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35 changes: 35 additions & 0 deletions
35
apps/drug_target_interaction/hybriddta/pointwise/GraphDTA/get_len.py
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| # Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved. | ||
| # | ||
| # Licensed under the Apache License, Version 2.0 (the "License"); | ||
| # you may not use this file except in compliance with the License. | ||
| # You may obtain a copy of the License at | ||
| # | ||
| # http://www.apache.org/licenses/LICENSE-2.0 | ||
| # | ||
| # Unless required by applicable law or agreed to in writing, software | ||
| # distributed under the License is distributed on an "AS IS" BASIS, | ||
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | ||
| # See the License for the specific language governing permissions and | ||
| # limitations under the License. | ||
|
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| """Calculate length of each group in dataset.""" | ||
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| import pandas as pd | ||
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| def get_kiba_len(): | ||
| # Get length of validation set | ||
| for cv in ["CV1", "CV2", "CV3", "CV4", "CV5"]: | ||
| df = pd.read_csv("../../Data/KIBA/"+cv+"/"+cv+"_KIBA_unseenP_seenD_val.csv") | ||
| df = df.groupby(['Target ID']).size().reset_index(name = 'counts') | ||
| f = open("../../Data/KIBA/"+cv+"/"+cv+"_val.txt",'a') | ||
| for i in df['counts'].values: | ||
| f.write(str(i) + "\n") | ||
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| # Get length of testing set | ||
| df = pd.read_csv("../../Data/KIBA/test_KIBA_unseenP_seenD.csv") | ||
| df = df.groupby(['Target ID']).size().reset_index(name = 'counts') | ||
| f = open("../../Data/KIBA/kiba_len.txt",'a') | ||
| for i in df['counts'].values: | ||
| f.write(str(i) + "\n") |
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74
apps/drug_target_interaction/hybriddta/pointwise/GraphDTA/models/gat.py
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| """GraphDTA_GAT backbone model.""" | ||
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| import torch | ||
| import torch.nn as nn | ||
| import torch.nn.functional as F | ||
| from torch.nn import Sequential, Linear, ReLU | ||
| from torch_geometric.nn import GATConv | ||
| from torch_geometric.nn import global_max_pool as gmp | ||
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| # GAT backbone model | ||
| class GATNet(torch.nn.Module): | ||
| """GAT model. | ||
| Args: | ||
| data: Input data. | ||
| Returns: | ||
| out: Prediction results. | ||
| """ | ||
| def __init__(self, num_features_xd=78, n_output=1, num_features_xt=25, | ||
| n_filters=32, embed_dim=128, output_dim=128, dropout=0.2): | ||
| super(GATNet, self).__init__() | ||
| # Basic config | ||
| self.relu = nn.ReLU() | ||
| self.dropout = nn.Dropout(dropout) | ||
| # SMILES graph branch | ||
| self.gcn1 = GATConv(num_features_xd, num_features_xd, heads=10, dropout=dropout) | ||
| self.gcn2 = GATConv(num_features_xd * 10, output_dim, dropout=dropout) | ||
| self.fc_g1 = nn.Linear(output_dim, output_dim) | ||
| # Protein sequence branch (1d conv) | ||
| self.embedding_xt = nn.Embedding(num_features_xt + 1, embed_dim) | ||
| self.conv_xt1 = nn.Conv1d(in_channels=1000, out_channels=n_filters, kernel_size=8) | ||
| self.fc_xt1 = nn.Linear(32*121, output_dim) | ||
| # Combined layers | ||
| self.fc1 = nn.Linear(256, 1024) | ||
| self.fc2 = nn.Linear(1024, 256) | ||
| self.out = nn.Linear(256, n_output) | ||
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| def forward(self, data): | ||
| """tbd.""" | ||
| # Get graph input | ||
| x, edge_index, batch = data.x, data.edge_index, data.batch | ||
| # Get protein input | ||
| target = data.target | ||
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| x = F.dropout(x, p=0.2, training=self.training) | ||
