  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.
