GAMT-GINE: A Graph Isomorphism Network Integrating Continuous Spatial Awareness and Multi-Task Learning for Protein-Ligand Binding Affinity Prediction.

Jiarui Li, Hongquan Li, Di Wu, Wei He, Weinan Cao, Hua Yang, Zhen Hou

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Article in International journal of molecular sciences, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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7 authors.

Jiarui LiSchool of Information Science and Engineering, Shanxi Agricultural University, Jinzhong 030801, China.

Hongquan LiShanxi Key Lab for Modernization of TCVM, College of Veterinary Medicine, Shanxi Agricultural University, Jinzhong 030801, China.

Di WuSchool of Information Science and Engineering, Shanxi Agricultural University, Jinzhong 030801, China.

Wei HeSchool of Information Science and Engineering, Shanxi Agricultural University, Jinzhong 030801, China.

Weinan CaoSchool of Information Science and Engineering, Shanxi Agricultural University, Jinzhong 030801, China.ORCID 0009-0007-9595-5588

Hua YangSchool of Information Science and Engineering, Shanxi Agricultural University, Jinzhong 030801, China.

Zhen HouSchool of Information Science and Engineering, Shanxi Agricultural University, Jinzhong 030801, China.ORCID 0000-0002-5476-1441

Funding

Department of Human Resources and Social Security of Shanxi Province SXBYKY2024022Shanxi Agricultural University 2024BQ29Shanxi Key Laboratory for Modernization of Traditional Chinese Veterinary Medicine SXKL2025013

6 · The paper itself

Abstract

Protein-ligand interactions (PLIs) play a crucial role in drug discovery, and accurately predicting protein-ligand binding affinity (PLA) remains a central challenge in computer-aided drug design. Although graph neural networks (GNNs) have demonstrated considerable potential in molecular modeling, existing methods still face several limitations, including excessive reliance on hand-crafted chemical features, loss of spatial information, and difficulties in integrating heterogeneous affinity labels, which restrict their generalization capability in PLA prediction. To address these challenges, we propose GAMT-GINE, a graph isomorphism network that integrates continuous spatial awareness with multi-task learning. The model employs minimalist atomic features and a batch-normalization-free mechanism, together with a multi-task branch that uses a large amount of half-maximal inhibitory concentration (IC

Indexed as

ProteinsDrug DiscoveryGraph Neural NetworksLigandsModels, MolecularProtein BindingLigandsProteinscontinuous spatial perceptiondeep learninggraph isomorphism networkmulti-task learningprotein–ligand affinity

Identifiers

PMID42653201

DOI10.3390/ijms27167196

PMCPMC13513052

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.