Interpreting graph neural networks with Myerson values for cheminformatics approaches

Digital Discovery 2026 Open access

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Authors: Samuel K. R. Homberg, Malte L. Modlich, Marlon Becker, Janosch Menke, Garrett M. Morris, Benjamin Risse, Oliver Koch
DOI: 10.1039/d5dd00577a
Cited by: 1
References: 32

Abstract

A new, scalable interpretability method for GNNs using Myerson values to provide faithful, node-wise attribution heatmaps. By incorporating graph structural constraints, molecular predictions are explained as individual atomic contributions.

Fields of Study & Subjects

Computational Drug Discovery Methods Machine Learning in Materials Science Advanced Graph Neural Networks

Publication details

Journal / source
Digital Discovery
Publisher
Royal Society of Chemistry (RSC)
Volume
5
Issue
8
Pages
3378–3389
Publication date
1 January 2026
Language
EN