MBC and ECBL Libraries: outstanding tools for drug discovery

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:

1. MBC2016 (Total entries: 1,096 cmpds; 7.39% excluded from properties analysis - QikProp failure).
2. MBC2022 (Total entries: 2,577 cmpds; 3.14% excluded from properties analysis - QikProp failure).
3. ECBL (Total entries: 101,021 cmpds; 0.20% excluded from properties analysis - QikProp failure).
4. ChEMBL v.31 (Total entries 1,908,325 cmpds; 2.97% excluded from properties analysis - QikProp failure).
5. DrugBank v.5.0 (Total entries 10,981 cmpds; 4.13% excluded from properties analysis - QikProp failure).
6. 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.