The exploring AI-generated pyrazalone derivatives as antifungal agents: Bringing together molecular docking and quantum chemical approaches

Abstract

Candida albicans, an opportunistic fungal pathogen, is the most prevalent species among the twenty types of Candida responsible for candidiasis in humans. The condition is characterized by symptoms such as itching, redness, skin rashes, fever, septic shock, and infections of mucous membranes. This study explores the potential of pyrazolones and their AI-generated derivatives as effective treatments for these fungal infections. We conducted molecular docking, quantum molecular simulations, drug-likeness study, spectroscopic analysis, electrostatic potential analysis, and topological analysis to evaluate the potential of these derivatives as effective pharmaceuticals alongside molecular dynamics (MD) simulations. Our results show that several of these derivatives bind strongly to the target protein N-myristoyl transferase (NMT), showing a range of binding energies from −9.2 to −9.8 kcal/mol. Further insights revealed that D1 interacts with the NMT protein through two hydrogen-bonding residues HIS-227 and LEU-355, while D2 forms hydrogen bonds with ASP-110 and VAL-108. The ADMET profiling performed using the pkCSM platform identified D1 as a lead candidate, exhibiting optimal intestinal absorption and a maximum total clearance rate, which aligns with the criteria for drug-likeness and therapeutic viability. Additionally, our results showed that these derivatives had stronger binding affinities as compared to the parent compound. Molecular dynamics simulations of selected complexes (D1, D2, D5, and D6) over 120 ns demonstrated their structural stability and dynamic flexibility, as indicated by metrics encompassing root mean square fluctuation (RMSF), root mean square deviation (RMSD), radius of gyration (Rg), and solvent accessible surface area (SASA). The values of RMSD, remaining well within the permissible 4 Å threshold, reflect minimal structural fluctuation, that support the concept of stable complexes during the simulation. Quantum chemical calculations revealed that D1 and D4 had enhanced reactivity, which may improve their ability to interact with biological targets. This study also compared experimental and theoretical approaches to analyzing the properties of the parent compound. Our computational findings demonstrate that derivatives D1 and D2 exhibit strong binding to NMT, a validated antifungal target, with interactions critical for disrupting fungal cell viability. ADMET profiling further identifies D1 as a promising lead with favorable pharmacokinetics, suggesting its potential to inhibit Candida albicans growth in vivo. These results position our derivatives as biologically relevant candidates for experimental validation, advancing the development of novel antifungal therapies.