Computational integration for antifungal 1,2,4-triazole inhibitors design: QSAR, molecular docking, molecular dynamics simulations, ADME/Tox, and retrosynthesis studies

Abstract

Fungal infections are a growing public health problem worldwide. Despite the availability of several medicines, their efficacy is still constrained by fungal resistance. This research conducted the 2D/3D-QSAR analysis on twenty-nine triazole molecules previously evaluated for their antifungal activity. The HQSAR/B-H, CoMFA and CoMSIA models were built using twenty-three molecules in the training set. They show high Q2 values (0.646, 0.564 and 0.561, respectively) and important R2 values (0.764, 0.805 and 0.787, respectively). The predictive capacity of the established models was validated by external validation; they performed well. The contour maps derived from the HQSAR/B-H, CoMFA and CoMSIA models provide more detail to identify favorable and unfavorable groupings impacting the activity. Then, 4 proposed new triazole molecules with significant antifungal activity were suggested. In addition, the molecular docking results showed good binding energies and interactions of the proposed inhibitors in the active site of the receptor studied. The molecular dynamics and MM/PBSA methods confirmed and validated the molecular docking results. The new triazole molecules were evaluated for their oral bioavailability and toxicity using ADME/Tox properties. Finally, the retrosynthesis method created a synthetic pathway for the candidate inhibitor Z1.

Keywords

• CoMFA
• CoMSIA
• HQSAR
• Lipinski's rule
• Protein–ligand interaction