MBC and ECBL Libraries: outstanding tools for drug discovery

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