们提出EquiFlow，一种结合最优传输的等变条件流匹配模型。该方法首次将条件流匹配用于分子3D构象预测，通过无仿真训练解决训练慢的问题。模型采用改进的Equiformer编码原子与键属性，生成高阶嵌入；同时使用ODE求解器，相比含SDE的扩散模型实现更快推理。在QM9数据集上的实验表明，EquiFlow在小分子构象预测上优于现有最先进模型。
原文摘要 · Abstract (English)
Molecular 3D conformations play a key role in determining how molecules interact with other molecules or protein surfaces. Recent deep learning advancements have improved conformation prediction, but slow training speeds and difficulties in utilizing high-degree features limit performance. We propose EquiFlow, an equivariant conditional flow matching model with optimal transport. EquiFlow uniquely applies conditional flow matching in molecular 3D conformation prediction, leveraging simulation-free training to address slow training speeds. It uses a modified Equiformer model to encode Cartesian molecular conformations along with their atomic and bond properties into higher-degree embeddings. Additionally, EquiFlow employs an ODE solver, providing faster inference speeds compared to diffusion models with SDEs. Experiments on the QM9 dataset show that EquiFlow predicts small molecule conformations more accurately than current state-of-the-art models.
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