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import torch
from torch import nn
import torch.nn.functional as F
# some predefined parameters
elem_list = ['C', 'N', 'O', 'S', 'F', 'Si', 'P', 'Cl', 'Br', 'Mg', 'Na', 'Ca', 'Fe', 'As', 'Al', 'I', 'B', 'V', 'K',
'Tl', 'Yb', 'Sb', 'Sn', 'Ag', 'Pd', 'Co', 'Se', 'Ti', 'Zn', 'H', 'Li', 'Ge', 'Cu', 'Au', 'Ni', 'Cd', 'In',
'Mn', 'Zr', 'Cr', 'Pt', 'Hg', 'Pb', 'W', 'Ru', 'Nb', 'Re', 'Te', 'Rh', 'Tc', 'Ba', 'Bi', 'Hf', 'Mo', 'U',
'Sm', 'Os', 'Ir', 'Ce', 'Gd', 'Ga', 'Cs', 'unknown']
atom_fdim = len(elem_list) + 6 + 6 + 6 + 1
bond_fdim = 6
max_nb = 6
class MONN(nn.Module):
# init_A, init_B, init_W = loading_emb(measure)
# net = Net(init_A, init_B, init_W, params)
def __init__(self, init_atom_features, init_bond_features, init_word_features, params):
super().__init__()
self.init_atom_features = init_atom_features
self.init_bond_features = init_bond_features
self.init_word_features = init_word_features
"""hyper part"""
GNN_depth, inner_CNN_depth, DMA_depth, k_head, kernel_size, hidden_size1, hidden_size2 = params
self.GNN_depth = GNN_depth
self.inner_CNN_depth = inner_CNN_depth
self.DMA_depth = DMA_depth
self.k_head = k_head
self.kernel_size = kernel_size
self.hidden_size1 = hidden_size1
self.hidden_size2 = hidden_size2
"""GraphConv Module"""
self.vertex_embedding = nn.Linear(atom_fdim,
self.hidden_size1) # first transform vertex features into hidden representations
# GWM parameters
self.W_a_main = nn.ModuleList(
[nn.ModuleList([nn.Linear(self.hidden_size1, self.hidden_size1) for i in range(self.k_head)]) for i in
range(self.GNN_depth)])
self.W_a_super = nn.ModuleList(
[nn.ModuleList([nn.Linear(self.hidden_size1, self.hidden_size1) for i in range(self.k_head)]) for i in
range(self.GNN_depth)])
self.W_main = nn.ModuleList(
[nn.ModuleList([nn.Linear(self.hidden_size1, self.hidden_size1) for i in range(self.k_head)]) for i in
range(self.GNN_depth)])
self.W_bmm = nn.ModuleList(
[nn.ModuleList([nn.Linear(self.hidden_size1, 1) for i in range(self.k_head)]) for i in
range(self.GNN_depth)])
self.W_super = nn.ModuleList([nn.Linear(self.hidden_size1, self.hidden_size1) for i in range(self.GNN_depth)])
self.W_main_to_super = nn.ModuleList(
[nn.Linear(self.hidden_size1 * self.k_head, self.hidden_size1) for i in range(self.GNN_depth)])
self.W_super_to_main = nn.ModuleList(
[nn.Linear(self.hidden_size1, self.hidden_size1) for i in range(self.GNN_depth)])
self.W_zm1 = nn.ModuleList([nn.Linear(self.hidden_size1, self.hidden_size1) for i in range(self.GNN_depth)])
self.W_zm2 = nn.ModuleList([nn.Linear(self.hidden_size1, self.hidden_size1) for i in range(self.GNN_depth)])
self.W_zs1 = nn.ModuleList([nn.Linear(self.hidden_size1, self.hidden_size1) for i in range(self.GNN_depth)])
self.W_zs2 = nn.ModuleList([nn.Linear(self.hidden_size1, self.hidden_size1) for i in range(self.GNN_depth)])
self.GRU_main = nn.GRUCell(self.hidden_size1, self.hidden_size1)
self.GRU_super = nn.GRUCell(self.hidden_size1, self.hidden_size1)
# WLN parameters
self.label_U2 = nn.ModuleList([nn.Linear(self.hidden_size1 + bond_fdim, self.hidden_size1) for i in
range(self.GNN_depth)]) # assume no edge feature transformation
self.label_U1 = nn.ModuleList(
