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import torch
import torch.nn as nn
from UltraFlow import layers, losses
class IGN_basic(nn.Module):
def __init__(self,config):
super(IGN_basic, self).__init__()
self.config = config
self.pretrain_assay_mlp_share = config.train.pretrain_assay_mlp_share
self.pretrain_use_assay_description = config.train.pretrain_use_assay_description
self.graph_conv = layers.ModifiedAttentiveFPGNNV2(config.model.lig_node_dim, config.model.lig_edge_dim, config.model.num_layers, config.model.hidden_dim, config.model.dropout, config.model.jk)
if config.model.jk == 'concat':
self.noncov_graph = layers.DTIConvGraph3Layer_IGN_basic(config.model.hidden_dim * config.model.num_layers + config.model.inter_edge_dim, config.model.inter_out_dim, config.model.dropout)
else:
self.noncov_graph = layers.DTIConvGraph3Layer_IGN_basic(config.model.hidden_dim + config.model.inter_edge_dim, config.model.inter_out_dim, config.model.dropout)
if config.model.readout.startswith('multi_head') and config.model.attn_merge=='concat':
self.FC = layers.FC(config.model.inter_out_dim * (config.model.num_head + 1), config.model.fc_hidden_dim, config.model.dropout, config.model.out_dim)
else:
self.FC = layers.FC(config.model.inter_out_dim * 2, config.model.fc_hidden_dim, config.model.dropout, config.model.out_dim)
self.readout = layers.ReadsOutLayer(config.model.inter_out_dim, config.model.readout, config.model.num_head, config.model.attn_merge)
self.softmax = nn.Softmax(dim=1)
if self.pretrain_use_assay_description:
print(f'use assay descrption type: {config.data.assay_des_type}')
if self.pretrain_assay_mlp_share:
self.assay_info_aggre_mlp = layers.FC(config.data.assay_des_dim, config.model.assay_des_fc_hidden_dim,
config.model.dropout, config.model.inter_out_dim * 2)
else:
self.assay_info_aggre_mlp_pointwise = layers.FC(config.data.assay_des_dim, config.model.assay_des_fc_hidden_dim,
config.model.dropout, config.model.inter_out_dim * 2)
self.assay_info_aggre_mlp_pairwise = layers.FC(config.data.assay_des_dim, config.model.assay_des_fc_hidden_dim,
config.model.dropout, config.model.inter_out_dim * 2)
def forward(self, batch):
bg_lig, bg_prot, bg_inter, labels, _, ass_des = batch
node_feats_lig = self.graph_conv(bg_lig)
node_feats_prot = self.graph_conv(bg_prot)
bg_inter.ndata['h'] = self.alignfeature(bg_lig,bg_prot,node_feats_lig,node_feats_prot)
bond_feats_inter = self.noncov_graph(bg_inter)
graph_embedding = self.readout(bg_inter, bond_feats_inter)
if self.pretrain_use_assay_description:
if self.pretrain_assay_mlp_share:
ranking_assay_embedding = self.assay_info_aggre_mlp(ass_des)
affinity_pred = self.FC(graph_embedding + ranking_assay_embedding)
else:
regression_assay_embedding = self.assay_info_aggre_mlp_pointwise(ass_des)
affinity_pred = self.FC(graph_embedding + regression_assay_embedding)
ranking_assay_embedding = self.assay_info_aggre_mlp_pairwise(ass_des)
else:
affinity_pred = self.FC(graph_embedding)
ranking_assay_embedding = torch.zeros(len(affinity_pred))
return affinity_pred, graph_embedding, ranking_assay_embedding
def alignfeature(self,bg_lig,bg_prot,node_feats_lig,node_feats_prot):
inter_feature = torch.cat((node_feats_lig,node_feats_prot))
lig_num,prot_num = bg_lig.batch_num_nodes(),bg_prot.batch_num_nodes()
lig_start, prot_start = lig_num.cumsum(0) - lig_num, prot_num.cumsum(0) - prot_num
inter_start = lig_start + prot_start
for i in range(lig_num.shape[0]):
inter_feature[inter_start[i]:inter_start[i]+lig_num[i]] = node_feats_lig[lig_start[i]:lig_start[i]+lig_num[i]]
