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Table 1 Bioactivity datasets assembled from ChEMBL repository and utilized in this study

From: Deep-learning: investigating deep neural networks hyper-parameters and comparison of performance to shallow methods for modeling bioactivity data

Activity class CHEMBL target id Number of active inhibitors Number of decoys
Carbonic anhydrase II,
Class: enzyme, lyase
CHEMBL205 1631 16,310
Cyclin-dependent kinase 2,
Class: protein kinase
CHEMBL301 705 7050
HERG,
Class: Voltage-gated ion channel
CHEMBL240 700 7000
Dopamine D4 receptor,
Class: membrane receptor, GPCR
CHEMBL219 506 5060
Coagulation factor X,
Class: enzyme, serine protease
CHEMBL244 1144 11,440
Cannabinoid CB1 receptor,
Class: membrane receptor, GPCR
CHEMBL218 1911 19,013
Cytochrome P450 19A1,
Class: enzyme, cytochrome P450
CHEMBL1978 621 6210
  1. The ratio of decoys/active per activity class was set to 10:1