Accurate molecular property prediction is central to drug discovery, yet graph neural networks often underperform in data-scarce regimes and fail to surpass traditional fingerprints. We introduce cross-graph inter-message passing (XIMP), which performs message passing both within and across multiple related graph representations. For small molecules, we combine the molecular graph with scaffold-aware junction trees and pharmacophore-encoding extended reduced graphs, integrating complementary abstractions. While prior work is either limited to a single abstraction or non-iterative communication across graphs, XIMP supports an arbitrary number of abstractions and both direct and indirect communication between them in each layer. Across ten diverse molecular property prediction tasks, XIMP outperforms state-of-the-art baselines in most cases, leveraging interpretable abstractions as an inductive bias that guides learning toward established chemical concepts, enhancing generalization in low-data settings.

翻译：准确的分子性质预测是药物发现的核心，然而图神经网络在数据稀缺场景下往往表现不佳，且难以超越传统指纹方法。本文提出跨图消息传递机制（XIMP），该机制能够在多个相关图表示内部及之间进行消息传递。对于小分子，我们将分子图与基于骨架感知的连接树以及药效团编码的扩展简化图相结合，从而整合互补的抽象表示。先前的研究要么局限于单一抽象表示，要么仅支持图之间的非迭代通信，而XIMP支持任意数量的抽象表示，并在每一层中实现图之间的直接与间接通信。在十项不同的分子性质预测任务中，XIMP在多数情况下优于现有最先进的基线方法，其通过可解释的抽象表示作为归纳偏置，引导学习过程朝向既定的化学概念，从而增强了低数据场景下的泛化能力。