inter_feature[inter_start[i]+lig_num[i]:inter_start[i]+lig_num[i]+prot_num[i]] = node_feats_prot[prot_start[i]:prot_start[i]+prot_num[i]]
return inter_feature
class IGN(nn.Module):
def __init__(self,config):
super(IGN, self).__init__()
self.config = config
self.pretrain_assay_mlp_share = config.train.pretrain_assay_mlp_share
self.pretrain_use_assay_description = config.train.pretrain_use_assay_description
self.ligand_conv = layers.ModifiedAttentiveFPGNNV2(config.model.lig_node_dim, config.model.lig_edge_dim, config.model.num_layers, config.model.hidden_dim, config.model.dropout, config.model.jk)
self.protein_conv = layers.ModifiedAttentiveFPGNNV2(config.model.pro_node_dim, config.model.pro_edge_dim, config.model.num_layers, config.model.hidden_dim, config.model.dropout, config.model.jk)
if config.model.jk == 'concat':
self.noncov_graph = layers.DTIConvGraph3Layer(config.model.hidden_dim * (config.model.num_layers + config.model.num_layers) + config.model.inter_edge_dim, config.model.inter_out_dim, config.model.dropout)
else:
self.noncov_graph = layers.DTIConvGraph3Layer(config.model.hidden_dim * 2 + config.model.inter_edge_dim, config.model.inter_out_dim, config.model.dropout)
if config.model.readout.startswith('multi_head') and config.model.attn_merge=='concat':
self.FC = layers.FC(config.model.inter_out_dim * (config.model.num_head + 1), config.model.fc_hidden_dim, config.model.dropout, config.model.out_dim)
else:
self.FC = layers.FC(config.model.inter_out_dim * 2, config.model.fc_hidden_dim, config.model.dropout, config.model.out_dim)
self.readout = layers.ReadsOutLayer(config.model.inter_out_dim, config.model.readout, config.model.num_head, config.model.attn_merge)
self.softmax = nn.Softmax(dim=1)
if self.pretrain_use_assay_description:
print(f'use assay descrption type: {config.data.assay_des_type}')
if self.pretrain_assay_mlp_share:
self.assay_info_aggre_mlp = layers.FC(config.data.assay_des_dim, config.model.assay_des_fc_hidden_dim,
config.model.dropout, config.model.inter_out_dim * 2)
else:
self.assay_info_aggre_mlp_pointwise = layers.FC(config.data.assay_des_dim, config.model.assay_des_fc_hidden_dim,
config.model.dropout, config.model.inter_out_dim * 2)
self.assay_info_aggre_mlp_pairwise = layers.FC(config.data.assay_des_dim, config.model.assay_des_fc_hidden_dim,
config.model.dropout, config.model.inter_out_dim * 2)
def forward(self, batch):
bg_lig, bg_prot, bg_inter, labels, _, ass_des = batch
node_feats_lig = self.ligand_conv(bg_lig)
node_feats_prot = self.protein_conv(bg_prot)
bg_inter.ndata['h'] = self.alignfeature(bg_lig,bg_prot,node_feats_lig,node_feats_prot)
bond_feats_inter = self.noncov_graph(bg_inter)
graph_embedding = self.readout(bg_inter, bond_feats_inter)
if self.pretrain_use_assay_description:
if self.pretrain_assay_mlp_share:
ranking_assay_embedding = self.assay_info_aggre_mlp(ass_des)
affinity_pred = self.FC(graph_embedding + ranking_assay_embedding)
else:
regression_assay_embedding = self.assay_info_aggre_mlp_pointwise(ass_des)
affinity_pred = self.FC(graph_embedding + regression_assay_embedding)
ranking_assay_embedding = self.assay_info_aggre_mlp_pairwise(ass_des)
else:
affinity_pred = self.FC(graph_embedding)
ranking_assay_embedding = torch.zeros(len(affinity_pred))
return affinity_pred, graph_embedding, ranking_assay_embedding
def alignfeature(self,bg_lig,bg_prot,node_feats_lig,node_feats_prot):
inter_feature = torch.cat((node_feats_lig,node_feats_prot))
lig_num,prot_num = bg_lig.batch_num_nodes(),bg_prot.batch_num_nodes()
