Computational Discovery of Antifungal Candidates Against Moniliophthora perniciosa: Multi-target Virtual Screening, Molecular Docking, and QSAR Modeling

Abstract Moniliophthora perniciosa, the causal agent of witches' broom disease of cacao (Theobroma cacao L.), causes annual losses exceeding 30% of Brazilian production, yet no specific fungicide has been developed for this pathosystem. We present the first multi-target computational pipeline for antifungal discovery against M. perniciosa, integrating three fungal-specific targets: lanosterol 14α-demethylase (ERG11/CYP51), alternative oxidase (MpAOX), and chitin synthase class III (CHS). Bioactivity data for 1,398 compounds from ChEMBL were integrated with homology-based structural models and AlphaFold2 predictions. Molecular docking with AutoDock Vina 1.2.5 across five protein structures yielded 92 binding poses. QSAR models (Random Forest, ECFP4 fingerprints) achieved external validation R² of 0.771 (ERG11) and 0.462 (MpAOX). ADMET profiling identified 293 drug-like candidates from 459 pre-filtered compounds. Composite multi-criteria scoring ranked CHEMBL133046 (a farnesyl-hydroquinone derivative; docking affinity −10.16 kcal/mol; IC₅₀ = 2.0 nM; QED = 0.692) as top candidate against MpAOX, alongside N-alkylamide propionamide scaffolds with sub-nanomolar potency against CHS. The results provide a computationally validated shortlist of structurally diverse antifungal candidates with favorable pharmacological profiles for prioritized experimental validation.