The exploration of chemical space holds promise for developing influential chemical entities. Molecular representations, which reflect features of molecular structure in silico, assist in navigating chemical space appropriately. Unlike atom-level molecular representations, such as SMILES and atom graph, which can sometimes lead to confusing interpretations about chemical substructures, substructure-level molecular representations encode important substructures into molecular features; they not only provide more information for predicting molecular properties and drug‒drug interactions but also help to interpret the correlations between molecular properties and substructures. However, it remains challenging to represent the entire molecular structure both intactly and simply with substructure-level molecular representations. In this study, we developed a novel substructure-level molecular representation and named it a group graph. The group graph offers three advantages: (a) the substructure of the group graph reflects the diversity and consistency of different molecular datasets; (b) the group graph retains molecular structural features with minimal information loss because the graph isomorphism network (GIN) of the group graph performs well in molecular properties and drug‒drug interactions prediction, showing higher accuracy and efficiency than the model of other molecular graphs, even without any pretraining; and (c) the molecular property may change when the substructure is substituted with another of differing importance in group graph, facilitating the detection of activity cliffs. In addition, we successfully predicted structural modifications to improve blood‒brain barrier permeability (BBBP) via the GIN of group graph. Therefore, the group graph takes advantages for simultaneously representing molecular local characteristics and global features. Scientific contribution The group graph, as a substructure-level molecular representation, has the ability to retain molecular structural features with minimal information loss. As a result, it shows superior performance in predicting molecular properties and drug‒drug interactions with enhanced efficiency and interpretability.

中文翻译：

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组图：具有增强的性能、效率和可解释性的分子图表示


对化学空间的探索为发展有影响力的化学实体带来了希望。分子表示反映了计算机中分子结构的特征，有助于适当地导航化学空间。与原子水平的分子表示（如 SMILES 和原子图）不同，原子水平的分子表示有时会导致对化学子结构的混淆解释，而子结构水平的分子表示将重要的子结构编码为分子特征;它们不仅为预测分子性质和药物相互作用提供了更多信息，还有助于解释分子性质和子结构之间的相关性。然而，用子结构水平的分子表示既完整又简单地表示整个分子结构仍然具有挑战性。在这项研究中，我们开发了一种新的亚结构水平的分子表示，并将其命名为群图。该组图具有三个优点：（a） 组图的子结构反映了不同分子数据集的多样性和一致性;（b） 由于群图的图同构网络 （GIN） 在分子性质和药物相互作用预测方面表现良好，即使没有任何预训练，也显示出比其他分子图模型更高的准确性和效率，因此群图保留了分子结构特征，信息损失最小;（c） 当子结构被组图中另一个不同重要性的子结构替换时，分子特性可能会发生变化，从而有助于检测活动悬崖。此外，我们通过组图 GIN 成功预测了改善血液脑屏障通透性 （BBBP） 的结构修饰。 因此，群图具有同时表示分子局部特征和全局特征的优势。科学贡献 群图作为子结构级别的分子表示，能够以最小的信息损失保留分子结构特征。因此，它在预测分子性质和药物-药物相互作用方面表现出卓越的性能，具有更高的效率和可解释性。