MolSpectra: Pre-training 3D Molecular Representation with Multi-modal Energy Spectra
Keywords: 3D molecular representation learning, molecular spectra, pre-training
TL;DR: We propose incorporating molecular spectra into the pre-training of 3D molecular representations, thereby infusing the knowledge of quantum mechanical principles into the representations.
Abstract: 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.
Primary Area: applications to physical sciences (physics, chemistry, biology, etc.)
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Submission Number: 6027
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