[nn.Linear(self.hidden_size1 * 2, self.hidden_size1) for i in range(self.GNN_depth)])
"""CNN-RNN Module"""
# CNN parameters
self.embed_seq = nn.Embedding(len(self.init_word_features), 20, padding_idx=0)
self.embed_seq.weight = nn.Parameter(self.init_word_features)
self.embed_seq.weight.requires_grad = False
self.conv_first = nn.Conv1d(20, self.hidden_size1, kernel_size=self.kernel_size,
padding=(self.kernel_size - 1) / 2)
self.conv_last = nn.Conv1d(self.hidden_size1, self.hidden_size1, kernel_size=self.kernel_size,
padding=(self.kernel_size - 1) / 2)
self.plain_CNN = nn.ModuleList([])
for i in range(self.inner_CNN_depth):
self.plain_CNN.append(nn.Conv1d(self.hidden_size1, self.hidden_size1, kernel_size=self.kernel_size,
padding=(self.kernel_size - 1) / 2))
"""Affinity Prediction Module"""
self.super_final = nn.Linear(self.hidden_size1, self.hidden_size2)
self.c_final = nn.Linear(self.hidden_size1, self.hidden_size2)
self.p_final = nn.Linear(self.hidden_size1, self.hidden_size2)
# DMA parameters
self.mc0 = nn.Linear(hidden_size2, hidden_size2)
self.mp0 = nn.Linear(hidden_size2, hidden_size2)
self.mc1 = nn.ModuleList([nn.Linear(self.hidden_size2, self.hidden_size2) for i in range(self.DMA_depth)])
self.mp1 = nn.ModuleList([nn.Linear(self.hidden_size2, self.hidden_size2) for i in range(self.DMA_depth)])
self.hc0 = nn.ModuleList([nn.Linear(self.hidden_size2, self.hidden_size2) for i in range(self.DMA_depth)])
self.hp0 = nn.ModuleList([nn.Linear(self.hidden_size2, self.hidden_size2) for i in range(self.DMA_depth)])
self.hc1 = nn.ModuleList([nn.Linear(self.hidden_size2, 1) for i in range(self.DMA_depth)])
self.hp1 = nn.ModuleList([nn.Linear(self.hidden_size2, 1) for i in range(self.DMA_depth)])
self.c_to_p_transform = nn.ModuleList(
[nn.Linear(self.hidden_size2, self.hidden_size2) for i in range(self.DMA_depth)])
self.p_to_c_transform = nn.ModuleList(
[nn.Linear(self.hidden_size2, self.hidden_size2) for i in range(self.DMA_depth)])
self.GRU_dma = nn.GRUCell(self.hidden_size2, self.hidden_size2)
# Output layer
self.W_out = nn.Linear(self.hidden_size2 * self.hidden_size2 * 2, 1)
"""Pairwise Interaction Prediction Module"""
self.pairwise_compound = nn.Linear(self.hidden_size1, self.hidden_size1)
self.pairwise_protein = nn.Linear(self.hidden_size1, self.hidden_size1)
def mask_softmax(self, a, mask, dim=-1):
a_max = torch.max(a, dim, keepdim=True)[0]
a_exp = torch.exp(a - a_max)
a_exp = a_exp * mask
a_softmax = a_exp / (torch.sum(a_exp, dim, keepdim=True) + 1e-6)
return a_softmax
def graph_conv_module(self, batch_size, vertex_mask, vertex, edge, atom_adj, bond_adj, nbs_mask):
n_vertex = vertex_mask.size(1)
# initial features
vertex_initial = torch.index_select(self.init_atom_features, 0, vertex.view(-1))
vertex_initial = vertex_initial.view(batch_size, -1, atom_fdim)
edge_initial = torch.index_select(self.init_bond_features, 0, edge.view(-1))
edge_initial = edge_initial.view(batch_size, -1, bond_fdim)
vertex_feature = F.leaky_relu(self.vertex_embedding(vertex_initial), 0.1)
super_feature = torch.sum(vertex_feature * vertex_mask.view(batch_size, -1, 1), dim=1, keepdim=True)
for GWM_iter in range(self.GNN_depth):
# prepare main node features
for k in range(self.k_head):
a_main = torch.tanh(self.W_a_main[GWM_iter][k](vertex_feature))
