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机器学习为下一代抗生素的三碳复合物的指导设计

Miroslava Nedyalkova^1,2, Gozde Demirci¹, Youri Cortat¹

¹Department of Chemistry, Fribourg University, Chemin Du Musée 9, Fribourg 1700, Switzerland.

ACS bio & med chem Au

|October 20, 2025

概括

这项研究引入了一种新的计算方法,使用机器学习来设计新的基于的抗生素. 这种方法有效地预测了对抗药性细菌的抗菌功效,有助于开发下一代抗菌剂.

科学领域:

药用化学医学化学
计算化学的计算化学
传染性疾病传染性疾病

背景情况:

抗生素耐药性的增加需要新的抗菌剂.
基于金属的化合物,特别是复合物,显示出作为抗生素的希望.
目前的机器学习 (ML) 方法通常依赖于结构相似性,限制了创新.

研究的目的:

为合理设计基于的新型抗生素制定计算策略.
利用机器学习模型来预测复合物的抗菌作用.
对抗抗生素耐药细菌探索结构多样化的复合体.

主要方法:

开发了使用结构描述符的预测ML模型 (MLP和RF).
针对MRSA和MSSA的估计最低抑制度 (MIC).
采用SHAP分析来解释模型预测的可解释性.

主要成果:

ML模型表现出强大的抗菌活性预测性能.
成功评估了26种新的复合体.
确定了影响抗菌功能的关键结构特征.

结论:

这种以ML为指导的方法有助于*de novo*设计强效的以为基础的抗生素.
这一策略是有效的对抗抗生素耐药性细菌感染.
为发现下一代抗菌剂提供了一个有前途的途径.

关键词:

抗微生物药物是一种抗菌药物.抗微生物耐药性抗微生物耐药性机器学习是机器学习.金属复杂复杂的金属.复合物中的复合物.

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