使用隐性扩散模型的人工智能能够产生多样化和强大的抗微生物
Yeji Wang1, Minghui Song1, Fujing Liu1
1Department of Natural Product Chemistry, Key Laboratory of Chemical Biology (Ministry of Education), School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, Shandong Province, China.
Science advances
|February 5, 2025
概括
这项研究引入了一种新型的人工智能管道,用于设计各种抗菌 (AMP),包括抗真菌剂. 新方法成功生成了具有显著抗菌和抗真菌活性的,解决了当前AI模型的局限性.
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:
- 人工智能 (AI) 显示出对抗微生物 (AMP) 设计的希望.
- 目前的人工智能模型难以产生新型和多样化的AMP,并且很少准抗真菌.
研究的目的:
- 使用扩散模型和分子动力学开发人工智能管道,用于AMP的新设计.
- 与现有方法相比,生成具有更大的新性和多样性的AMP.
- 专门针对抗真菌的生成.
主要方法:
- 利用扩散模型与分子动力学相结合用于体设计.
- 合成了40个设计的实验验证.
- 测试了合成的体外和体内抗菌和抗真菌活动.
主要成果:
- 管道产生的体表现出较低的相似性和相同性比其他方法产生的体.
- 在40种合成的中,有25种表现出抗菌或抗真菌活性.
- AMP-29表现出针对Candida glabrata的选择性抗真菌活性和体内疗效.
- AMP-24在体外对抗格兰氏阴性细菌表现出强烈的活性,在体内对抗Acinetobacter baumannii表现出强烈的疗效.
结论:
- 开发的管道为设计不同的AMP提供了一种新的方法.
- 这种方法可以有效地产生具有显著抗菌和抗真菌特性的.
- 这些发现有助于通过提供新的治疗性候选物来打击抗生素耐药性.
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