LiSENCE: A Hybrid Ligand and Sequence Encoder Network for Predicting CYP450 Inhibitors in Safe Multidrug Administration

Abena Achiaa Atwereboannah, Wei-Ping Wu (), Sophyani B. Yussif, Muhammed Amin Abdullah, Edwin K. Tenagyei, Chiagoziem C. Ukuoma, Yeong Hyeon Gu () and Mugahed A. Al-antari ()
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Abena Achiaa Atwereboannah: School of Computer Science and Engineering, University of Electronic Science and Technology, Chengdu 610056, China
Wei-Ping Wu: School of Computer Science and Engineering, University of Electronic Science and Technology, Chengdu 610056, China
Sophyani B. Yussif: School of Computer Science and Engineering, University of Electronic Science and Technology, Chengdu 610056, China
Muhammed Amin Abdullah: School of Software Engineering, University of Electronic Science and Technology, Chengdu 610056, China
Edwin K. Tenagyei: School of Engineering and Built Environment, Griffith University, Nathan 4111, Australia
Chiagoziem C. Ukuoma: College of Nuclear Technology and Automation Engineering, Chengdu University of Technology, Chengdu 610059, China
Yeong Hyeon Gu: Department of Artificial Intelligence and Data Science, Daeyang AI Center, College of AI Convergence, Sejong University, Seoul 05006, Republic of Korea
Mugahed A. Al-antari: Department of Artificial Intelligence and Data Science, Daeyang AI Center, College of AI Convergence, Sejong University, Seoul 05006, Republic of Korea

Mathematics, 2025, vol. 13, issue 9, 1-32

Abstract: Adverse drug–drug interactions (DDIs) often arise from cytochrome P450 (CYP450) enzyme inhibition, which is vital for metabolism. The accurate identification of CYP450 inhibitors is crucial, but current machine learning models struggle to assess the importance of key inputs like ligand SMILES and protein sequences, limiting their biological insights. The proposed study developed LiSENCE, an artificial intelligence (AI) framework to identify CYP450 inhibitors. It aimed to enhance prediction accuracy and provide biological insights, improving drug development and patient safety regarding drug–drug interactions: The innovative LiSENCE AI framework comprised four modules: the Ligand Encoder Network (LEN), Sequence Encoder Network (SEN), classification module, and explainability (XAI) module. The LEN and SEN, as deep learning pipelines, extract high-level features from drug ligand strings and CYP protein target sequences, respectively. These features are combined to improve prediction performance, with the XAI module providing biological interpretations. Data were outsourced from three databases: ligand/compound SMILES strings from the PubChem and ChEMBL databases and protein target sequences from the Protein Data Bank (PDB) for five CYP isoforms: 1A2, 2C9, 2C19, 2D6, and 3A4. The model attains an average accuracy of 89.2%, with the LEN and SEN contributing 70.1% and 63.3%, respectively. The evaluation performance records 97.0% AUC, 97.3% specificity, 92.2% sensitivity, 93.8% precision, 83.3% F1-score, and 87.8% MCC. LiSENCE outperforms baseline models in identifying inhibitors, offering valuable interpretability through heatmap analysis, which aids in advancing drug development research.

Keywords: inhibitor; cytochrome P450; joint-localized attention (JoLA); attentive graph isomorphism network (AGIN); self-attention; explainable artificial intelligence (XAI) (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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