Mechanism‐Aware Deep Learning Maps the Redox Landscape of Cancer‐Relevant Antioxidants

Reactive oxygen species (ROS) occupy a mechanistically complex role in cancer, simultaneously sustaining oncogenic signaling and generating oxidative vulnerability. Existing antioxidant prediction approaches fail to account for this mechanistic stratification or the chemical diversity of redox‐active small molecules. Here, we curate human‐relevant ROS modulators from PubChem BioAssays and the antioxidant database (AODB) and segregate the compiled compounds into three coherent mechanistic regimes: signaling/metabolic modulators (HIF, NF‐κB), antioxidant‐defense activators (NRF2/KEAP1), and ROS source inhibitors (NOX/XDH). Building on this, we introduce a dual prediction framework, named mechanism‐informed hierarchical multitask learning (MI‐HMTL), comprising a multimodal chemistry‐driven classical model (BioChem‐AOS) and a mechanism‐aware hierarchical multitask deep learning system (MA‐AOS), both powered by unified chemical dice integrator (CDI) generalised embeddings. MA‐AOS achieves high predictive accuracy across six mechanistic targets and, similar to structure‐driven scoring, uniquely recapitulates metabolic redox behavior in human tumor metabolomics samples. These findings demonstrate that the mechanistic context governs antioxidant function, positioning this framework as a scalable platform for mechanism‐guided discovery of redox therapeutics.