The functional role of biomolecular condensates is shaped by the composition of constituent proteins, nucleic acids, ions, and small molecules. Selective partitioning of small molecules into condensates has therefore emerged as a potential route to condensate-specific chemical probes and therapeutics. Although partitioning is influenced by differences in solvation environments between coexisting dense and dilute phases, a molecular framework connecting small-molecule structure to condensate-specific enrichment remains lacking. Here, we use existing experimental partitioning data for a library of FDA-approved drugs and metabolites across four biomolecular condensates to develop an interpretable graph-based model of small-molecule partitioning. By combining multitask pretraining, condensate-specific fine-tuning, evidential uncertainty quantification, and atom-level attribution analysis, our model predicts continuous partition coefficients with improved accuracy over descriptor-based approaches. Atom-level attributions reveal that condensate partitioning is not governed by a universal chemical rule: the same molecular scaffold can be read differently by distinct condensate environments, with local atomic context and connectivity determining whether specific atoms promote or suppress enrichment. We further apply the trained model to ~1.7 million drug-like molecules from ChEMBL, identifying a chemically diverse space of predicted condensate-selective partitioners and mapping regions where predictions are confident versus where new measurements would be most informative. Together, this work establishes condensate partitioning as a chemically learnable property shaped by the interplay between small-molecule structure and condensate-specific microenvironments, providing an interpretable and uncertainty-aware framework for defining molecular determinants of partitioning and guiding the discovery of condensate-selective small molecules.
Khambhawala, A., Rekhi, S., Chen, Q., Mohanty, P., Tabor, D. P., Mittal, J.
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