lig_start, prot_start = lig_num.cumsum(0) - lig_num, prot_num.cumsum(0) - prot_num
inter_start = lig_start + prot_start
for i in range(lig_num.shape[0]):
inter_feature[inter_start[i]:inter_start[i]+lig_num[i]] = node_feats_lig[lig_start[i]:lig_start[i]+lig_num[i]]
inter_feature[inter_start[i]+lig_num[i]:inter_start[i]+lig_num[i]+prot_num[i]] = node_feats_prot[prot_start[i]:prot_start[i]+prot_num[i]]
return inter_feature
class GNNs(nn.Module):
def __init__(self, nLigNode, nLigEdge, nLayer, nHid, JK, GNN):
super(GNNs, self).__init__()
if GNN == 'GCN':
self.Encoder = layers.GCN(nLigNode, hidden_feats=[nHid] * nLayer)
elif GNN == 'GAT':
self.Encoder = layers.GAT(nLigNode, hidden_feats=[nHid] * nLayer)
elif GNN == 'GIN':
self.Encoder = layers.GIN(nLigNode, nHid, nLayer, num_mlp_layers=2, dropout=0.1, learn_eps=False,
neighbor_pooling_type='sum', JK=JK)
elif GNN == 'EGNN':
self.Encoder = layers.EGNN(nLigNode, nLigEdge, nHid, nLayer, dropout=0.1, JK=JK)
elif GNN == 'AttentiveFP':
self.Encoder = layers.ModifiedAttentiveFPGNNV2(nLigNode, nLigEdge, nLayer, nHid, 0.1, JK)
def forward(self, Graph, Perturb=None):
Node_Rep = self.Encoder(Graph, Perturb)
return Node_Rep
class Affinity_GNNs(nn.Module):
def __init__(self, config):
super(Affinity_GNNs, self).__init__()
lig_node_dim = config.model.lig_node_dim
lig_edge_dim = config.model.lig_edge_dim
pro_node_dim = config.model.pro_node_dim
pro_edge_dim = config.model.pro_edge_dim
layer_num = config.model.num_layers
hidden_dim = config.model.hidden_dim
jk = config.model.jk
GNN = config.model.GNN_type
self.pretrain_assay_mlp_share = config.train.pretrain_assay_mlp_share
self.pretrain_use_assay_description = config.train.pretrain_use_assay_description
self.lig_encoder = GNNs(lig_node_dim, lig_edge_dim, layer_num, hidden_dim, jk, GNN)
self.pro_encoder = GNNs(pro_node_dim, pro_edge_dim, layer_num, hidden_dim, jk, GNN)
if config.model.jk == 'concat':
self.noncov_graph = layers.DTIConvGraph3Layer(hidden_dim * (layer_num + layer_num) + config.model.inter_edge_dim, config.model.inter_out_dim, config.model.dropout)
else:
self.noncov_graph = layers.DTIConvGraph3Layer(hidden_dim * 2 + config.model.inter_edge_dim, config.model.inter_out_dim, config.model.dropout)
self.readout = layers.ReadsOutLayer(config.model.inter_out_dim, config.model.readout, config.model.num_head, config.model.attn_merge)
if config.model.readout.startswith('multi_head') and config.model.attn_merge=='concat':
self.FC = layers.FC(config.model.inter_out_dim * (config.model.num_head + 1), config.model.fc_hidden_dim, config.model.dropout, config.model.out_dim)
else:
self.FC = layers.FC(config.model.inter_out_dim * 2, config.model.fc_hidden_dim, config.model.dropout, config.model.out_dim)
self.softmax = nn.Softmax(dim=1)
if self.pretrain_use_assay_description:
print(f'use assay descrption type: {config.data.assay_des_type}')
if self.pretrain_assay_mlp_share:
self.assay_info_aggre_mlp = layers.FC(config.data.assay_des_dim, config.model.assay_des_fc_hidden_dim,
config.model.dropout, config.model.inter_out_dim * 2)
else:
self.assay_info_aggre_mlp_pointwise = layers.FC(config.data.assay_des_dim, config.model.assay_des_fc_hidden_dim,
config.model.dropout, config.model.inter_out_dim * 2)
self.assay_info_aggre_mlp_pairwise = layers.FC(config.data.assay_des_dim, config.model.assay_des_fc_hidden_dim,
config.model.dropout, config.model.inter_out_dim * 2)
def forward(self, batch):
bg_lig, bg_prot, bg_inter, labels, _, ass_des = batch
node_feats_lig = self.lig_encoder(bg_lig)
node_feats_prot = self.pro_encoder(bg_prot)
