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_keywords: energy states 3D structures 3D molecular representations energy spectra classical mechanics quantum mechanical effects quantized energy level structures energy spectra pretraining MolSpectra SpecFormer masked patch reconstruction contrastive objective 3D encoder spectrum encoder molecular properties modeling dynamics