Learning Molecular Representation in a Cell
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.
66733955335916dbbce7fa38
Gang Liu
claimed_verified
2024-06-21T07:06:57.469Z
63bf1afd4a2beec65565ee90
/avatars/134ac63b443e1360be7c91f84f9d5ec7.svg
Gang Liu
user
liuganghuggingface
66733955335916dbbce7fa39
Srijit Seal
66733955335916dbbce7fa3a
John Arevalo
admin_assigned
2024-06-24T14:11:51.859Z
645c0092f9d4ec91fdd9a4af
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John Arevalo
user
johnarevalo
66733955335916dbbce7fa3b
Zhenwen Liang
admin_assigned
2024-06-24T14:11:57.701Z
62ffa3f8311cad266f9af236
/avatars/203dac40bc546ee25a01d8715a4b3049.svg
Zhenwen Liang
user
invokerliang
66733955335916dbbce7fa3c
Anne E. Carpenter
66733955335916dbbce7fa3d
Meng Jiang
admin_assigned
2024-06-24T14:12:11.427Z
62cdc931c589e4a9e2411d39
https://cdn-avatars.huggingface.co/v1/production/uploads/62cdc931c589e4a9e2411d39/Q91Xp6WeI50AjViMzbD3Q.png
Meng Jiang
user
mjiang89
66733955335916dbbce7fa3e
Shantanu Singh
Learning Molecular Representation in a Cell
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.
InfoAlign uses an information bottleneck approach to learn molecular representations by optimizing encoder latent space for minimal redundancy and feature alignment in a context graph, improving model performance in molecular property prediction and zero-shot molecule-morphology matching.
information bottleneck InfoAlign context graph encoder's latent representation minimality objective sufficiency objective molecular representation learning feature alignment