利用人工智能用于抗微生物创新,对抗多药性耐药性.
João P F Pimentel1, Raquel M Quigua Orozco1, Samilla Beatriz de Rezende1
1S-Inova Biotech, Programa de Pós-Graduação em Biotecnologia, Universidade Católica Dom Bosco, Campo Grande-MS 79117900, Brazil.
JACS Au
|February 27, 2026
概括
人工智能 (AI) 加快了抗菌素 (AMP) 的发现,以对抗抗菌素耐药性 (AMR). 人工智能模型预测,设计和优化新型AMP,增强药物开发管道.
科学领域:
- 生物技术是生物技术.
- 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) 是一个重大的全球卫生危机,需要新的治疗策略.
- 抗微生物 (AMP) 是传统抗生素的替代品,但很难在实验中发现.
- 人工智能 (AI) 提供了强大的工具来克服AMP识别和开发的局限性.
研究的目的:
- 审查针对抗微生物发现的人工智能驱动方法的最新进展.
- 突出机器学习 (ML),深度学习 (DL) 和生成模型在加速AMP研究中的作用.
- 讨论在设计中整合多态数据和新兴技术,如量子计算 (QC).
主要方法:
- 利用预测模型进行准确的AMP识别和选.
- 采用生成模型来设计和优化新的AMP候选者.
- 整合多组学数据以提高对的功能和机制的理解.
- 探索量子计算 (QC) 以解决设计中的计算挑战.
主要成果:
- 人工智能显著加速了潜在的AMP的大规模选和功能注释.
- 人工智能能够准确预测和设计新型抗菌.
- 整合多组学数据为AMP疗效提供了更深入的见解.
- 像QC这样的新兴技术显示了先进设计的潜力.
结论:
- 人工智能驱动的策略正在彻底改变抗微生物的发现.
- 这些先进的计算方法对于开发下一代基于的抗微生物药物至关重要.
- 人工智能整合有望扩大治疗领域,并解决对抗AMR的紧急临床需求.
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