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<p>Fungal infections are an increasing global health issue. Despite available treatments, fungal resistance reduces medicine effectiveness. This research conducted QSAR analysis on fifty-one 4-aryl-2-hydrazinothiazole derivatives previously evaluated for antifungal activity. The QSAR model was derived from a hybrid method combining genetic algorithms (GA) and multiple linear regression (MLR). The analysis showed a negative correlation between pMIC and RDF100e, ITH, R4m+, RDF120s, and GATS8e. The model was validated using an external test set by the leave-one-out cross-validation method. Additionally, Y-randomization, MAE, and Golbraikh-Tropsha metrics assessed the model’s applicability domain. The study offers an in-depth molecular descriptor interpretation through three methods: atomic pair distribution, substructure-based analysis, and molecular surface mapping with cumulative atomic contributions. These methods help identify favorable and unfavorable structural groupings. Key molecular features influencing antifungal activity were identified, particularly the spatial arrangement of N1-hydrazine and C4 fragments in the thiazole nucleus. The research highlights Van der Waals interactions, electronegative atoms in substituents, and electron-donating groups. To address the limitations of modeling a small dataset, we applied the novel ARKA approach—based on Arithmetic Residuals in K-groups Analysis—to reduce descriptor dimensionality while preserving chemical relevance and improving interpretability.</p> Graphical abstract <p></p> <p>In the era of artificial intelligence (AI), molecular descriptor interpretation remains crucial in QSAR studies, especially for small datasets, where AI methods are less effective. This study identifies key molecular descriptors governing the antifungal activity of thiazole derivatives against <i>Candida albicans</i> through QSAR modeling. The findings clarify molecular influences on bioactivity, providing a valuable alternative to AI for predicting compound efficacy.</p>

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Interpretable Quantitative Structure–Activity Relationship (QSAR) for identification of potent antifungal activity agents towards Candida albicans ATCC 2091

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年11月28日

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  • Mariusz Zapadka,
  • Krzysztof Zbigniew Łączkowski,
  • Anna Budzyńska,
  • Mateusz Maciejewski,
  • Przemysław Dekowski,
  • Bogumiła Kupcewicz

摘要

Fungal infections are an increasing global health issue. Despite available treatments, fungal resistance reduces medicine effectiveness. This research conducted QSAR analysis on fifty-one 4-aryl-2-hydrazinothiazole derivatives previously evaluated for antifungal activity. The QSAR model was derived from a hybrid method combining genetic algorithms (GA) and multiple linear regression (MLR). The analysis showed a negative correlation between pMIC and RDF100e, ITH, R4m+, RDF120s, and GATS8e. The model was validated using an external test set by the leave-one-out cross-validation method. Additionally, Y-randomization, MAE, and Golbraikh-Tropsha metrics assessed the model’s applicability domain. The study offers an in-depth molecular descriptor interpretation through three methods: atomic pair distribution, substructure-based analysis, and molecular surface mapping with cumulative atomic contributions. These methods help identify favorable and unfavorable structural groupings. Key molecular features influencing antifungal activity were identified, particularly the spatial arrangement of N1-hydrazine and C4 fragments in the thiazole nucleus. The research highlights Van der Waals interactions, electronegative atoms in substituents, and electron-donating groups. To address the limitations of modeling a small dataset, we applied the novel ARKA approach—based on Arithmetic Residuals in K-groups Analysis—to reduce descriptor dimensionality while preserving chemical relevance and improving interpretability.

Graphical abstract

In the era of artificial intelligence (AI), molecular descriptor interpretation remains crucial in QSAR studies, especially for small datasets, where AI methods are less effective. This study identifies key molecular descriptors governing the antifungal activity of thiazole derivatives against Candida albicans through QSAR modeling. The findings clarify molecular influences on bioactivity, providing a valuable alternative to AI for predicting compound efficacy.