MSIGN: A deep learning framework based on multi-scale interaction graph neural networks for predicting binding of synthetic cannabinoids to receptors

Digital Discovery 2026 Open access

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Authors: Zhenyong Cheng, Dinghao Liu, Yuanpeng Fu, Kewei Sheng, Yan Xing, Yanling Qiao, Shangxuan Cai, Jubo Wang, Peng Xu, Bin Di, Jun Liao
DOI: 10.1039/d5dd00317b
Cited by: 0
References: 49

Abstract

MSIGN leverages multi-scale graph features for affinity prediction. Following domain-specific fine-tuning, the model is effectively applied to synthetic cannabinoid binding assessment, achieving high accuracy as confirmed by experimental assays.

Fields of Study & Subjects

Computational Drug Discovery Methods Cannabis and Cannabinoid Research Receptor Mechanisms and Signaling

Publication details

Journal / source
Digital Discovery
Volume
5
Issue
3
Pages
1351–1362
Publication date
1 January 2026
Language
EN