However, these programs efficiently serve their purpose only with prior knowledge of the binding site. As a result, they often require independent and laborious use in combination with non-/covalent binding site predictor programs such as P2Rank 29 , PocketFinder 30 , SiteMap , and PocketMiner , to initially identify non-covalent binding cavities, followed by calculations that suggest whether the binding pocket is amenable to covalent modification. Nevertheless, while some of these programs can be impractically coupled with nascent cysteine reactivity predictors such as DeepCoSi 32 , sbPCR , and HyperCys 34 , many of these programs are intended for topdown proteomics discovery or have limitations. None of these platforms are designed with a utility of assessing ligandabilities across large numbers of chemoproteomics sites or are tailored for exploring sites other than cysteines. Recently, an MS-based quantitative proteolysis method (LiP-Quant) lightly integrating ML was used to prioritize true drug targets in chemoproteomics output dataset . Although an expensive method anticipating establishment of acceptable detection rates on genuine ligandable sites and false-positives, LiP-Quant method can be promising for future chemical proteomics-based drug discovery research. In this work, we sought to develop an inexpensive method capable of analyzing existing chemoproteomics target sites in public or private repositories for proximal binding cavities and prioritize them using a ligandability score.