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. 2019 Feb 15;35(18):3329–3338. doi: 10.1093/bioinformatics/btz111

Table 8.

Comparing unified RNN-CNN and unified RNN/GCNN-CNN based on RMSE (and Pearson’s r) for pIC50 prediction

SMILES rep.
Graph rep.
Single Parameter Parameter+NN Single Parameter Parameter+NN
ensemble ensemble ensemble ensemble
Training 0.47 (0.94) 0.45 (0.95) 0.44 (0.95) 0.55 (0.92) 0.54 (0.92) 0.55 (0.92)
Testing 0.78 (0.84) 0.77 (0.84) 0.73 (0.86) 1.50 (0.35) 1.50 (0.35) 1.34 (0.45)
Generalization—ER 1.53 (0.16) 1.52 (0.19) 1.46 (0.30) 1.68 (0.05) 1.67 (0.03) 1.67 (0.07)
Generalization—Ion Channel 1.34 (0.17) 1.33 (0.18) 1.30 (0.18) 1.43 (0.10) 1.41 (0.13) 1.35 (0.12)
Generalization—GPCR 1.40 (0.24) 1.40 (0.24) 1.36 (0.30) 1.63 (0.04) 1.61 (0.04) 1.49 (0.07)
Generalization—Tyrosine Kinase 1.24 (0.39) 1.25 (0.38) 1.23 (0.42) 1.74 (0.01) 1.71 (0.03) 1.70 (0.03)

Note: Bold-faced entries correspond to the best performance for each row (data set).