From: Multi-channel PINN: investigating scalable and transferable neural networks for drug discovery

Base model | Value | Description | |
---|---|---|---|

PINN | Separated layers | 1, 2, 3, 4 | The number of separated layers for PINN |

Concatenated layers | 1, 2 | The number of concatenated layers for PINN | |

Number of nodes | 256, 512, 1024, 2048 | The number of nodes for layers | |

Dilated CNN | Filters | 4, 8, 16, 32 | The number of filters for Dilated CNN |

Kernel size | 6, 8, 12, 22 | The length of the convolution window for Dilated CNN | |

Embedding | 16, 32 | Dimension of dense embedding for low level representations | |

LSTM, BLSTM | Units | 128, 256 | The units to represent hidden layers for RNN |

DNN | Lr | 0.0005 | Initial learning rate |

Initializer | \([ - \sqrt{ 3 / fan_{in}}, \sqrt{ 3 / fan_{in}}]\) | Initial weight value called Lecun uniform distribution | |

Optimizer | Adam | Optimizer for stochastic gradient descent | |

Weight decay | 0.0, 0.00001 | Learning rate decay over each update | |

Activation function | ReLU, ELU | Neuron activation function | |

Drop out | 0.25, 0.5 | The rate of drop out | |

Batch | 1024 | Batch size for training | |

Epochs_training | 400 | Training epochs on a training task | |

Epochs_finetune | 200 | Finetuning epochs for a pretrained model on a test task |