Evolving Molecular Graph Neural Networks with Hierarchical Evaluation Strategy

Yingfang Yuan, Wenjun Wang, Xin Li, Chen Kefan, Yonghan Zhang, Wei Pang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Graph representation of molecular data enables extracting stereoscopic features, with graph neural networks (GNNs) excelling in molecular property prediction. However, selecting optimal hyper-parameters for GNN construction is challenging due to the vast search space and high computational costs. To tackle this, we introduce a hierarchical evaluation strategy integrated with a genetic algorithm (HESGA). HESGA combines full and fast evaluations of GNNs. Full evaluation involves training a GNN with preset epochs, using root mean square error (RMSE) to measure hyperparameter quality. Fast evaluation interrupts training early, using the difference in RMSE values as a score for GNN potential. HESGA integrates these evaluations, with fast evaluation guiding candidate selection for full evaluation, maintaining elite individuals. Applying HESGA to optimise deep GNNs for molecular property prediction, experimental results on three datasets demonstrate its superiority over traditional Bayesian optimisation, Tree-structured Parzen Estimator, and CMA-ES. HESGA efficiently navigates the complex GNN hyperparameter space, offering a promising approach for molecular property prediction.
Original languageEnglish
Title of host publicationProceedings of the Genetic and Evolutionary Computation Conference 2024
PublisherAssociation for Computing Machinery
Pages1417-1425
Number of pages9
ISBN (Electronic)9798400704949
DOIs
Publication statusAccepted/In press - 21 Mar 2024
EventGenetic and Evolutionary Computation Conference 2024 - Melbourne, Australia
Duration: 14 Jul 202418 Jul 2024

Conference

ConferenceGenetic and Evolutionary Computation Conference 2024
Abbreviated titleGECCO 2024
Country/TerritoryAustralia
CityMelbourne
Period14/07/2418/07/24

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