GPT2-ICC: A data-driven approach for accurate ion channel identification using pre-trained large language models.

Zihan ZHOU; Yang YU; Chengji YANG; Leyan CAO; Shaoying ZHANG; Junnan LI; Yingnan ZHANG; Huayun HAN; Guoliang SHI; Qiansen ZHANG; Juwen SHEN; Huaiyu YANG

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GPT2-ICC: A data-driven approach for accurate ion channel identification using pre-trained large language models.

Author: Zihan ZHOU ¹ ; Yang YU ² ; Chengji YANG ¹ ; Leyan CAO ¹ ; Shaoying ZHANG ¹ ; Junnan LI ¹ ; Yingnan ZHANG ¹ ; Huayun HAN ¹ ; Guoliang SHI ² ; Qiansen ZHANG ¹ ; Juwen SHEN ¹ ; Huaiyu YANG ¹
Author Information

1. Shanghai Key Laboratory of Regulatory Biology, Institute of Biomedical Sciences and School of Life Sciences, East China Normal University, Shanghai, 200241, China.
2. College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, 210000, China.
Publication Type:Journal Article
Keywords: Artificial intelligence; GPT2; Ion channel; Protein language model; Representation learning
From: Journal of Pharmaceutical Analysis 2025;15(8):101302-101302
CountryChina
Language:English
Abstract: Current experimental and computational methods have limitations in accurately and efficiently classifying ion channels within vast protein spaces. Here we have developed a deep learning algorithm, GPT2 Ion Channel Classifier (GPT2-ICC), which effectively distinguishing ion channels from a test set containing approximately 239 times more non-ion-channel proteins. GPT2-ICC integrates representation learning with a large language model (LLM)-based classifier, enabling highly accurate identification of potential ion channels. Several potential ion channels were predicated from the unannotated human proteome, further demonstrating GPT2-ICC's generalization ability. This study marks a significant advancement in artificial-intelligence-driven ion channel research, highlighting the adaptability and effectiveness of combining representation learning with LLMs to address the challenges of imbalanced protein sequence data. Moreover, it provides a valuable computational tool for uncovering previously uncharacterized ion channels.