pIC50 prediction on pIC50 Generalization - GPCR
1.36RMSEUnified RNN-CNN
Evaluation Results
| Method | Links | ||
|---|---|---|---|
| Unified RNN-CNNArchitecture=Unified RNN-CNN, Ensemble Strategy=parameter+NN ensemble, Representation=novel representations learned from seq2seq2018.06 | 1.36 | 0.3 | |
| Random ForestArchitecture=RF, Ensemble Strategy=single, Representation=novel representations learned from seq2seq2018.06 | 1.4 | 0.25 | |
| Unified RNN-CNNArchitecture=Unified RNN-CNN, Ensemble Strategy=single, Representation=novel representations learned from seq2seq2018.06 | 1.4 | 0.24 | |
| Unified RNN-CNNArchitecture=Unified RNN-CNN, Ensemble Strategy=parameter ensemble, Representation=novel representations learned from seq2seq2018.06 | 1.4 | 0.24 | |
| Separate RNN-CNNArchitecture=Separate RNN-CNN, Ensemble Strategy=parameter+NN ensemble, Representation=novel representations learned from seq2seq2018.06 | 1.41 | 0.2 | |
| Separate RNN-CNNArchitecture=Separate RNN-CNN, Ensemble Strategy=parameter ensemble, Representation=novel representations learned from seq2seq2018.06 | 1.44 | 0.19 |