使用深度学习开发抗菌的研究进展
Yuchen Hu1,2, Junchao Zhou1, Yuhang Gao1
1National '111' Centre for Cellular Regulation and Molecular Pharmaceutics, Key Laboratory of Fermentation Engineering (Ministry of Education), Cooperative Innovation Centre of Industrial Fermentation (Ministry of Education & Hubei Province), School of Life and Health Sciences, Hubei University of Technology, Wuhan, People's Republic of China.
Journal of computational chemistry
|August 13, 2025
概括
人工智能 (AI),特别是深度学习,通过有效预测序列来加速抗菌 (AMP) 的发现. 这种人工智能驱动的方法克服了传统方法的局限性,降低了成本,并加强了对抗抗生素耐药性的研究.
科学领域:
- 生物技术和生物信息学
- 计算生物学 计算生物学
- 药物发现 药物发现 药物发现
背景情况:
- 抗生素耐药性是一个日益严重的全球健康危机,需要新的治疗策略.
- 抗微生物 (AMP) 由于具有广泛的活性而具有前景,但使用传统方法发现它们具有挑战性.
- 传统的AMP发现依赖于昂贵和耗时的实验室试验.
研究的目的:
- 审查深度学习用于预测抗微生物 (AMP) 序列的应用.
- 突出AI在加速AMP发现和开发方面的优势.
- 讨论AMP预测中的深度学习的当前进展,局限性和挑战.
主要方法:
- 使用深度学习算法来分析已知的AMP的大规模数据集.
- 通过深度学习模型进行自动特征提取和序列预测.
- 对人工智能驱动的AMP序列预测工作流的概述.
主要成果:
- 深度学习模型在选和预测AMP方面具有显著的优势.
- 人工智能提高了查效率,降低了研发成本.
- 这种方法为AMP研究和临床应用开辟了新的途径.
结论:
- 深度学习为AMP发现的传统方法提供了强大而高效的替代方案.
- 针对AMP预测的AI的持续研究和开发对于解决抗生素耐药性的解决至关重要.
- 解决目前的局限性将进一步释放AI在抗菌药物开发中的潜力.
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