a_super = torch.tanh(self.W_a_super[GWM_iter][k](super_feature))
a = self.W_bmm[GWM_iter][k](a_main * super_feature)
attn = self.mask_softmax(a.view(batch_size, -1), vertex_mask).view(batch_size, -1, 1)
k_main_to_super = torch.bmm(attn.transpose(1, 2), self.W_main[GWM_iter][k](vertex_feature))
if k == 0:
m_main_to_super = k_main_to_super
else:
m_main_to_super = torch.cat([m_main_to_super, k_main_to_super], dim=-1) # concat k-head
main_to_super = torch.tanh(self.W_main_to_super[GWM_iter](m_main_to_super))
main_self = self.wln_unit(batch_size, vertex_mask, vertex_feature, edge_initial, atom_adj, bond_adj,
nbs_mask, GWM_iter)
super_to_main = torch.tanh(self.W_super_to_main[GWM_iter](super_feature))
super_self = torch.tanh(self.W_super[GWM_iter](super_feature))
# warp gate and GRU for update main node features, use main_self and super_to_main
z_main = torch.sigmoid(self.W_zm1[GWM_iter](main_self) + self.W_zm2[GWM_iter](super_to_main))
hidden_main = (1 - z_main) * main_self + z_main * super_to_main
vertex_feature = self.GRU_main(hidden_main.view(-1, self.hidden_size1),
vertex_feature.view(-1, self.hidden_size1))
vertex_feature = vertex_feature.view(batch_size, n_vertex, self.hidden_size1)
# warp gate and GRU for update super node features
z_supper = torch.sigmoid(self.W_zs1[GWM_iter](super_self) + self.W_zs2[GWM_iter](main_to_super))
hidden_super = (1 - z_supper) * super_self + z_supper * main_to_super
super_feature = self.GRU_super(hidden_super.view(batch_size, self.hidden_size1),
super_feature.view(batch_size, self.hidden_size1))
super_feature = super_feature.view(batch_size, 1, self.hidden_size1)
return vertex_feature, super_feature
def wln_unit(self, batch_size, vertex_mask, vertex_features, edge_initial, atom_adj, bond_adj, nbs_mask, GNN_iter):
n_vertex = vertex_mask.size(1)
n_nbs = nbs_mask.size(2)
vertex_mask = vertex_mask.view(batch_size, n_vertex, 1)
nbs_mask = nbs_mask.view(batch_size, n_vertex, n_nbs, 1)
vertex_nei = torch.index_select(vertex_features.view(-1, self.hidden_size1), 0, atom_adj).view(batch_size,
n_vertex, n_nbs,
self.hidden_size1)
edge_nei = torch.index_select(edge_initial.view(-1, bond_fdim), 0, bond_adj).view(batch_size, n_vertex, n_nbs,
bond_fdim)
# Weisfeiler Lehman relabelling
l_nei = torch.cat((vertex_nei, edge_nei), -1)
nei_label = F.leaky_relu(self.label_U2[GNN_iter](l_nei), 0.1)
nei_label = torch.sum(nei_label * nbs_mask, dim=-2)
new_label = torch.cat((vertex_features, nei_label), 2)
new_label = self.label_U1[GNN_iter](new_label)
vertex_features = F.leaky_relu(new_label, 0.1)
return vertex_features
def cnn_module(self, sequence):
ebd = self.embed_seq(sequence)
ebd = ebd.transpose(1, 2)
x = F.leaky_relu(self.conv_first(ebd), 0.1)
for i in range(self.inner_CNN_depth):
x = self.plain_CNN[i](x)
x = F.leaky_relu(x, 0.1)
x = F.leaky_relu(self.conv_last(x), 0.1)
H = x.transpose(1, 2)
# H, hidden = self.rnn(H)
return H
def pairwise_pred_module(self, batch_size, comp_feature, prot_feature, vertex_mask, seq_mask):
pairwise_c_feature = F.leaky_relu(self.pairwise_compound(comp_feature), 0.1)
pairwise_p_feature = F.leaky_relu(self.pairwise_protein(prot_feature), 0.1)
pairwise_pred = torch.matmul(pairwise_c_feature, pairwise_p_feature.transpose(1, 2))
