MolSpectra: Pre-training 3D Molecular Representation with Multi-modal
  Energy Spectra
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.
67bfdbd0302c06f220658e9d
Liang Wang
claimed_verified
2025-02-27T09:14:42.802Z
64e84ec6d41a68b065bf78a7
/avatars/bae3c5e3210b40af6e4f113e85f3e206.svg
Liang Wang
user
AzureLeon1
67bfdbd0302c06f220658e9e
Shaozhen Liu
67bfdbd0302c06f220658e9f
Yu Rong
claimed_verified
2025-06-10T09:31:05.491Z
642eecbf9b2484d7d8526781
https://cdn-avatars.huggingface.co/v1/production/uploads/642eecbf9b2484d7d8526781/4IvGbd66s49Wx5pZyZGHA.png
Yu Rong
user
Swrooy
67bfdbd0302c06f220658ea0
Deli Zhao
67bfdbd0302c06f220658ea1
Qiang Liu
67bfdbd0302c06f220658ea2
Shu Wu
67bfdbd0302c06f220658ea3
Liang Wang
MolSpectra: Pre-training 3D Molecular Representation with Multi-modal
  Energy Spectra
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.
SpecFormer enhances 3D molecular representations by incorporating energy spectra to capture quantum mechanical effects, improving predictions and modeling of molecular dynamics.
3D molecular representations energy spectra quantum mechanics quantized energy levels SpecFormer multi-spectrum encoder masked patch reconstruction contrastive objective molecular properties molecular dynamics