在机器学习的指导下,发现了AcrB和MexB效流抑制剂
Abhishek Bera1, Rakesh Kumar Roy1, Pritish Joshi1
1Department of Chemistry & Chemical Biology, Indian Institute of Technology (ISM) Dhanbad, Dhanbad 826004, India.
The journal of physical chemistry. B
|January 10, 2024
概括
研究人员确定了用于格兰氏阴性感染的新型药物排泄抑制剂. 机器学习模型选了一个库,产生了8个命中分子,Mol.3488显示了对AcrB和MexB的高结合亲和力.
科学领域:
- 药用化学 医学化学
- 计算化学的计算化学
- 微生物学 微生物学
背景情况:
- 多种药物排泄,如AcrB和MexB,在格兰氏阴性病原体中赋予抗菌耐药性.
- 目前针对这些的抑制剂缺乏临床批准,需要新的治疗策略.
研究的目的:
- 为了识别新的,强大的类似抑制剂的分子,以向格拉姆阴性多药物排泄.
- 利用监督分类模型对潜在抑制剂进行虚拟选.
主要方法:
- 使用已知抑制剂的分子描述器开发监督分类模型 (SVM,随机森林,AdaBoost,LightGBM).
- 选了5043个化合物的库,过以识别8个命中分子.
- 进行了分子动力学模拟和结合自由能量计算,以评估对AcrB和MexB的抑制活性.
主要成果:
- 四种分类模型在预测抑制剂功效方面实现了>80%的准确性.
- Mol.3488对AcrB和MexB排泄具有很高的结合亲和力.
- 特定的分子部分 (pyridopyrimidone,benzothiazole) 和配体定向增强了结合亲和力.
结论:
- 基于机器学习的虚拟查对于发现排泄抑制剂是有效的.
- Mol.3488代表了一种有前途的化合物,用于对抗阴性细菌感染.
- 了解结构-活性关系可以指导更强大的抑制剂的设计.
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