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

doi:10.1016/j.jpha.2025.101302

Volume 15 Issue 8

Sep. 2025

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Article Navigation > Journal of Pharmaceutical Analysis > 2025 > 15(8): 101302

Zihan Zhou, Yang Yu, Chengji Yang, Leyan Cao, Shaoying Zhang, Junnan Li, Yingnan Zhang, Huayun Han, Guoliang Shi, Qiansen Zhang, Juwen Shen, Huaiyu Yang. GPT2-ICC: A data-driven approach for accurate ion channel identification using pre-trained large language models[J]. Journal of Pharmaceutical Analysis, 2025, 15(8): 101302. doi: 10.1016/j.jpha.2025.101302

Citation:

Zihan Zhou, Yang Yu, Chengji Yang, Leyan Cao, Shaoying Zhang, Junnan Li, Yingnan Zhang, Huayun Han, Guoliang Shi, Qiansen Zhang, Juwen Shen, Huaiyu Yang. GPT2-ICC: A data-driven approach for accurate ion channel identification using pre-trained large language models[J]. Journal of Pharmaceutical Analysis, 2025, 15(8): 101302. doi: 10.1016/j.jpha.2025.101302

Citation:

Zihan Zhou, Yang Yu, Chengji Yang, Leyan Cao, Shaoying Zhang, Junnan Li, Yingnan Zhang, Huayun Han, Guoliang Shi, Qiansen Zhang, Juwen Shen, Huaiyu Yang. GPT2-ICC: A data-driven approach for accurate ion channel identification using pre-trained large language models[J]. Journal of Pharmaceutical Analysis, 2025, 15(8): 101302. doi: 10.1016/j.jpha.2025.101302

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

doi: 10.1016/j.jpha.2025.101302

a. Shanghai Key Laboratory of Regulatory Biology, Institute of Biomedical Sciences and School of Life Sciences, East China Normal University, Shanghai, 200241, China;
b. College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, 210000, China

Funds:

This work is funded by grants from the National Key Research and Development Program of China (Grant Nos.: 2022YFE0205600 and 2022YFC3400504), the National Natural Science Foundation of China (Grant Nos.: 82373792 and 82273857), the Fundamental Research Funds for the Central Universities, China, and the East China Normal University Medicine and Health Joint Fund, China (Grant No.: 2022JKXYD07001). We are also thankful for the support of the ECNU Multifunctional Platform for Innovation (001).

Received Date: Oct. 30, 2024
Accepted Date: Apr. 04, 2025
Rev Recd Date: Mar. 10, 2025
Publish Date: Apr. 09, 2025

Abstract

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.
- Ion channel,
- Artificial intelligence,
- Representation learning,
- GPT2,
- Protein language model

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References(48)

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