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一个多属性优化生成对抗网络的 de novo 抗菌设计

Jiaming Liu^1,2, Tao Cui³, Tao Wang^1,2

¹AI for Science Interdisciplinary Research Center, School of Computer Science, Northwestern Polytechnical University, NO. 1 Dongxiang Road, Xi'an, 710129, China.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)

|August 11, 2025

概括

一个新的AI模型,MPOGAN,有效地设计具有强效活性和低毒性的抗微生物 (AMP). 这加速了新型抗感染药物的开发,克服了传统合成和计算方法的局限性.

科学领域:

生物技术和制药科学生物技术和制药科学
计算化学和药物设计

背景情况:

抗微生物 (AMP) 对于新型抗感染药物开发至关重要,因为它具有广泛的活性和较低的抗药性.
传统的AMP的实验室合成是费力和耗时的.
现有的计算方法难以同时优化多个AMP属性.

研究的目的:

引入一种新的计算方法,MPOGAN,用于设计具有优化的多重性质的抗微生物 (AMP).
解决当前方法的局限性,同时提高抗微生物功效,降低细胞毒性,增加多样性.

主要方法:

开发一个多属性优化生成对抗网络 (MPOGAN) 模型.
使用动态更新的数据集,对质属性关系的代学习.
进行了广泛的计算测试,以评估MPOGAN的设计能力.

主要成果:

MPOGAN成功地产生了具有强烈抗菌活性的AMP,降低了细胞毒性,并增加了多样性.
合成了10种设计的AMP,其中9种表现出抗菌活性和低细胞毒性.
两种合成的呈现出强大的广泛抗菌活性,并与显著降低的细胞毒性相结合.

结论:

MPOGAN为设计多功能抗微生物提供了一种卓越的计算策略.
开发的AMP显示出在抗感染疗法下游应用的巨大潜力.
这种人工智能驱动的方法加速了下一代抗菌剂的发现和优化.

关键词:

抗微生物是一种抗微生物.这是一个de novo设计.生成性的对抗性网络.优化多个属性的优化.

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