VDLIN: A Deep Learning‐Based Platform for Methylcobalamin‐Inspired Immunomodulatory Compound Screening

Abstract During the COVID‐19 pandemic, methylcobalamin (MCB), an active form of Vitamin B12 (VB12), showed therapeutic potential in mitigating the cytokine storm associated with SARS‐CoV‐2 infection. While MCB's significant anti‐inflammatory properties are confirmed, it is also observed that its treatment may impair macrophage‐mediated innate immune responses. Comprehensive RNA‐seq, ATAC‐seq, and CUT&Tag analyses revealed that MCB reduces inflammation and weakens innate immunity by limiting chromatin accessibility at NF‐κB and EGR1 binding sites, leading to decreased IFNB1 production and enhanced viral immune evasion. To address this challenge, a deep learning model, VDLIN ( Vitamin B12‐derived Deep Learning for Innate Immunity ), is developed to identify compounds capable of both suppressing inflammation and boosting innate immunity. As anticipated, VDLIN identified a novel compound, “Co7,” which retains MCB's strong anti‐inflammatory effects while also enhancing immune activation via the TLR4 signaling pathway. Co7 thus emerges as a promising therapeutic candidate, offering advantages over MCB by balancing anti‐inflammatory and immune‐stimulatory functions. Taken together, this study sheds light on the intricate interplay between chromatin dynamics and immune regulation, presenting new opportunities for therapeutic interventions in inflammatory diseases and SARS‐CoV‐2 infection.