Graph neural networks (GNNs) demonstrate great performance in compound property and activity prediction due to their capability to efficiently learn complex molecular graph structures. However, two main limitations persist including compound representation and model interpretability. While atom-level molecular graph representations are commonly used because of their ability to capture natural topology, they may not fully express important substructures or functional groups which significantly influence molecular properties. Consequently, recent research proposes alternative representations employing reduction techniques to integrate higher-level information and leverages both representations for model learning. However, there is still a lack of study about different molecular graph representations on model learning and interpretation. Interpretability is also crucial for drug discovery as it can offer chemical insights and inspiration for optimization. Numerous studies attempt to include model interpretation to explain the rationale behind predictions, but most of them focus solely on individual prediction with little analysis of the interpretation on different molecular graph representations. This research introduces multiple molecular graph representations that incorporate higher-level information and investigates their effects on model learning and interpretation from diverse perspectives. The results indicate that combining atom graph representation with reduced molecular graph representation can yield promising model performance. Furthermore, the interpretation results can provide significant features and potential substructures consistently aligning with background knowledge. These multiple molecular graph representations and interpretation analysis can bolster model comprehension and facilitate relevant applications in drug discovery.

翻译：图神经网络因其能够高效学习复杂分子图结构，在化合物性质与活性预测中展现出卓越性能。然而，当前仍存在两大主要局限：化合物表示与模型可解释性。尽管原子级分子图表示因能捕获天然拓扑结构而被广泛使用，但其可能无法充分表达对分子性质具有重要影响的子结构或官能团。为此，近期研究提出采用降维技术整合高阶信息的替代表示方法，并同时利用两种表示进行模型学习。然而，关于不同分子图表示对模型学习与解释的影响仍缺乏系统研究。可解释性对药物发现至关重要，因其能提供化学见解与优化启示。大量研究尝试通过模型解释阐述预测依据，但多数仅关注单次预测，缺乏对不同分子图表示解释的深入分析。本研究引入整合高阶信息的多重分子图表示，从多维度探究其对模型学习与解释的影响。结果表明，原子图表示与简化分子图表示的联合应用可获得优异的模型性能。此外，解释结果能够提取与背景知识一致的关键特征与潜在子结构。这些多重分子图表示及其解释分析可增强模型理解，并推动药物发现领域的相关应用。