bg_inter.ndata['h'] = self.alignfeature(bg_lig,bg_prot,node_feats_lig,node_feats_prot)
bond_feats_inter = self.noncov_graph(bg_inter)
graph_embedding = self.readout(bg_inter, bond_feats_inter)
if self.pretrain_use_assay_description:
if self.pretrain_assay_mlp_share:
ranking_assay_embedding = self.assay_info_aggre_mlp(ass_des)
affinity_pred = self.FC(graph_embedding + ranking_assay_embedding)
else:
regression_assay_embedding = self.assay_info_aggre_mlp_pointwise(ass_des)
affinity_pred = self.FC(graph_embedding + regression_assay_embedding)
ranking_assay_embedding = self.assay_info_aggre_mlp_pairwise(ass_des)
else:
affinity_pred = self.FC(graph_embedding)
ranking_assay_embedding = torch.zeros(len(affinity_pred))
return affinity_pred, graph_embedding, ranking_assay_embedding
def alignfeature(self,bg_lig,bg_prot,node_feats_lig,node_feats_prot):
inter_feature = torch.cat((node_feats_lig,node_feats_prot))
lig_num,prot_num = bg_lig.batch_num_nodes(),bg_prot.batch_num_nodes()
lig_start, prot_start = lig_num.cumsum(0) - lig_num, prot_num.cumsum(0) - prot_num
inter_start = lig_start + prot_start
for i in range(lig_num.shape[0]):
inter_feature[inter_start[i]:inter_start[i]+lig_num[i]] = node_feats_lig[lig_start[i]:lig_start[i]+lig_num[i]]
inter_feature[inter_start[i]+lig_num[i]:inter_start[i]+lig_num[i]+prot_num[i]] = node_feats_prot[prot_start[i]:prot_start[i]+prot_num[i]]
return inter_feature
class affinity_head(nn.Module):
def __init__(self, config):
super(affinity_head, self).__init__()
self.pretrain_assay_mlp_share = config.train.pretrain_assay_mlp_share
self.pretrain_use_assay_description = config.train.pretrain_use_assay_description
if self.pretrain_use_assay_description:
print(f'use assay descrption type: {config.data.assay_des_type}')
if self.pretrain_assay_mlp_share:
self.assay_info_aggre_mlp = layers.FC(config.data.assay_des_dim, config.model.assay_des_fc_hidden_dim,
config.model.dropout, config.model.inter_out_dim * 2)
else:
self.assay_info_aggre_mlp_pointwise = layers.FC(config.data.assay_des_dim, config.model.assay_des_fc_hidden_dim,
config.model.dropout, config.model.inter_out_dim * 2)
self.assay_info_aggre_mlp_pairwise = layers.FC(config.data.assay_des_dim, config.model.assay_des_fc_hidden_dim,
config.model.dropout, config.model.inter_out_dim * 2)
if config.model.readout.startswith('multi_head') and config.model.attn_merge=='concat':
self.FC = layers.FC(config.model.inter_out_dim * (config.model.num_head + 1), config.model.fintune_fc_hidden_dim, config.model.dropout, config.model.out_dim)
else:
self.FC = layers.FC(config.model.inter_out_dim * 2, config.model.fintune_fc_hidden_dim, config.model.dropout, config.model.out_dim)
def forward(self, graph_embedding, ass_des):
if self.pretrain_use_assay_description:
if self.pretrain_assay_mlp_share:
ranking_assay_embedding = self.assay_info_aggre_mlp(ass_des)
affinity_pred = self.FC(graph_embedding + ranking_assay_embedding)
else:
regression_assay_embedding = self.assay_info_aggre_mlp_pointwise(ass_des)
affinity_pred = self.FC(graph_embedding + regression_assay_embedding)
ranking_assay_embedding = self.assay_info_aggre_mlp_pairwise(ass_des)
else:
affinity_pred = self.FC(graph_embedding)
ranking_assay_embedding = torch.zeros(len(affinity_pred))
return affinity_pred
class ASRP_head(nn.Module):
def __init__(self, config):
super(ASRP_head, self).__init__()
self.readout = layers.ReadsOutLayer(config.model.inter_out_dim, config.model.readout, config.model.num_head, config.model.attn_merge)
self.pretrain_assay_mlp_share = config.train.pretrain_assay_mlp_share
