用量子能级谱预训练分子3D表示，提升属性预测与动力学建模能力
MolSpectra: Pre-training 3D Molecular Representation with Multi-modal Energy Spectra
- 通过多谱图掩码重建学习分子能级谱特征
- 在多个基准上超越现有方法，显著提升性质预测精度
- 适合需要高精度分子表征的药物设计与材料研发人员
建立分子三维结构与能量态之间的关系，是学习3D分子表示的有前景路径。然而，现有方法仅基于经典力学建模分子能量态，忽略了量子力学效应，如离散能级结构，这些结构能更准确估计分子能量，并可通过能量谱实验测量。本文提出MolSpectra，利用能量谱增强3D分子表示的预训练，将量子力学知识融入分子表征。具体地，提出SpecFormer，一种用于通过掩码片段重建编码分子光谱的多谱图编码器。进一步通过对比目标对齐3D编码器与谱编码器输出，提升3D编码器对分子的理解。在公开基准上的评估表明，所提出的预训练表示在预测分子性质和建模动力学方面优于现有方法。
原文摘要 · Abstract (English)
Establishing the relationship between 3D structures and the energy states of molecular systems has proven to be a promising approach for learning 3D molecular representations. However, existing methods are limited to modeling the molecular energy states from classical mechanics. This limitation results in a significant oversight of quantum mechanical effects, such as quantized (discrete) energy level structures, which offer a more accurate estimation of molecular energy and can be experimentally measured through energy spectra. In this paper, we propose to utilize the energy spectra to enhance the pre-training of 3D molecular representations (MolSpectra), thereby infusing the knowledge of quantum mechanics into the molecular representations. Specifically, we propose SpecFormer, a multi-spectrum encoder for encoding molecular spectra via masked patch reconstruction. By further aligning outputs from the 3D encoder and spectrum encoder using a contrastive objective, we enhance the 3D encoder's understanding of molecules. Evaluations on public benchmarks reveal that our pre-trained representations surpass existing methods in predicting molecular properties and modeling dynamics.
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