Biomolecular condensates regulate cellular function by compartmentalizing molecules without a surrounding membrane. Condensate function arises from the specific exclusion or enrichment of molecules. Thus, understanding condensate composition is critical to characterizing condensate function. Whereas principles defining macromolecular composition have been described, understanding of small-molecule composition remains limited. Here we quantified the partitioning of ~1,700 biologically relevant small molecules into condensates composed of different macromolecules. Partitioning varied nearly a million-fold across compounds but was correlated among condensates, indicating that disparate condensates are physically similar. For one system, the enriched compounds did not generally bind macromolecules with high affinity under conditions where condensates do not form, suggesting that partitioning is not governed by site-specific interactions. Correspondingly, a machine learning model accurately predicts partitioning using only computed physicochemical features of the compounds, chiefly those related to solubility and hydrophobicity. These results suggest that a hydrophobic environment emerges upon condensate formation, driving the enrichment and exclusion of small molecules.

生物分子凝聚物通过在没有周围膜的情况下分隔分子来调节细胞功能。缩合功能源于分子的特异性排斥或富集。因此，了解凝析油成分对于表征凝析油功能至关重要。虽然已经描述了定义大分子组成的原理，但对小分子组成的理解仍然有限。在这里，我们量化了 ~1,700 个生物学相关的小分子被分配成由不同大分子组成的凝聚物。化合物之间的分配差异近 100 万倍，但凝聚物之间的分配是相关的，这表明不同的凝聚物在物理上是相似的。对于一个系统，在不形成缩合物的条件下，富集的化合物通常不会以高亲和力结合大分子，这表明分配不受位点特异性相互作用的控制。相应地，机器学习模型仅使用计算出的化合物的物理化学特征（主要是与溶解度和疏水性相关的特征）来准确预测分配。这些结果表明，冷凝物形成时会出现疏水环境，从而驱动小分子的富集和排除。