利用机器学习鉴定抗白色念珠菌的抗真菌化合物。

IF 2.5 4区生物学 Q3 MICROBIOLOGY

Future microbiology Pub Date : 2025-07-02 DOI:10.1080/17460913.2025.2525717

Dienny Rodrigues de Souza, Lívia Do Carmo Silva, Kleber Santiago Freitas E Silva, Fabricio Silva de Jesus, Amanda Alves de Oliveira, Bruno Junior Neves, Maristela Pereira

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引用次数: 0

摘要

目的：评估机器学习方法在开发白色念珠菌抗真菌活性分类和回归模型中的有效性。材料和方法：利用RF， SVM和LightGBM算法筛选emmolecules®库。选择17个虚拟命中进行体外检测。结果：11种化合物具有抗白色念珠菌活性。化合物1和17分别在0.51µM和0.071µM处抑制白色念珠菌。结论：RF模型对虚拟筛选是有效的，表明理化分类和回归模型在鉴定抗白色念珠菌新分子方面是成功的。

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Employing machine learning for identifying antifungal compounds against Candida albicans.

Aims: To evaluate the efficacy of a machine learning approach in developing classification and regression models for antifungal activity against Candida albicans.

Materials & methods: Utilized RF, SVM, and LightGBM algorithms to screen the eMolecules® library. Selected 17 virtual hits for in vitro assays.

Results: Eleven compounds showed activity against C. albicans. Compounds 1 and 17 inhibited C. albicans at 0.51 µM and 0.071 µM, respectively.

Conclusions: The RF model proved effective for virtual screening, demonstrating the success of the physicochemical classification and regression model in identifying new antifungal molecules against C. albicans.

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来源期刊

Future microbiology 生物-微生物学

CiteScore

4.90

自引率

3.20%

发文量

134

审稿时长

6-12 weeks

期刊介绍： Future Microbiology delivers essential information in concise, at-a-glance article formats. Key advances in the field are reported and analyzed by international experts, providing an authoritative but accessible forum for this increasingly important and vast area of research.