pIC50 prediction on pIC50 prediction dataset (test)
0.73RMSEUnified RNN-CNN
Evaluation Results
| Method | Links | ||
|---|---|---|---|
| Unified RNN-CNNArchitecture=Unified RNN-CNN, Ensemble Strategy=parameter+NN ensemble, Representation=novel representations learned from seq2seq2018.06 | 0.73 | 0.86 | |
| Unified RNN-CNNArchitecture=Unified RNN-CNN, Ensemble Strategy=parameter ensemble, Representation=novel representations learned from seq2seq2018.06 | 0.77 | 0.84 | |
| Unified RNN-CNNArchitecture=Unified RNN-CNN, Ensemble Strategy=single, Representation=novel representations learned from seq2seq2018.06 | 0.78 | 0.84 | |
| Random ForestArchitecture=RF, Ensemble Strategy=single, Representation=novel representations learned from seq2seq2018.06 | 0.91 | 0.78 | |
| Separate RNN-CNNArchitecture=Separate RNN-CNN, Ensemble Strategy=parameter+NN ensemble, Representation=novel representations learned from seq2seq2018.06 | 0.92 | 0.77 | |
| Separate RNN-CNNArchitecture=Separate RNN-CNN, Ensemble Strategy=parameter ensemble, Representation=novel representations learned from seq2seq2018.06 | 0.94 | 0.76 |