self.pretrain_use_assay_description = config.train.pretrain_use_assay_description
if self.pretrain_use_assay_description:
print(f'use assay descrption type: {config.data.assay_des_type}')
if self.pretrain_assay_mlp_share:
self.assay_info_aggre_mlp = layers.FC(config.data.assay_des_dim, config.model.assay_des_fc_hidden_dim,
config.model.dropout, config.model.inter_out_dim * 2)
else:
self.assay_info_aggre_mlp_pointwise = layers.FC(config.data.assay_des_dim, config.model.assay_des_fc_hidden_dim,
config.model.dropout, config.model.inter_out_dim * 2)
self.assay_info_aggre_mlp_pairwise = layers.FC(config.data.assay_des_dim, config.model.assay_des_fc_hidden_dim,
config.model.dropout, config.model.inter_out_dim * 2)
if config.model.readout.startswith('multi_head') and config.model.attn_merge=='concat':
self.FC = layers.FC(config.model.inter_out_dim * (config.model.num_head + 1), config.model.fintune_fc_hidden_dim, config.model.dropout, config.model.out_dim)
else:
self.FC = layers.FC(config.model.inter_out_dim * 2, config.model.fintune_fc_hidden_dim, config.model.dropout, config.model.out_dim)
self.regression_loss_fn = nn.MSELoss(reduce=False)
self.ranking_loss_fn = losses.pairwise_BCE_loss(config)
self.pairwise_two_tower_regression_loss = config.train.pairwise_two_tower_regression_loss
if self.pairwise_two_tower_regression_loss:
print('use two tower regression loss')
def forward(self, bg_inter, bond_feats_inter, ass_des, labels, select_flag):
graph_embedding = self.readout(bg_inter, bond_feats_inter)
if self.pretrain_use_assay_description:
if self.pretrain_assay_mlp_share:
ranking_assay_embedding = self.assay_info_aggre_mlp(ass_des)
affinity_pred = self.FC(graph_embedding + ranking_assay_embedding)
else:
regression_assay_embedding = self.assay_info_aggre_mlp_pointwise(ass_des)
affinity_pred = self.FC(graph_embedding + regression_assay_embedding)
ranking_assay_embedding = self.assay_info_aggre_mlp_pairwise(ass_des)
else:
affinity_pred = self.FC(graph_embedding)
ranking_assay_embedding = torch.zeros(len(affinity_pred))
y_pred_num = len(affinity_pred)
assert y_pred_num % 2 == 0
if self.pairwise_two_tower_regression_loss:
regression_loss = self.regression_loss_fn(affinity_pred, labels) #
labels_select = labels[select_flag]
affinity_pred_select = affinity_pred[select_flag]
regression_loss_select = regression_loss[select_flag].sum()
else:
regression_loss = self.regression_loss_fn(affinity_pred[:y_pred_num // 2], labels[:y_pred_num // 2]) #
labels_select = labels[:y_pred_num // 2][select_flag[:y_pred_num // 2]]
affinity_pred_select = affinity_pred[:y_pred_num // 2][select_flag[:y_pred_num // 2]]
regression_loss_select = regression_loss[select_flag[:y_pred_num // 2]].sum()
ranking_loss, relation, relation_pred = self.ranking_loss_fn(graph_embedding, labels, ranking_assay_embedding) #
ranking_loss_select = ranking_loss[select_flag[:y_pred_num // 2]].sum()
relation_select = relation[select_flag[:y_pred_num // 2]]
relation_pred_selcet = relation_pred[select_flag[:y_pred_num // 2]]
return regression_loss_select, ranking_loss_select,\
labels_select, affinity_pred_select,\
relation_select, relation_pred_selcet
def forward_pointwise(self, bg_inter, bond_feats_inter, ass_des, labels, select_flag):
graph_embedding = self.readout(bg_inter, bond_feats_inter)
affinity_pred = self.FC(graph_embedding)
regression_loss = self.regression_loss_fn(affinity_pred, labels) #
regression_loss_select = regression_loss[select_flag].sum()
labels_select = labels[select_flag]
