CNN+RNN

class DeepPurpose.models.CNN_RNN(nn.Sequential)

CNN_RNN means a GRU/LSTM on top of a CNN on SMILES.

constructor create CNN_RNN

__init__(self, encoding, **config)

• encoding (string, “drug” or “protein”) - specify input type, “drug” or “protein”.

• config (kwargs, keyword arguments) - specify the parameter of transformer. The keys include

  • cnn_drug_filters (list, each element is int) - specify the size of filter when encoding drug, e.g., cnn_drug_filters = [32,64,96].

  • cnn_drug_kernels (list, each element is int) - specify the size of kernel when encoding drug, e.g., cnn_drug_kernels = [4,6,8].

  • rnn_drug_hid_dim (int) - specify the hidden dimension of RNN when encoding drug, e.g., rnn_drug_hid_dim = 64.

  • rnn_drug_n_layers (int) - specify number of layer in RNN when encoding drug, .e.g, rnn_drug_n_layers = 2.

  • rnn_drug_bidirectional (bool) - specify if RNN is bidirectional when encoding drug, .e.g, rnn_drug_bidirectional = True.

  • hidden_dim_drug (int) - specify the hidden dimension when encoding drug, e.g., hidden_dim_drug = 256.

  • cnn_target_filters (list, each element is int) - specify the size of filter when encoding protein, e.g, cnn_target_filters = [32,64,96].

  • cnn_target_kernels (list, each element is int) - specify the size of kernel when encoding protein, e.g, cnn_target_kernels = [4,8,12].

  • hidden_dim_protein (int) - specify the hidden dimension when encoding protein, e.g., hidden_dim_protein = 256.

  • rnn_target_hid_dim (int) - specify hidden dimension of RNN when encoding protein, e.g., rnn_target_hid_dim = 64.

  • rnn_target_n_layers (int) - specify the number of layer in RNN when encoding protein, e.g., rnn_target_n_layers = 2.

  • rnn_target_bidirectional (bool) - specify if RNN is bidirectional when encoding protein, e.g., rnn_target_bidirectional = True

Calling functions implement the feedforward procedure of CNN_RNN.

forward(self, v)

• v (torch.Tensor) - input feature of CNN_RNN.