Learning Topology-Specific Experts for Molecular Property Prediction

نویسندگان

چکیده

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 distinct structural patterns limits its prediction performance. In this paper, motivated by observation, propose TopExpert leverage topology-specific models (referred as experts), each responsible group sharing similar topological semantics. That is, expert learns discriminative features while being trained corresponding group. To tackle key challenge grouping patterns, introduce clustering-based gating module assigns an input molecule into clusters and further optimizes two different types self-supervision: semantics induced GNNs scaffolds, respectively. Extensive experiments demonstrate has boosted performance property also achieved better generalization new unseen scaffolds than baselines. The code available at https://github.com/kimsu55/ToxExpert.

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ژورنال

عنوان ژورنال: Proceedings of the ... AAAI Conference on Artificial Intelligence

سال: 2023

ISSN: ['2159-5399', '2374-3468']

DOI: https://doi.org/10.1609/aaai.v37i7.26000