在的方法对抗菌发现:PTML模拟虚拟多菌株抑制剂对黄金葡萄球菌
Valeria V Kleandrova1, M Natália D S Cordeiro1, Alejandro Speck-Planche1
1LAQV@REQUIMTE/Department of Chemistry and Biochemistry, Faculty of Sciences, University of Porto, 4169-007 Porto, Portugal.
Pharmaceuticals (Basel, Switzerland)
|February 26, 2025
概括
研究人员开发了一种机器学习模型,用于预测和设计新的抗菌化合物,以药物耐药的金黄色葡萄球菌菌株为目标. 这种方法加速了新型抗微生物药物的发现,以对抗不断增长的传染病威胁.
科学领域:
- 计算化学是一种计算化学.
- 机器学习 机器学习
- 药物发现 药物发现
背景情况:
- 抗多种药物的金黄色葡萄球菌感染对全球健康构成重大挑战.
- 在 silico 方法提供了一条快速识别和设计新型抗菌剂的途径.
- 针对具有不同耐药性的多个金黄色细菌菌株是有效治疗的关键.
研究的目的:
- 开发一种扰动理论机器学习模型 (PTML-MLP),用于预测和设计抗黄菌的抑制剂.
- 创建一个多功能虚拟抑制剂模型,能够针对多种S. aureus菌株.
- 为了加速抗菌药物发现的早期阶段.
主要方法:
- 利用来自ChEMBL数据库的化学和生物数据.
- 使用Box-Jenkins方法将拓索引转换为图形理论索引.
- 使用这些索引开发了一个多层感知神经网络 (PTML-MLP) 模型.
主要成果:
- 在培训和测试中,PTML-MLP模型的准确性超过了80%.
- 基于碎片的拓设计 (FBTD) 能够解释为活动做出贡献的分子碎片.
- 四种新型类似药物的分子被设计为潜在的多菌株黄金色杆菌抑制剂.
结论:
- 扰动理论机器学习建模显示,对早期抗菌药物发现有前途.
- 设计的分子代表了未来合成和测试的有希望的化学型.
- 这种方法可以帮助开发多功能抗S. aureus药物.
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