Explaining compound activity predictions with a substructure-aware loss for graph neural networks

Explaining compound activity predictions with a substructure-aware loss for graph neural networks

Abstract Explainable machine learning is increasingly used in drug discovery to help rationalize compound property predictions. Feature attribution techniques are popular choices to identify which molecular substructures are responsible for a predicted property change. However, established molecular...

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Bibliographic Details
Main Authors: Kenza Amara, Raquel Rodríguez-Pérez, José Jiménez-Luna
Format: Article
Language:English
Published: BMC 2023-07-01
Series:Journal of Cheminformatics
Subjects:
Explainable AI
Model interpretation
Graph neural networks
Benchmark
Activity predictions
QSAR
Online Access:https://doi.org/10.1186/s13321-023-00733-9

Internet

https://doi.org/10.1186/s13321-023-00733-9

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