采用机器学习来识别抗真菌化合物来对抗Candida albicans
Dienny Rodrigues de Souza1,2, Lívia Do Carmo Silva1, Kleber Santiago Freitas E Silva1
1Laboratory of Molecular Biology, Institute of Biological Sciences, Federal University of Goiás, Goiânia, Brazil.
Future microbiology
|July 2, 2025
概括
机器学习模型有效地识别了针对Candida albicans的新抗真菌化合物. 随机森林 (RF) 模型成功选了化学库,从而发现了强大的抗真菌剂.
科学领域:
- 计算化学是一种计算化学.
- 药品化学 药品化学 是一个
- 菌类学 菌类学是指菌类学.
背景情况:
- 抗真菌耐药性的出现需要发现新的治疗剂.
- 机器学习 (ML) 提供了一种强大的方法,通过预测化合物活性来加速药物发现.
研究的目的:
- 评估ML模型在预测对Candida albicans的抗真菌活性方面的有效性.
- 开发分类和回归模型,用于识别新型抗真菌化合物.
主要方法:
- 使用随机森林 (RF),支持矢量机 (SVM) 和LightGBM算法对 eMolecules® 库进行选.
- 根据ML预测选择了17个虚拟命中,用于随后的体外验证.
- 在体外抗真菌测试以确定针对Candida albicans的选择性化合物的活性.
主要成果:
- 在17种选择的化合物中,有11种具有针对Candida albicans的抗真菌活性.
- 化合物1和17表现出显著的抑制,最小抑制度 (MIC) 分别为0.51微米和0.071微米.
- 射频模型在虚拟查和预测抗真菌潜力方面表现出高效.
结论:
- 开发的ML分类和回归模型对于识别新的抗真菌分子是有效的.
- 射频模型是虚拟查的宝贵工具,用于寻找针对Candida albicans的新型抗真菌剂.
- 这项研究强调了ML在加速急需的抗真菌疗法的发现方面的潜力.
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