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MBC and ECBL Libraries: outstanding tools for drug discovery

Authors: Tiziana Ginex; Enrique Madruga; Ana Martínez; Carmen Gil;

MBC and ECBL Libraries: outstanding tools for drug discovery

Abstract

UPDATE. New in this revision: python scripts to process DBs and calculate the percentage of molecules which pass the Veber and Ghose filters. - Veber_filter.py ad Ghose filter.py (data presented in Table 1 of the MS). Data and scripts to reproduce all the graphics reported in the Manuscript entitled: "MBC and ECBL Libraries: outstanding tools for drug discovery". List of analyzed DBs: 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). Files: QikProp_properties.docx: doc file containing the full list of QikProp properties calculated for each analyzed DB. DATA_comparison.xlsx: excel file containing data used to reproduce plots in Figure 4 of the MS. Murcko_scaffold_percentages: distribution (%) of the first 50 most populated Murcko scaffolds for MBC2016, MBC2022 and ECBL. Murcko_scaffolds_comparison: distribution (count) of the first 94 common Murcko scaffolds for MBC2016, MBC2022 and ECBL. QikProp properties for all the analyzed DBs (6 files; CSV format). SMILES codes for all the analyzed DBs (6 files; SMI format). joinplots.py: python script to generate the 2D plots in Figure 2 of the MS. fingerprint_similarity.py: python script to run and generate the Tanimoto similarity plots in Figure 3 of the MS. calc_kde.py: python script to run kernel density analysis reported in Figure 5 of the MS.

Revised version of the original publication.

Related Organizations
Keywords

Chemical databases, Virtual Screening

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This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
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