Recently, graph neural networks (GNNs) have been successfully applied to predicting molecular properties, which is one of the most classical cheminformatics tasks with various applications. Despite their effectiveness, we empirically observe that training a single GNN model for diverse molecules with distinct structural patterns limits its prediction performance. In this paper, motivated by this observation, we propose \proposed to leverage topology-specific prediction models (referred to as experts), each of which is responsible for each molecular group sharing similar topological semantics. That is, each expert learns topology-specific discriminative features while being trained with its corresponding topological group. To tackle the key challenge of grouping molecules by their topological patterns, we introduce a clustering-based gating module that assigns an input molecule into one of the clusters and further optimizes the gating module with two different types of self-supervision: topological semantics induced by GNNs and molecular scaffolds, respectively. Extensive experiments demonstrate that \proposed has boosted the performance for molecular property prediction and also achieved better generalization for new molecules with unseen scaffolds than baselines. The code is available at https://github.com/kimsu55/ToxExpert.
翻译:最近,图形神经网络(GNNs)被成功地应用于预测分子特性,这是具有各种应用的最古典化学化学任务之一。尽管具有效力,但我们从经验上观察到,为不同结构模式的不同分子培训一个单一的GNN模型限制了其预测性能。在本文中,我们提议利用表层特定预测模型(称为专家),每个模型都负责每个分子组具有相似的表层语义。也就是说,每个专家在接受相应的表层学组的培训时,学习了因地貌学而特有的歧视特征。为了通过它们的表层学模式应对分子组合的主要挑战,我们引入了一个基于集群的模型,将一个输入分子划入其中的一个集群,并进一步优化了由两种不同的自我监督型构件模块:由GNNS和分子骨架分别引导的表理学语义。广泛的实验表明,\图案提高了分子属性预测的性能,并且实现了以其表层学形态学形态学形态学模式对新分子进行更好的概括化。MARFAMSMMM/MFFMs的基线比可得到的软体/commas。</s>