MolSpectra: Pre-training 3D Molecular Representation with Multi-modal
  Energy Spectra
Authors: name: Liang Wang, name: Shaozhen Liu, name: Yu Rong, name: Deli Zhao, name: Qiang Liu, name: Shu Wu, name: Liang Wang

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.