| x = F.elu(self.gcn1(x, edge_index)) | ||
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| x = F.dropout(x, p=0.2, training=self.training) | ||
| x = self.gcn2(x, edge_index) | ||
| x = self.relu(x) | ||
| x = gmp(x, batch) # global max pooling | ||
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| x = self.fc_g1(x) | ||
| x = self.relu(x) | ||
| # 1d conv layers | ||
| embedded_xt = self.embedding_xt(target) | ||
| conv_xt = self.conv_xt1(embedded_xt) | ||
| conv_xt = self.relu(conv_xt) | ||
| # Flatten | ||
| xt = conv_xt.view(-1, 32 * 121) | ||
| xt = self.fc_xt1(xt) | ||
| # Concat | ||
| xc = torch.cat((x, xt), 1) | ||
| # Add some dense layers | ||
| xc = self.fc1(xc) | ||
| xc = self.relu(xc) | ||
| xc = self.dropout(xc) | ||
| xc = self.fc2(xc) | ||
| xc = self.relu(xc) | ||
| xc = self.dropout(xc) | ||
| out = self.out(xc) | ||
| return out |
74 changes: 74 additions & 0 deletions
74
apps/drug_target_interaction/hybriddta/pointwise/GraphDTA/models/gat_gcn.py
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| """GraphDTA_GATGCN backbone model.""" | ||
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| import torch | ||
| import torch.nn as nn | ||
| import torch.nn.functional as F | ||
| from torch.nn import Sequential, Linear, ReLU | ||
| from torch_geometric.nn import GCNConv, GATConv, GINConv, global_add_pool | ||
| from torch_geometric.nn import global_mean_pool as gap, global_max_pool as gmp | ||
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| # GATGCN backbone model | ||
| class GAT_GCN(torch.nn.Module): | ||
| """GATGCN model. | ||
| Args: | ||
| data: Input data. | ||
| Returns: | ||
| out: Prediction results. | ||
| """ | ||
| def __init__(self, n_output=1, num_features_xd=78, num_features_xt=25, | ||
| n_filters=32, embed_dim=128, output_dim=128, dropout=0.2): | ||
| super(GAT_GCN, self).__init__() | ||
| # Basic config | ||
| self.relu = nn.ReLU() | ||
| self.dropout = nn.Dropout(dropout) | ||
| self.n_output = n_output | ||
| # SMILES graph branch | ||
| self.conv1 = GATConv(num_features_xd, num_features_xd, heads=10) | ||
| self.conv2 = GCNConv(num_features_xd*10, num_features_xd*10) | ||
| self.fc_g1 = torch.nn.Linear(num_features_xd*10*2, 1500) | ||
| self.fc_g2 = torch.nn.Linear(1500, output_dim) | ||
| # Protein sequence branch (1d conv) | ||
| self.embedding_xt = nn.Embedding(num_features_xt + 1, embed_dim) | ||
| self.conv_xt_1 = nn.Conv1d(in_channels=1000, out_channels=n_filters, kernel_size=8) | ||
| self.fc1_xt = nn.Linear(32*121, output_dim) | ||
| # Combined layers | ||
| self.fc1 = nn.Linear(256, 1024) | ||
| self.fc2 = nn.Linear(1024, 512) | ||
| self.out = nn.Linear(512, self.n_output) # n_output = 1 for regression task | ||
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| def forward(self, data): | ||
| """tbd.""" | ||
| # Get graph input | ||
| x, edge_index, batch = data.x, data.edge_index, data.batch | ||
| # Get protein input | ||
| target = data.target | ||
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| x = self.conv1(x, edge_index) | ||
| x = self.relu(x) | ||
| x = self.conv2(x, edge_index) | ||
| x = self.relu(x) | ||
| # Apply global max pooling (gmp) and global mean pooling (gap) | ||
| x = torch.cat([gmp(x, batch), gap(x, batch)], dim=1) | ||
| x = self.relu(self.fc_g1(x)) | ||
| x = self.dropout(x) | ||
| x = self.fc_g2(x) | ||
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| embedded_xt = self.embedding_xt(target) | ||
| conv_xt = self.conv_xt_1(embedded_xt) | ||
| # Flatten | ||
| xt = conv_xt.view(-1, 32 * 121) | ||
| xt = self.fc1_xt(xt) | ||
| # Concat | ||
| xc = torch.cat((x, xt), 1) | ||
| # Add some dense layers | ||
| xc = self.fc1(xc) | ||
| xc = self.relu(xc) | ||
| xc = self.dropout(xc) | ||
| xc = self.fc2(xc) | ||
| xc = self.relu(xc) | ||
| xc = self.dropout(xc) | ||
| out = self.out(xc) | ||
| return out |
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