affinity_pred_select = affinity_pred[select_flag]
return regression_loss_select, labels_select, affinity_pred_select
def evaluate_mtl(self, bg_inter, bond_feats_inter, ass_des, labels):
graph_embedding = self.readout(bg_inter, bond_feats_inter)
if self.pretrain_use_assay_description:
if self.pretrain_assay_mlp_share:
ranking_assay_embedding = self.assay_info_aggre_mlp(ass_des)
affinity_pred = self.FC(graph_embedding + ranking_assay_embedding)
else:
regression_assay_embedding = self.assay_info_aggre_mlp_pointwise(ass_des)
affinity_pred = self.FC(graph_embedding + regression_assay_embedding)
ranking_assay_embedding = self.assay_info_aggre_mlp_pairwise(ass_des)
else:
affinity_pred = self.FC(graph_embedding)
ranking_assay_embedding = torch.zeros(len(affinity_pred))
n = graph_embedding.shape[0]
pair_a_index, pair_b_index = [], []
for i in range(n):
pair_a_index.extend([i] * (n - 1))
pair_b_index.extend([j for j in range(n) if i != j])
pair_index = pair_a_index + pair_b_index
_, relation, relation_pred = self.ranking_fn(graph_embedding[pair_index], labels[pair_index], ranking_assay_embedding[pair_index])
return affinity_pred, relation, relation_pred
class Affinity_GNNs_MTL(nn.Module):
def __init__(self, config):
super(Affinity_GNNs_MTL, self).__init__()
lig_node_dim = config.model.lig_node_dim
lig_edge_dim = config.model.lig_edge_dim
pro_node_dim = config.model.pro_node_dim
pro_edge_dim = config.model.pro_edge_dim
layer_num = config.model.num_layers
hidden_dim = config.model.hidden_dim
jk = config.model.jk
GNN = config.model.GNN_type
self.multi_task = config.train.multi_task
self.pretrain_assay_mlp_share = config.train.pretrain_assay_mlp_share
self.pretrain_use_assay_description = config.train.pretrain_use_assay_description
self.lig_encoder = GNNs(lig_node_dim, lig_edge_dim, layer_num, hidden_dim, jk, GNN)
self.pro_encoder = GNNs(pro_node_dim, pro_edge_dim, layer_num, hidden_dim, jk, GNN)
if config.model.jk == 'concat':
self.noncov_graph = layers.DTIConvGraph3Layer(hidden_dim * (layer_num + layer_num) + config.model.inter_edge_dim, config.model.inter_out_dim, config.model.dropout)
else:
self.noncov_graph = layers.DTIConvGraph3Layer(hidden_dim * 2 + config.model.inter_edge_dim, config.model.inter_out_dim, config.model.dropout)
self.softmax = nn.Softmax(dim=1)
if self.multi_task == 'IC50KdKi':
self.IC50_ASRP_head = ASRP_head(config)
self.Kd_ASRP_head = ASRP_head(config)
self.Ki_ASRP_head = ASRP_head(config)
elif self.multi_task == 'IC50K':
self.IC50_ASRP_head = ASRP_head(config)
self.K_ASRP_head = ASRP_head(config)
self.config = config
def forward(self, batch, ASRP=True, Perturb=None, Perturb_v=None):
if self.multi_task == 'IC50KdKi':
bg_lig, bg_prot, bg_inter, labels, _, ass_des, IC50_f, Kd_f, Ki_f = batch
lig_node_feats_init = bg_lig.ndata['h']
pro_node_feats_init = bg_prot.ndata['h']
if Perturb is not None and Perturb_v == 'v_intra':
node_feats_lig = self.lig_encoder(bg_lig, Perturb_v[:bg_lig.number_of_nodes()])
node_feats_prot = self.pro_encoder(bg_prot, Perturb_v[bg_lig.number_of_nodes():])
else:
node_feats_lig = self.lig_encoder(bg_lig)
node_feats_prot = self.pro_encoder(bg_prot)
if self.config.train.encoder_ablation == 'interact':
return node_feats_lig, node_feats_prot
elif self.config.train.encoder_ablation == 'ligand':
node_feats_lig = node_feats_lig.zero_()
node_feats_lig[:,:self.config.model.lig_node_dim] = lig_node_feats_init
elif self.config.train.encoder_ablation == 'protein':
node_feats_prot = node_feats_prot.zero_()
node_feats_prot[:,:self.config.model.pro_node_dim] = pro_node_feats_init