# TODO: difference between the pairwise_mask here and in the data?
pairwise_mask = torch.matmul(vertex_mask.view(batch_size, -1, 1), seq_mask.view(batch_size, 1, -1))
pairwise_pred = pairwise_pred * pairwise_mask
return pairwise_pred
def affinity_pred_module(self, batch_size, comp_feature, prot_feature, super_feature, vertex_mask, seq_mask,
pairwise_pred):
comp_feature = F.leaky_relu(self.c_final(comp_feature), 0.1)
prot_feature = F.leaky_relu(self.p_final(prot_feature), 0.1)
super_feature = F.leaky_relu(self.super_final(super_feature.view(batch_size, -1)), 0.1)
cf, pf = self.dma_gru(batch_size, comp_feature, vertex_mask, prot_feature, seq_mask, pairwise_pred)
cf = torch.cat([cf.view(batch_size, -1), super_feature.view(batch_size, -1)], dim=1)
kroneck = F.leaky_relu(
torch.matmul(cf.view(batch_size, -1, 1), pf.view(batch_size, 1, -1)).view(batch_size, -1), 0.1)
affinity_pred = self.W_out(kroneck)
return affinity_pred
def dma_gru(self, batch_size, comp_feats, vertex_mask, prot_feats, seq_mask, pairwise_pred):
vertex_mask = vertex_mask.view(batch_size, -1, 1)
seq_mask = seq_mask.view(batch_size, -1, 1)
cf = torch.Tensor()
pf = torch.Tensor()
c0 = torch.sum(comp_feats * vertex_mask, dim=1) / torch.sum(vertex_mask, dim=1)
p0 = torch.sum(prot_feats * seq_mask, dim=1) / torch.sum(seq_mask, dim=1)
m = c0 * p0
for DMA_iter in range(self.DMA_depth):
c_to_p = torch.matmul(pairwise_pred.transpose(1, 2),
F.tanh(self.c_to_p_transform[DMA_iter](comp_feats))) # batch * n_residue * hidden
p_to_c = torch.matmul(pairwise_pred,
F.tanh(self.p_to_c_transform[DMA_iter](prot_feats))) # batch * n_vertex * hidden
c_tmp = F.tanh(self.hc0[DMA_iter](comp_feats)) * F.tanh(self.mc1[DMA_iter](m)).view(batch_size, 1,
-1) * p_to_c
p_tmp = F.tanh(self.hp0[DMA_iter](prot_feats)) * F.tanh(self.mp1[DMA_iter](m)).view(batch_size, 1,
-1) * c_to_p
c_att = self.mask_softmax(self.hc1[DMA_iter](c_tmp).view(batch_size, -1), vertex_mask.view(batch_size, -1))
p_att = self.mask_softmax(self.hp1[DMA_iter](p_tmp).view(batch_size, -1), seq_mask.view(batch_size, -1))
cf = torch.sum(comp_feats * c_att.view(batch_size, -1, 1), dim=1)
pf = torch.sum(prot_feats * p_att.view(batch_size, -1, 1), dim=1)
m = self.GRU_dma(m, cf * pf)
return cf, pf
def forward(self, enc_drug, enc_protein):
vertex_mask, vertex, edge, atom_adj, bond_adj, nbs_mask = enc_drug
vertex, vertex_mask = vertex
edge, _ = edge
atom_adj, _ = atom_adj
bond_adj, _ = bond_adj
nbs_mask, _ = enc_drug
seq_mask, sequence = enc_protein
batch_size = vertex.size(0)
atom_feature, super_feature = self.graph_conv_module(batch_size, vertex_mask, vertex, edge, atom_adj, bond_adj,
nbs_mask)
prot_feature = self.cnn_module(sequence)
pairwise_pred = self.pairwise_pred_module(batch_size, atom_feature, prot_feature, vertex_mask, seq_mask)
affinity_pred = self.affinity_pred_module(batch_size, atom_feature, prot_feature, super_feature, vertex_mask,
seq_mask, pairwise_pred)
return affinity_pred # , pairwise_pred
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