HyperPhS: a pharmacophore-guided multimodal representation framework for metabolic stability prediction through contrastive hypergraph learning Article Swipe
Xiaoyi Liu
,
Na Zhang
,
Charles A. Kang
,
Hongpeng Yang
,
Chengwei Ai
,
Jijun Tang
,
Fei Guo
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1093/bioinformatics/btaf524
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1093/bioinformatics/btaf524
Motivation Metabolic stability is crucial in the early stage of drug discovery and development. Drug candidate screening and optimization can be streamlined through the accurate prediction of stability. Functional groups within drug molecules are known as pharmacophores, which bind directly to receptors or biological macromolecules to produce biological effects, thereby affecting metabolic stability. Therefore, determining metabolic stability via the pharmacophore groups remains a significant challenge. Results To address these issues, we propose a Pharmacophore-guided Hypergraph representation framework for predicting metabolic Stability (HyperPhS). In this study, we introduce a hypergraph-based method to extract features from metabolic pharmacophores with multi-view representation and contrastive learning. In particular, we introduce a pharmacophore-based contrastive learning encoder that captures the consistency between functional and nonfunctional structures. Our method applies ChatGPT simultaneously to metabolites and heterogeneous encoders and integrates multimodal representations by using attention-driven fusion modules coupled with fully connected neural networks. On the HLM dataset, HyperPhS achieves outstanding performance with 87.6% in AUC and 62.6% in MCC, alongside an external test AUC of 88.3%. In addition, pharmacophore groups studied by HyperPhS are validated for their interpretability through case studies. Overall, HyperPhS is an effective and interpretable tool for determining metabolic stability, identifying critical functional groups, and optimizing compounds. Availability and implementation The code and data are available at https://github.com/xiaoyiliu-usc/HyperPhS.
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HyperPhS: a pharmacophore-guided multimodal representation framework for metabolic stability prediction through contrastive hypergraph learningWork title
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2025Year of publication
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2025-09-19Full publication date if available
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Xiaoyi Liu, Na Zhang, Charles A. Kang, Hongpeng Yang, Chengwei Ai, Jijun Tang, Fei GuoList of authors in order
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https://doi.org/10.1093/bioinformatics/btaf524Publisher landing page
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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0Total citation count in OpenAlex
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25Number of works referenced by this work
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| publication_date | 2025-09-19 |
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