bg_inter.ndata['h'] = self.alignfeature(bg_lig,bg_prot,node_feats_lig,node_feats_prot)
if Perturb is not None and Perturb_v == 'v_inter':
bg_inter.ndata['h'] = bg_inter.ndata['h'] + Perturb
bond_feats_inter = self.noncov_graph(bg_inter)
if ASRP:
return self.multi_head_pred(bg_inter, bond_feats_inter, labels, ass_des, IC50_f, Kd_f, Ki_f)
else:
return self.multi_head_pointwise(bg_inter, bond_feats_inter, labels, ass_des, IC50_f, Kd_f, Ki_f)
elif self.multi_task == 'IC50K':
bg_lig, bg_prot, bg_inter, labels, _, ass_des, IC50_f, K_f = batch
lig_node_feats_init = bg_lig.ndata['h']
pro_node_feats_init = bg_prot.ndata['h']
if Perturb is not None and Perturb_v == 'v_intra':
node_feats_lig = self.lig_encoder(bg_lig, Perturb_v[:bg_lig.number_of_nodes()])
node_feats_prot = self.pro_encoder(bg_prot, Perturb_v[bg_lig.number_of_nodes():])
else:
node_feats_lig = self.lig_encoder(bg_lig)
node_feats_prot = self.pro_encoder(bg_prot)
if self.config.train.encoder_ablation == 'interact':
return node_feats_lig, node_feats_prot
elif self.config.train.encoder_ablation == 'ligand':
node_feats_lig = node_feats_lig.zero_()
node_feats_lig[:,:self.config.model.lig_node_dim] = lig_node_feats_init
elif self.config.train.encoder_ablation == 'protein':
node_feats_prot = node_feats_prot.zero_()
node_feats_prot[:,:self.config.model.pro_node_dim] = pro_node_feats_init
bg_inter.ndata['h'] = self.alignfeature(bg_lig,bg_prot,node_feats_lig,node_feats_prot)
if Perturb is not None and Perturb_v == 'v_inter':
bg_inter.ndata['h'] = bg_inter.ndata['h'] + Perturb
bond_feats_inter = self.noncov_graph(bg_inter)
if ASRP:
return self.multi_head_pred_v2(bg_inter, bond_feats_inter, labels, ass_des, IC50_f, K_f)
else:
return self.multi_head_pointwise_v2(bg_inter, bond_feats_inter, labels, ass_des, IC50_f, K_f)
def multi_head_pointwise(self, bg_inter, bond_feats_inter, labels, ass_des, IC50_f, Kd_f, Ki_f):
regression_loss_IC50, affinity_IC50, affinity_pred_IC50 = \
self.IC50_ASRP_head.forward_pointwise(bg_inter, bond_feats_inter, ass_des, labels, IC50_f)
regression_loss_Kd, affinity_Kd, affinity_pred_Kd = \
self.Kd_ASRP_head.forward_pointwise(bg_inter, bond_feats_inter, ass_des, labels, Kd_f)
regression_loss_Ki, affinity_Ki, affinity_pred_Ki = \
self.Ki_ASRP_head.forward_pointwise(bg_inter, bond_feats_inter, ass_des, labels, Ki_f)
return (regression_loss_IC50, regression_loss_Kd, regression_loss_Ki),\
(affinity_pred_IC50, affinity_pred_Kd, affinity_pred_Ki), \
(affinity_IC50, affinity_Kd, affinity_Ki)
def multi_head_pointwise_v2(self, bg_inter, bond_feats_inter, labels, ass_des, IC50_f, K_f):
regression_loss_IC50, affinity_IC50, affinity_pred_IC50 = \
self.IC50_ASRP_head.forward_pointwise(bg_inter, bond_feats_inter, ass_des, labels, IC50_f)
regression_loss_K, affinity_K, affinity_pred_K = \
self.K_ASRP_head.forward_pointwise(bg_inter, bond_feats_inter, ass_des, labels, K_f)
return (regression_loss_IC50, regression_loss_K),\
(affinity_pred_IC50, affinity_pred_K), \
(affinity_IC50, affinity_K)
def multi_head_pred(self, bg_inter, bond_feats_inter, labels, ass_des, IC50_f, Kd_f, Ki_f):
regression_loss_IC50, ranking_loss_IC50, \
affinity_IC50, affinity_pred_IC50, \
relation_IC50, relation_pred_IC50 = self.IC50_ASRP_head(bg_inter, bond_feats_inter, ass_des, labels, IC50_f)
regression_loss_Kd, ranking_loss_Kd, \
affinity_Kd, affinity_pred_Kd, \
relation_Kd, relation_pred_Kd = self.Kd_ASRP_head(bg_inter, bond_feats_inter, ass_des, labels, Kd_f)
regression_loss_Ki, ranking_loss_Ki, \
affinity_Ki, affinity_pred_Ki, \
