The oxidation of unsaturated lipids, facilitated by the enzyme Arachidonic acid 15-lipoxygenase (ALOX15), is an essential element in the development of ferroptosis. This study combined a dual-score exclusion strategy with high-throughput virtual screening, naive Bayesian and recursive partitioning machine learning models, the already established ALOX15 inhibitor i472, and a docking-based fragment substitution optimisation approach to identify potential ALOX15 inhibitors, ultimately leading to the discovery of three FDA-approved drugs that demonstrate optimal inhibitory potential against ALOX15. Through fragment substitution-based optimisation, seven new inhibitor structures have been developed. To evaluate their practicality, ADMET predictions and molecular dynamics simulations were performed. In conclusion, the compounds found in this study provide a novel approach to combat conditions related to ferroptosis-related injury by inhibiting ALOX15.
Keywords: ALOX15; Ferroptosis; machine learning; molecular dynamic simulation; virtual screening.