Quantitative structure-activity relationship and machine learning studies of 2-thiazolylhydrazone derivatives with anti-Cryptococcus neoformans activity

Abstract

Cryptococcus neoformans is a fungus responsible for infections in humans with a significant number of cases in immunosuppressed patients, mainly in underdeveloped countries. In this context, the thiazolylhydrazones are a promising class of compounds with activity against C. neoformans. The understanding of the structure-activity relationship of these derivatives could lead to the design of robust compounds that could be promising drug candidates for fungal infections. Specifically, modern techniques such as 4D-QSAR and machine learning methods were employed in this work to generate two QSAR models (one 2D and one 4D) with high predictive power (r² for the test set equals to 0.934 and 0.831, respectively), and one random forest classification model was reported with Matthews correlation coefficient equals to 1 and 0.62 for internal and external validations, respectively. The physicochemical interpretation of selected models, indicated the importance of aliphatic substituents at the hydrazone moiety to antifungal activity, corroborating experimental data.

Communicated by Ramaswamy H. Sarma

Keywords:

• Antifungal agents
• thiazolylhydrazones
• Cryptococcus neoformans
• QSAR
• 2D-QSAR
• 4D-QSAR
• machine learning
• random forest
• ligand-based drug design (LBDD)

Author contribution

POF carried out and interpreted classification models; JPAM carried out and interpreted 4D-QSAR models; EBM carried out and interpreted 4D-QSAR models; VGM carried out chemometrics analyzes and SOM model; TK and VGM designed the experiments; POF, JPAM, EBM, RBO, TK, and VGM wrote and revised the manuscript.

Acknowledgements

The authors would like to thank OpenEye Scientific Software for the OMEGA academic license, Pro-Reitoria de Pesquisa – UFMG for intramural funding, CNPq and FAPEMIG for financial support.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

Fundação de Amparo à Pesquisa do Estado de Minas Gerais;Pró-Reitoria de Pesquisa, Universidade Federal de Minas Gerais.