MBC and ECBL Libraries: outstanding tools for drug discovery

<strong>UPDATE</strong>. New in this revision: python scripts to process DBs and calculate the percentage of molecules which pass the Veber and Ghose filters. Two new DBs were also added and considered for the analysis. Data and scripts to reproduce all the graphics reported in the Manuscript entitled: "MBC and ECBL Libraries: outstanding tools for drug discovery". <strong>List of analyzed DBs:</strong> MBC2016 (Total entries: 1,096 cmpds; 7.39% excluded from properties analysis - QikProp failure). MBC2022 (Total entries: 2,577 cmpds; 3.14% excluded from properties analysis - QikProp failure). ECBL (Total entries: 101,021 cmpds; 0.20% excluded from properties analysis - QikProp failure). ChEMBL v.31 (Total entries 1,908,325 cmpds; 2.97% excluded from properties analysis - QikProp failure). DrugBank v.5.0 (Total entries 10,981 cmpds; 4.13% excluded from properties analysis - QikProp failure). ZINC20 (Total entries 10,723,360 cmpds; 0.61% excluded from properties analysis - QikProp failure). NuBBE (Total entries 2,223 cmpds) - <strong>NEW</strong> Approved drugs (Total entries: 3,140 cmpds) - <strong>NEW</strong> <strong>Files:</strong> <em>QikProp_properties.docx</em>: doc file containing the full list of QikProp properties calculated for each analyzed DB. <em>DATA_comparison.xlsx</em>: excel file containing data used to reproduce plots in <strong>Figure 4</strong> of the MS. <em>Murcko_scaffold_percentages</em>: distribution (%) of the first 50 most populated Murcko scaffolds for MBC2016, MBC2022 and ECBL. <em>Murcko_scaffolds_comparison</em>: distribution (count) of the first 94 common Murcko scaffolds for MBC2016, MBC2022 and ECBL. QikProp properties for all the analyzed DBs (8 files; CSV format). SMILES codes for all the analyzed DBs (8 files; SMI format). <em>joinplots.py</em>: python script to generate the 2D plots in <strong>Figure 2</strong> of the MS. <em>fingerprint_similarity.py</em>: python script to run and generate the Tanimoto similarity plots in <strong>Figure 3</strong> of the MS. <em>calc_kde.py</em>: python script to run kernel density analysis reported in <strong>Figure 5 </strong>of the MS. <em>Veber_filter.py: python script to generate </em>data presented in <strong>Table 1 </strong>of the MS. <strong>(NEW)</strong> <em>Ghose filter.py: python script to generate </em>data presented in <strong>Table 1 </strong>of the MS. <strong>(NEW)</strong>