HyperPhS: a pharmacophore-guided multimodal representation framework for metabolic stability prediction through contrastive hypergraph learning

Authors

Jijun Tang (ORCID: https://orcid.org/0000-0002-6377-536X)
Chengwei Ai (ORCID: https://orcid.org/0000-0003-1314-660X)
Charles A. Kang (ORCID: https://orcid.org/0000-0002-2948-4710)
Hongpeng Yang
Na Zhang (ORCID: https://orcid.org/0000-0002-9050-3327)
Fei Guo (ORCID: https://orcid.org/0000-0001-8346-0798)
Xiaoyi Liu (ORCID: https://orcid.org/0009-0006-2586-2389)

Institutions

Central South University (CN)
University of South Carolina (US)
Beijing University of Chinese Medicine (CN)
Southern University of Science and Technology (CN)
Institute for Advanced Study (DE)

Publication Details

Journal: Bioinformatics
Published: 2025-09-19
DOI: https://doi.org/10.1093/bioinformatics/btaf524
Primary Topic: Computational Drug Discovery Methods
Type: article
Field-Weighted Citation Impact: 0.00

Funders

National Natural Science Foundation of China
Central South University
Fundamental Research Funds for the Central Universities

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article

HyperPhS: a pharmacophore-guided multimodal representation framework for metabolic stability prediction through contrastive hypergraph learning

Jijun Tang, Chengwei Ai, Charles A. Kang, Hongpeng Yang et al.

Sep 19, 2025

Bioinformatics

Computational Drug Discovery Methods

article

HyperPhS: a pharmacophore-guided multimodal representation framework for metabolic stability prediction through contrastive hypergraph learning

Jijun Tang, Chengwei Ai, Charles A. Kang, Hongpeng Yang, Na Zhang, Fei Guo, Xiaoyi Liu

September 19, 2025article en

Abstract

No abstract available for this paper.

BioinformaticsVol. 41(10)

Central South University (CN), University of South Carolina (US), Beijing University of Chinese Medicine (CN), Southern University of Science and Technology (CN), Institute for Advanced Study (DE)

National Natural Science Foundation of China, Central South University, Fundamental Research Funds for the Central Universities

Openalex Percentile: Top 73%

Computational Drug Discovery Methods

DOI Open Access PDF OpenAlex

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