A molecular modeling and machine learning-based approach for the identification of potential human ALOX15 inhibitors.

contextArachidonate 15-lipoxygenase (ALOX15) metabolises polyunsaturated fatty acids into lipid mediators that regulate inflammation. Depending on the cell type and substrate, ALOX15 influences both pro-inflammatory and pro-resolving pathways. Its dysregulation is associated with asthma, atherosclerosis, neurodegeneration, ferroptosis, inflammation, oxidative stress and disrupted lipid homeostasis. Therefore, targeting ALOX15 offers a promising strategy for modulating pathological lipid oxidation.

methodIn this study, a hybrid ligand-based and structure-based machine learning (ML) framework was developed that combines physicochemical and 3D ligand features with interacting features (including SIFts) derived from protein-ligand interactions for the discovery of putative ALOX15 inhibitors. To identify potent human ALOX15 inhibitors, predictive machine learning (ML) models were developed, a dataset was obtained from BindingDB and multiple molecular representations (1-D, 2-D, 3-D descriptors, fingerprints and post-docking features) were computed. Two classes of ML models, Type A and Type B, were developed using 8 regression algorithms. The best Type A model, XGBoost, achieved training R