title: Learning Molecular Representation in a Cell
summary: Predicting drug efficacy and safety in vivo requires information on
  biological responses (e.g., cell morphology and gene expression) to small
  molecule perturbations. However, current molecular representation learning
  methods do not provide a comprehensive view of cell states under these
  perturbations and struggle to remove noise, hindering model generalization. We
  introduce the Information Alignment (InfoAlign) approach to learn molecular
  representations through the information bottleneck method in cells. We
  integrate molecules and cellular response data as nodes into a context graph,
  connecting them with weighted edges based on chemical, biological, and
  computational criteria. For each molecule in a training batch, InfoAlign
  optimizes the encoder's latent representation with a minimality objective to
  discard redundant structural information. A sufficiency objective decodes the
  representation to align with different feature spaces from the molecule's
  neighborhood in the context graph. We demonstrate that the proposed sufficiency
  objective for alignment is tighter than existing encoder-based contrastive
  methods. Empirically, we validate representations from InfoAlign in two
  downstream tasks: molecular property prediction against up to 19 baseline
  methods across four datasets, plus zero-shot molecule-morphology matching.
paper_authors._id: 66733955335916dbbce7fa38
paper_authors.name: Gang Liu
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-06-21T07:06:57.469Z
paper_authors._id: 63bf1afd4a2beec65565ee90
paper_authors.avatarUrl: /avatars/134ac63b443e1360be7c91f84f9d5ec7.svg
paper_authors.fullname: Gang Liu
paper_authors.type: user
paper_authors.user: liuganghuggingface
paper_authors._id: 66733955335916dbbce7fa39
paper_authors.name: Srijit Seal
paper_authors._id: 66733955335916dbbce7fa3a
paper_authors.name: John Arevalo
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-06-24T14:11:51.859Z
paper_authors._id: 645c0092f9d4ec91fdd9a4af
paper_authors.avatarUrl: /avatars/680274875d62dcc39d6ee34ecd8f519e.svg
paper_authors.fullname: John Arevalo
paper_authors.type: user
paper_authors.user: johnarevalo
paper_authors._id: 66733955335916dbbce7fa3b
paper_authors.name: Zhenwen Liang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-06-24T14:11:57.701Z
paper_authors._id: 62ffa3f8311cad266f9af236
paper_authors.avatarUrl: /avatars/4c88cb518e000a475f8381573f21aa7f.svg
paper_authors.fullname: Zhenwen Liang
paper_authors.type: user
paper_authors.user: invokerliang
paper_authors._id: 66733955335916dbbce7fa3c
paper_authors.name: Anne E. Carpenter
paper_authors._id: 66733955335916dbbce7fa3d
paper_authors.name: Meng Jiang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-06-24T14:12:11.427Z
paper_authors._id: 62cdc931c589e4a9e2411d39
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/62cdc931c589e4a9e2411d39/Q91Xp6WeI50AjViMzbD3Q.png
paper_authors.fullname: Meng Jiang
paper_authors.type: user
paper_authors.user: mjiang89
paper_authors._id: 66733955335916dbbce7fa3e
paper_authors.name: Shantanu Singh
paper_title: Learning Molecular Representation in a Cell
paper_summary: Predicting drug efficacy and safety in vivo requires information on
  biological responses (e.g., cell morphology and gene expression) to small
  molecule perturbations. However, current molecular representation learning
  methods do not provide a comprehensive view of cell states under these
  perturbations and struggle to remove noise, hindering model generalization. We
  introduce the Information Alignment (InfoAlign) approach to learn molecular
  representations through the information bottleneck method in cells. We
  integrate molecules and cellular response data as nodes into a context graph,
  connecting them with weighted edges based on chemical, biological, and
  computational criteria. For each molecule in a training batch, InfoAlign
  optimizes the encoder's latent representation with a minimality objective to
  discard redundant structural information. A sufficiency objective decodes the
  representation to align with different feature spaces from the molecule's
  neighborhood in the context graph. We demonstrate that the proposed sufficiency
  objective for alignment is tighter than existing encoder-based contrastive
  methods. Empirically, we validate representations from InfoAlign in two
  downstream tasks: molecular property prediction against up to 19 baseline
  methods across four datasets, plus zero-shot molecule-morphology matching.
paper_ai_keywords: molecular representation information bottleneck method context graph encoder's latent representation sufficiency objective contrastive methods molecular property prediction show-shot molecule-morphology