Machine learning-guided screening and validation of antioxidant small molecules from Clausena lansium

Abstract

To discover natural antioxidants from Clausena lansium (Lour.) Skeels for food applications, we established a 474-compound database and applied a multiscale workflow integrating ensemble machine learning, molecular docking, structural clustering, molecular dynamics simulations, and quantum chemical calculations. The ensemble models achieved ROC-AUC values of 0.85–0.99 across eight antioxidant assays. Multi-criteria screening yielded 47 candidates, and 30 structurally diverse candidates underwent molecular dynamics simulations. Quercetin 3-arabinoside was prioritized, exhibiting stable non-covalent binding within the Keap1 Kelch domain during a 500 ns simulation. Quantum chemical calculations showed a HOMO–LUMO gap of 4.0589 eV, 25.5% lower than that of vitamin C. Given standard availability, its structural isomer Avicularin was assessed and exhibited dose-dependent antioxidant activity, with DPPH and ABTS IC50 values of 26.84 and 6.94 μM, respectively, and a FRAP value of 1.327 ± 0.218 mmol FeSO4 equivalents L−1 at 25 μM. This study supports antioxidant discovery from edible fruits.