relation_Ki, relation_pred_Ki = self.Ki_ASRP_head(bg_inter, bond_feats_inter, ass_des, labels, Ki_f)
return (regression_loss_IC50, regression_loss_Kd, regression_loss_Ki),\
(ranking_loss_IC50, ranking_loss_Kd, ranking_loss_Ki), \
(affinity_pred_IC50, affinity_pred_Kd, affinity_pred_Ki), \
(relation_pred_IC50, relation_pred_Kd, relation_pred_Ki), \
(affinity_IC50, affinity_Kd, affinity_Ki), \
(relation_IC50, relation_Kd, relation_Kd)
def multi_head_pred_v2(self, bg_inter, bond_feats_inter, labels, ass_des, IC50_f, K_f):
regression_loss_IC50, ranking_loss_IC50, \
affinity_IC50, affinity_pred_IC50, \
relation_IC50, relation_pred_IC50 = self.IC50_ASRP_head(bg_inter, bond_feats_inter, ass_des, labels, IC50_f)
regression_loss_K, ranking_loss_K, \
affinity_K, affinity_pred_K, \
relation_K, relation_pred_K = self.K_ASRP_head(bg_inter, bond_feats_inter, ass_des, labels, K_f)
return (regression_loss_IC50, regression_loss_K),\
(ranking_loss_IC50, ranking_loss_K), \
(affinity_pred_IC50, affinity_pred_K), \
(relation_pred_IC50, relation_pred_K), \
(affinity_IC50, affinity_K), \
(relation_IC50, relation_K)
def multi_head_evaluate(self, bg_inter, bond_feats_inter, labels, ass_des, IC50_f, Kd_f, Ki_f):
if sum(IC50_f):
assert sum(Kd_f) == 0 and sum(Ki_f) == 0
return self.IC50_ASRP_head.evaluate_mtl(bg_inter, bond_feats_inter, labels, ass_des)
elif sum(Kd_f):
assert sum(IC50_f) == 0 and sum(Ki_f) == 0
return self.Kd_ASRP_head.evaluate_mtl(bg_inter, bond_feats_inter, labels, ass_des)
elif sum(Ki_f):
assert sum(IC50_f) == 0 and sum(Kd_f) == 0
return self.Kd_ASRP_head.evaluate_mtl(bg_inter, bond_feats_inter, labels, ass_des)
def alignfeature(self,bg_lig,bg_prot,node_feats_lig,node_feats_prot):
inter_feature = torch.cat((node_feats_lig,node_feats_prot))
lig_num,prot_num = bg_lig.batch_num_nodes(),bg_prot.batch_num_nodes()
lig_start, prot_start = lig_num.cumsum(0) - lig_num, prot_num.cumsum(0) - prot_num
inter_start = lig_start + prot_start
for i in range(lig_num.shape[0]):
inter_feature[inter_start[i]:inter_start[i]+lig_num[i]] = node_feats_lig[lig_start[i]:lig_start[i]+lig_num[i]]
inter_feature[inter_start[i]+lig_num[i]:inter_start[i]+lig_num[i]+prot_num[i]] = node_feats_prot[prot_start[i]:prot_start[i]+prot_num[i]]
return inter_feature
class interact_ablation(nn.Module):
def __init__(self, config):
super(interact_ablation, self).__init__()
self.IC50_ASRP_head = interact_ablation_head(config)
self.K_ASRP_head = interact_ablation_head(config)
self.config = config
def forward(self, graph_embedding, labels, IC50_f, K_f):
regression_loss_IC50, \
affinity_IC50, affinity_pred_IC50,= self.IC50_ASRP_head(graph_embedding, labels, IC50_f)
regression_loss_K, \
affinity_K, affinity_pred_K = self.K_ASRP_head(graph_embedding, labels, K_f)
return (regression_loss_IC50, regression_loss_K),\
(affinity_pred_IC50, affinity_pred_K), \
(affinity_IC50, affinity_K), \
class interact_ablation_head(nn.Module):
def __init__(self, config):
super(interact_ablation_head, self).__init__()
self.FC = layers.FC(config.model.inter_out_dim * 2, config.model.fintune_fc_hidden_dim, config.model.dropout,
config.model.out_dim)
self.regression_loss_fn = nn.MSELoss(reduce=False)
def forward(self, graph_embedding, labels, select_flag):
affinity_pred = self.FC(graph_embedding)
regression_loss = self.regression_loss_fn(affinity_pred, labels) #
regression_loss_select = regression_loss[select_flag].sum()
labels_select = labels[select_flag]
affinity_pred_select = affinity_pred[select_flag]
return regression_loss_select, labels_select, affinity_pred_select |