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通过基于深度学习的模型和生物活动验证,从随机酸中发现AMP

Jun Du¹, Changyan Yang¹, Yabo Deng²

¹School of Basic Medical Sciences, Lanzhou University, Donggang West Road, Lanzhou, 730000, China; Gansu Provincial Maternity and Child Care Hospital, North Road 143, Qilihe District, Lanzhou, 730000, China.

European journal of medicinal chemistry

|August 28, 2024

概括

深度学习模型从随机序列中识别了新型抗微生物 (AMP),提供了更有效的发现方法. 有前途的候选药物表现出强大的抗菌活性,低毒性,并有可能治疗耐药性感染.

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:

抗微生物 (AMP) 对于对抗耐药病原体至关重要.
发现新的AMP是具有挑战性和昂贵的.
人工智能 (AI) 具有加速AMP发现的潜力.

研究的目的:

开发和应用基于深度学习的多区分器模型,用于识别新型抗微生物 (AMP).
选大量随机的图书馆,以寻找潜在的AMP.
验证已识别的AMP候选药物的疗效和安全性.

主要方法:

构建三种深度学习多差别模型.
对3万个随机的查.
对候选的抗微生物和溶血活性测定.
关于杀菌作用的机制研究.
在小鼠伤口感染模型中进行体内疗效测试.

主要成果:

使用深度学习模型,成功选了12种新型抗微生物 (AMP).
三种候选 (P2,P11,P12) 显示出强大的抗菌活性和低血解活性.
类药物通过破坏膜产生杀菌作用,降低了耐药性潜力.
12 (P12) 在小鼠模型中显示出显著的疗效,在小鼠模型中显示出黄金葡萄球菌伤口感染,器官毒性最小.

结论:

基于深度学习的多分辨器模型对于从随机库中识别新型抗菌 (AMP) 有效.
已识别的AMP在治疗细菌感染方面表现出有前途的临床潜力,包括由耐药菌株引起的细菌感染.
开发的AI方法提高了抗菌素发现的效率,降低了抗菌素发现的成本.

关键词:

抗微生物是一种抗微生物.人工智能 (AI) 是一种人工智能.深度学习是一种深度学习.耐药细菌是耐药性的细菌.伤口感染建模伤口感染建模

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