Covalent fragment-based drug discovery (FBDD) approaches using quantitative mass spectrometry (QMS) have conventionally been used to study inaccessible protein targets, reveal cryptic pockets and identify new potential targets in the proteome . However, the success rate of chemical proteomics-based drug screening is comparable to crystallographic fragmentscreening (5-10%). Given the molecular dynamism of protein structures and the potential for occlusion of many transitory binding cavities, subpockets or PPI sites, an integrative in-silico pipeline that searches for potential druggable sites near nucleophilic residues specifically is a necessary part of the chemical proteomics-based covalent drug design pipeline. Typical cell-based shotgun/bottom-up proteomics experiments can suggest >10,000 implicit protein targets 42 however, visual inspection or in-vitro biophysical validation of each suggestive chemical proteomics-based hit is impractical and impossible. Given this challenge and in meeting the growing demand for TCI development , we developed a computational pipeline that uses an AI-based ligandable predictor (DeepPocket -developed) to scrupulously assess and mass-validate chemical proteomics-based reactive sites for apparent and cryptic ligandable cavities adjacent to each site. This comprehensive platform allows researchers to upload a proteomics-based covalent hits and perform a mass-scale ligandability evaluation of each suggestive target site for filtering out improbable hits. DeepPocket has emerged as a state-ofthe-art model which builds on an established computational method, namely FPocket , to map out the precise boundaries of ligandable sites and detect subcavities on a protein structure.