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model.py
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import torch
import torch.nn as nn
import torch.nn.functional as F
from torch_geometric.nn import GCNConv, MessagePassing, global_max_pool
from torch_geometric.utils import add_self_loops, degree
from tan import TAN
# from torch.nn.utils.weight_norm import weight_norm
class PROTAC_STAN(nn.Module):
def __init__(self, cfg):
super(PROTAC_STAN, self).__init__()
self.protac_encoder = MolecularEncoder(
num_mol_features=cfg['protac']['feature'],
embedding_dim=cfg['protac']['embed'],
hidden_channels=cfg['protac']['hidden'],
edge_dim=cfg['protac']['edge_dim']
)
self.e3_ligase_encoder = ProteinEncoder(
embedding_dim=cfg['protein']['embed'],
hidden=cfg['protein']['hidden'],
out_dim=cfg['protein']['out_dim'],
)
self.poi_encoder = ProteinEncoder(
embedding_dim=cfg['protein']['embed'],
hidden=cfg['protein']['hidden'],
out_dim=cfg['protein']['out_dim'],
)
self.tan = TAN(cfg['tan']['in_dims'], cfg['clf']['embed'], cfg['tan']['heads'])
self.mlp = nn.Sequential(
nn.Linear(cfg['clf']['embed'], cfg['clf']['hidden']),
nn.BatchNorm1d(cfg['clf']['hidden']),
nn.ReLU(),
nn.Linear(cfg['clf']['hidden'], cfg['clf']['class']),
)
def forward(self, protac, e3_ligase, poi, mode='train'):
protac_embedding = self.protac_encoder(protac)
e3_ligase_embedding = self.e3_ligase_encoder(e3_ligase)
poi_embedding = self.poi_encoder(poi)
atts = None
joint_embedding, atts = self.tan(
protac_embedding.unsqueeze(2),
e3_ligase_embedding.unsqueeze(2),
poi_embedding.unsqueeze(2),
)
output = self.mlp(joint_embedding)
pred = F.log_softmax(output, dim=1)
if mode == 'train':
return pred
elif mode == 'eval':
return pred, atts
else:
raise ValueError(f'Unknown mode: {mode}')
class EdgedGCNConv(MessagePassing):
def __init__(self, in_channels, out_channels, edge_dim):
super(EdgedGCNConv, self).__init__(aggr='add')
self.node_lin = torch.nn.Linear(in_channels, out_channels, bias=False)
self.edge_lin = torch.nn.Linear(edge_dim, out_channels, bias=False)
self.bias = torch.nn.Parameter(torch.zeros(out_channels))
def forward(self, x, edge_index, edge_attr):
edge_index, _ = add_self_loops(edge_index, num_nodes=x.size(0))
self_loop_attr = torch.zeros((x.size(0), edge_attr.size(1)), dtype=edge_attr.dtype, device=edge_attr.device)
edge_attr = torch.cat((edge_attr, self_loop_attr), dim=0)
x = self.node_lin(x)
edge_attr = self.edge_lin(edge_attr)
row, col = edge_index
deg = degree(col, x.size(0), dtype=x.dtype)
deg_inv_sqrt = deg.pow(-0.5)
norm = deg_inv_sqrt[row] * deg_inv_sqrt[col]
out = self.propagate(edge_index, size=(x.size(0), x.size(0)), x=x, edge_attr=edge_attr, norm=norm)
out += self.bias
return out
def message(self, x_j, edge_attr, norm):
return norm.view(-1, 1) * (x_j + edge_attr)
def __repr__(self):
return '{}(\n\t(node_lin): {}\n\t(edge_lin): {}\n)'.format(
self.__class__.__name__,
self.node_lin,
self.edge_lin,
)
class MolecularEncoder(nn.Module):
def __init__(self, num_mol_features, embedding_dim, hidden_channels, edge_dim):
super(MolecularEncoder, self).__init__()
self.lin = nn.Linear(num_mol_features, embedding_dim)
self.bn = nn.BatchNorm1d(embedding_dim)
self.conv1 = EdgedGCNConv(embedding_dim, hidden_channels, edge_dim)
self.conv2 = EdgedGCNConv(hidden_channels, embedding_dim, edge_dim)
def forward(self, data):
x, edge_index, edge_attr, batch = data.x, data.edge_index, data.edge_attr, data.batch
x = self.lin(x)
x = self.bn(x)
x = F.relu(x)
x = self.conv1(x, edge_index, edge_attr)
x = F.relu(x)
x = self.conv2(x, edge_index, edge_attr)
x = global_max_pool(x, batch)
return x
class ProteinEncoder(nn.Module):
def __init__(self, embedding_dim, hidden, out_dim):
super(ProteinEncoder, self).__init__()
self.adapter = nn.Linear(embedding_dim, hidden)
self.fc = nn.Linear(hidden, out_dim)
def forward(self, x):
x = self.adapter(x)
x = F.relu(x)
x = self.fc(x)
return x