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).