使用可解释的人工智能设计高强度抗生物膜,抗微生物
Karina Pikalyova1, Tagir Akhmetshin1, Alexey Orlov1
1Laboratory of Chemoinformatics, University of Strasbourg, Strasbourg 67081, France.
Journal of chemical information and modeling
|December 23, 2025
概括
研究人员开发了一个人工智能管道,用于设计新型抗微生物 (AMP) 和抗菌膜剂. 这种方法在对抗生物膜时取得了100%的成功率,并为开发新型疗法提供了一种具有成本效益的方法.
科学领域:
- 计算化学和药物发现
- 医学中的人工智能
- 类治疗药物 类治疗药物
背景情况:
- 抗微生物 (AMP) 显示出对抗耐药细菌和生物膜的承诺.
- 目前的AMP设计方法需要优化,以提高效率和成本效益.
- 理性 de novo 设计对于开发具有所需性质的来说至关重要.
研究的目的:
- 开发用于AMP和抗菌膜的理性新设计的计算管道.
- 利用一个可解释的人工智能 (XAI) 框架,将瓦斯斯坦自动编码器 (WAE) 和生成地形绘图 (GTM) 结合起来.
- 通过计算选和识别有力的抗微生物和抗菌膜.
主要方法:
- 使用WAE来学习体空间的潜在表示.
- 通过2D潜伏空间地图,使用GTM指导创建新型AMP.
- 综合机器学习模型用于选基于预测活动的生成.
主要成果:
- 管道成功生成了具有经过验证的抗微生物和抗菌膜活性的.
- 对抗抗甲素耐药黄金葡萄球菌 (MRSA) 的实验验证显示,针对生物膜的成功率为100%.
- 与参考"1018"相比,最强大的抗菌膜显示了IC50的显著改善.
结论:
- 开发的基于XAI的计算管道使AMP和抗菌膜的有效的理性新设计成为可能.
- 该管道在识别具有改善活性的强效抗菌膜剂方面表现出高效.
- 这种方法可以适应优化额外的性质,促进开发基于的治疗方法.
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