With diverse nucleophilic amino acids in ligandable proteins and the emerging need for comprehensive mapping of the human proteome, covalent drug design approaches are poised for significant advancements. The identification and targeting of cryptic ligandable sites within proteins present immense potential for novel therapeutic interventions. However, the challenges in predicting and assessing these sites, particularly beyond cysteine residues, underscore the critical need for advanced computational tools. The developed pipeline (Figure ), leveraging AI-driven predictive models robustly assesses and validates chemical proteomics-based reactive sites with exceptional speed. These advancements not only enable the detection of cryptic pockets but also pave the way for a more efficient and systematic evaluation of potential drug targets. By offering a scalable platform for ligandability assessment and rational hit filtration, this work takes a significant step towards addressing the challenges in covalent drug discovery, ultimately aiding for more efficient, precise, and successful drug development strategies in the future.