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.

本研究提出了多种分子图表示法，探究了它们对模型学习和解释的影响，结果显示将原子图表示和减少的分子图表示相结合可以产生具有前途的模型性能。 此外，解释结果可以提供与背景知识一致的重要功能和潜在子结构。这些多个分子图表示和解释分析可以提高模型理解，并促进药物发现相关应用。