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The rapid development of artificial intelligence has brought many innovative achievements to fields such as drug design and drug discovery [1]. Combining traditional graphics methods with deep learning methods can significantly improve the accuracy of drug screening. Hypoxia refers to the process in which tissues or cells in the body undergo abnormal changes in morphology, physiological functions, and metabolism due to insufficient oxygen supply or oxygen utilization obstacles [2]. In the previous study, Enlargement of the cell nucleus is a recognizable morphological feature of hypoxia cells [3]. Based on a deep learning multi-cell image classification model, this study constructed a high-throughput compound screening system for discriminating the anti-hypoxia activity of thousands of compounds. By simultaneously performing prediction scoring using AC16 and H9C2 models, the anti-hypoxia activity of thousands of compounds was predicted, and some compound molecules with anti-hypoxia effects were successfully screened.

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The Establishment of a Hypoxia Cellular Morphology Model Based on Deep Convolutional Neural Networks and Intelligent Screening of Anti-hypoxia Drugs

  • 会议论文

  • 发表日期：

    2025年

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  • Xinyi Zhang,
  • Zheng Wang

摘要

The rapid development of artificial intelligence has brought many innovative achievements to fields such as drug design and drug discovery [1]. Combining traditional graphics methods with deep learning methods can significantly improve the accuracy of drug screening. Hypoxia refers to the process in which tissues or cells in the body undergo abnormal changes in morphology, physiological functions, and metabolism due to insufficient oxygen supply or oxygen utilization obstacles [2]. In the previous study, Enlargement of the cell nucleus is a recognizable morphological feature of hypoxia cells [3]. Based on a deep learning multi-cell image classification model, this study constructed a high-throughput compound screening system for discriminating the anti-hypoxia activity of thousands of compounds. By simultaneously performing prediction scoring using AC16 and H9C2 models, the anti-hypoxia activity of thousands of compounds was predicted, and some compound molecules with anti-hypoxia effects were successfully screened.