Machine Learning-Guided Screening and Experimental Validation of Plant-Derived Antifungal Compounds for Protecting Bamboo against Mold

Abstract Bamboo is a renewable material, but it is highly susceptible to mold growth under warm and humid conditions, which can significantly impair its serviceability and practical value. In this study, an XGBoost-based QSAR model integrating physicochemical descriptors and Morgan fingerprints was developed to screen plant-derived antifungal compounds against Aspergillus niger. The model achieved an R2 of 0.983 for the training set. Model-guided screening and subsequent antifungal assays identified vanillin, p-anisaldehyde, and geraniol as promising candidates. All three compounds inhibited common bamboo molds, and vanillin provided the best overall protection for sliced bamboo veneers. A source-expanded screening life cycle assessment showed that, with 80% ethanol recovery, the vanillin route had a GWP100 of 23.90 kg CO2-eq per 1000 m2 of treated sliced bamboo veneer, compared with 48.61 kg CO2-eq for ACQ-D, representing a reduction of 24.71 kg CO2-eq (50.83%). FTIR and SEM analyses indicated that impregnation altered the surface morphology and molecular environment of bamboo without producing readily detectable new functional groups. Physiological assays showed that all three compounds increased fungal membrane permeability and reduced ergosterol content. Molecular docking further suggested that the compounds may interact with the catalytic pocket of lanosterol 14α-demethylase (CYP51A), potentially interfering with ergosterol biosynthesis. These findings demonstrate the value of integrating machine-learning screening, experimental validation, mechanistic analysis, and environmental assessment to develop more sustainable mold-protection strategies for bamboo.