人工智能驱动的抗菌的发现和设计:进展,挑战和机遇
Hailin Meng1,2
1College of Biology and Pharmacy, Yulin Normal University, Yulin, 537000, China. hailinmeng@foxmail.com.
Probiotics and antimicrobial proteins
|November 22, 2025
概括
人工智能 (AI) 加快了抗微生物 (AMP) 的发现,以对抗抗生素耐药性. 人工智能方法增强了查,设计和验证,尽管数据和可解释性仍然是药物开发的挑战.
科学领域:
- 生物化学 生物化学
- 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:
- 抗菌素耐药性 (AMR) 需要超越传统抗生素的新型治疗策略.
- 由于独特的机制和低耐药性发展,抗微生物 (AMP) 显示出有前途.
- 升级的抗菌药物耐药性率造成了对创新的抗菌解决方案的迫切需求.
研究的目的:
- 审查人工智能 (AI) 在抗微生物 (AMP) 发现和设计方面的进展.
- 分析AI在AMP研究中的当前应用,挑战和未来方向.
- 为加速基于AMP的药物开发提供指导.
主要方法:
- 在AMP研究中对AI应用的系统审查,包括机器学习 (ML) 和深度学习 (DL).
- 分析人工智能模型,如歧视性,回归性,生成性和多式联络优化.
- 检查挑战,包括数据质量,模型可解释性和实验验证.
主要成果:
- 人工智能显著提高了AMP选效率,并使创新的设计策略成为可能.
- 目前的人工智能应用涵盖了各种模型,改善了临床前验证过程.
- 确定的主要挑战包括数据限制,解释性问题和验证瓶.
结论:
- 人工智能为加速AMP的发现和开发提供了前所未有的机会.
- 解决技术障碍对于实现人工智能在打击抗菌素耐药性的全部潜力至关重要.
- 本综述为人工智能驱动的AMP药物开发研究人员提供了全面的指南.
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