Structure Prototype Learning for Federated Molecular Property Prediction under Scaffold Heterogeneity.

Authors

Tingkai Sun, Xiucai Ye, Tetsuya Sakurai

Published in

Journal of chemical information and modeling. Volume 66. Issue 21. Pages 13870-13886. Nov 09, 2026.

Abstract

Federated molecular property prediction enables collaboration across private chemical libraries, but parameter averaging does not explicitly account for scaffold heterogeneity. FedSPL groups molecules using fixed descriptors and exchanges embedding prototypes alongside model parameters, enabling label-independent prototype matching while retaining supervised property prediction. Across five classification benchmarks and three heterogeneity levels, matched three-seed evaluation using masked, held-out server-model ROC-AUC showed higher mean final AUC than FedAvg in seven of 15 settings. Consistently positive differences on SIDER and mixed outcomes elsewhere indicate data set-dependent benefits, motivating validation-guided use of structural information in federated prototype learning.

PMID:
43001919
Bibliographic data and abstract were imported from PubMed on 09 Nov 2026.

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