Although several methods to predict CCS values are available, few of these are suitable for a large database like PubChem, or even PubChemLite. The calculation time of quantum methods (e.g., MobCal and ISiCLE ) is prohibitive. Furthermore, since ISiCLE only predicts values for C, H, N, O, P and Scontaining compounds, ~40% of PubChemLite would remain uncovered. Machine learning (ML) prediction methods developed on experimental CCS datasets are much faster, with options including AllCCS , CCSbase 20 , SigmaCCS 21 , DeepCCS . and CCS Predictor 2.0 . Since initial calculations with CCSbase were promising, this became the method of choice to establish pipelines for PubChemLite. Calculations are performed using cs3db (the code base behind CCSbase 20 ), which has been re-trained with the updated experimental database in CCSbase using the same methodology , modified to run on PubChem internal systems. Following the monthly PubChemLite release, CCS value calculations (by PubChem) are triggered on the second day of the following month. The resulting file is transferred by FTP, merged into the PubChemLite files, quality-controlled, then released to Zenodo (DOI: 10.5281/zenodo.4081056 redirects to the latest version).