对抗微生物的人工智能方法:进展和挑战
Carlos A Brizuela1, Gary Liu2, Jonathan M Stokes2
1Department of Computer Science, CICESE Research Center, Ensenada, Mexico.
Microbial biotechnology
|January 4, 2025
概括
人工智能 (AI) 加快了抗微生物 (AMP) 的发现. 本综述强调了先进的AI,包括大型语言模型 (LLM) 和图形神经网络 (GNN),用于识别针对耐药病原体的新型AMP.
科学领域:
- 生物化学和生物信息学
- 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:
- 抗微生物 (AMP) 显示出对抗多药耐药性病原体的前景.
- 传统的查方法昂贵且耗时.
- 人工智能,特别是机器学习 (ML),对于加速AMP识别和设计至关重要.
研究的目的:
- 在AMP发现和设计中提供AI方法的全面概述.
- 专注于新兴的人工智能技术,如大语言模型 (LLM) 和图形神经网络 (GNN).
- 解决人工智能驱动的AMP研究中的局限性和未来机会.
主要方法:
- 对用于AMP发现的AI近期进展的审查.
- 分析经典的ML,深度学习 (DL),LLM和GNN.
- 探索结构导向的AMP设计方法.
主要成果:
- 人工智能已经彻底改变了抗感染的发现.
- 观察到从经典的ML转向DL模型.
- 法律法规,GNN和结构导向设计代表着重要的,未被充分利用的潜力.
结论:
- 人工智能方法对于克服AMP发现的挑战至关重要.
- 需要对LLM,GNN和结构导向设计进行进一步的研究.
- 解决目前的局限性将为未来的AMP开发铺平道路.
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