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• W4319348458 abstract "Graph-driven techniques have been widely used in chemoinformatics and bioinformatics. It is of a great beneficial to develop toxicity prediction models. However, toxicity mechanisms are so complicated that they cannot be well explained. Many toxicity-related molecular features have been designed or explored, and the stacking ensemble strategies of machine learning models have been often used to boost toxicity predictive power. Herein, we review graph kernel learning (GKL) techniques for predictive toxicity models. These GKL techniques are fully graph data-driven, involving composed of graph kernels, graph neural networks, and graph embeddings. We briefly introduce the fundamental elements and developments of the GKL techniques in chemoinformatics. We systematically collect and evaluate the performance of the GKL methods on the public toxicity data sets. Consequently, we discuss applications, challenges, and perspectives about the GKL techniques for toxicity-related problems. We hope this chapter could help better understand and guide applications of GKL in solving computational toxicity problems." @default.
• W4319348458 created "2023-02-08" @default.
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• W4319348458 date "2023-01-01" @default.
• W4319348458 modified "2023-09-26" @default.
• W4319348458 title "Graph Kernel Learning for Predictive Toxicity Models" @default.
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• W4319348458 doi "https://doi.org/10.1007/978-3-031-20730-3_6" @default.
• W4319348458 hasPublicationYear "2023" @default.
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