For all modeling work: |
▪ Standardization and curation of the investigated dataset to ensure consistency. This should include a clearly-stated method (including inclusion and exclusion criteria) for curation of the data and a review of the rules applied to chemical structures in order to ensure standardization |
QSAR models: |
▪ Use of sufficiently diverse training set covering the EDC compound domain of interest |
▪ Use of sufficiently diverse external test set covering the EDC compound domain of interest should be used |
▪ Assembly of internal and external validation, i.e. several internal and external validation sets, and models created in a double loop fashion, followed by consensus predictions |
▪ Sufficient statistical quality achieved |
▪ Consistent applicability domain established, e.g. using a conformal prediction framework |
For ligand based pharmacophore models: |
▪ Use of sufficiently diverse training set covering the EDC compound mechanism/domain of interest |
▪ All training set compounds should, approximately, fit the derived model equally well unless there are demonstrable differences in the binding affinity |
▪ Use of sufficiently diverse external test set that covers the EDC compound domain of interest to demonstrate generalizability |
Protein structure based models: |
▪ Several protein structures should be used to account for flexibility of the protein covering relevant conformations |
▪ Use of sufficiently diverse training set covering the EDC compound domain of interest |
▪ Consensus docking and scoring to ensure robustness and stability of results |
▪ Use of sufficiently diverse external test set covering the EDC compound domain of interest |