Once docking results are collected and stored in the database, it is possible to filter it to identify promising hits which are commonly processed through visual inspection. 7 This entails going from millions of ligands down to approximately 500-1000 for individual visual Figure : Disk storage requirements for a virtual screening of a 1.7 M ligand library docked with one flexible residue. The total sizes for raw and compressed docking files are represented by the proportional area of the largest circle (blue), and the second largest (cyan), respectively. The black and white dotted lines illustrate the expected size of the results when AD-GPU the raw DLG files with only 3 poses and in Vina PDBQT format with 6 poses, respectively. The areas of the yellow and orange circles, respectively, illustrate the size of the database generated by rt process vs.py with default settings (store maximum 3 poses/ligand) uncompressed and compressed. The total data size for the output DLGs were estimated from the sizes of packages of 10,000 ligands. Plot produced with ggplot2 in RStudio. analysis. Given the very low throughput of this last step, 7 it is essential to have meaningful tools that are capable of isolating the desired information and enriching the results based on both target-and ligand-dependent properties. At the same time, these tools are required to be very efficient in order to minimize the time required to go through the large amount of unprocessed data. To this end, Ringtail has a number of filter options split into two groups: property filters and interaction filters. The property filters include energy and ligand efficiency (LE, calculated by dividing the docking score by the number of heavy atoms). Property filters can be specified by using explicit values or by setting percentile cutoffs calculated on the entire results set.