title: MolSpectra: Pre-training 3D Molecular Representation with Multi-modal
    Energy Spectra
summary: 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.
paper_authors._id: 67bfdbd0302c06f220658e9d
paper_authors.name: Liang Wang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-02-27T09:14:42.802Z
paper_authors._id: 64e84ec6d41a68b065bf78a7
paper_authors.avatarUrl: /avatars/bae3c5e3210b40af6e4f113e85f3e206.svg
paper_authors.fullname: Liang Wang
paper_authors.type: user
paper_authors.user: AzureLeon1
paper_authors._id: 67bfdbd0302c06f220658e9e
paper_authors.name: Shaozhen Liu
paper_authors._id: 67bfdbd0302c06f220658e9f
paper_authors.name: Yu Rong
paper_authors._id: 67bfdbd0302c06f220658ea0
paper_authors.name: Deli Zhao
paper_authors._id: 67bfdbd0302c06f220658ea1
paper_authors.name: Qiang Liu
paper_authors._id: 67bfdbd0302c06f220658ea2
paper_authors.name: Shu Wu
paper_authors._id: 67bfdbd0302c06f220658ea3
paper_authors.name: Liang Wang
paper_title: MolSpectra: Pre-training 3D Molecular Representation with Multi-modal
    Energy Spectra
paper_summary: 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.
paper_ai_summary: SpecFormer enhances 3D molecular representations by incorporating energy spectra to capture quantum mechanical effects, improving predictions and modeling of molecular dynamics.
paper_ai_keywords: 3D molecular representations energy spectra quantum mechanics quantized energy levels SpecFormer multi-spectrum encoder masked patch reconstruction contrastive objective molecular properties molecular dynamics