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import torch.nn as nn
import torch.nn.functional as F
from torch_scatter import scatter
from .prop_egnn import EnEquiEncoder
from ..common import compose_context_prop, ShiftedSoftplus
def get_encoder(config):
if config.name == 'egnn' or config.name == 'egnn_enc':
net = EnEquiEncoder(
num_layers=config.num_layers,
edge_feat_dim=config.edge_dim,
hidden_dim=config.hidden_dim,
num_r_gaussian=config.num_r_gaussian,
act_fn=config.act_fn,
norm=config.norm,
update_x=False,
k=config.knn,
cutoff=config.cutoff,
)
else:
raise ValueError(config.name)
return net
class PropPredNet(nn.Module):
def __init__(self, config, protein_atom_feature_dim, ligand_atom_feature_dim, output_dim=3):
super(PropPredNet, self).__init__()
self.config = config
self.hidden_dim = config.hidden_channels
self.output_dim = output_dim
self.protein_atom_emb = nn.Linear(protein_atom_feature_dim, self.hidden_dim)
self.ligand_atom_emb = nn.Linear(ligand_atom_feature_dim, self.hidden_dim)
# self.mean = target_mean
# self.std = target_std
# self.register_buffer('target_mean', target_mean)
# self.register_buffer('target_std', target_std)
self.encoder = get_encoder(config.encoder)
self.out_block = nn.Sequential(
nn.Linear(self.hidden_dim, self.hidden_dim),
ShiftedSoftplus(),
nn.Linear(self.hidden_dim, output_dim),
)
def forward(self, protein_pos, protein_atom_feature, ligand_pos, ligand_atom_feature, batch_protein, batch_ligand,
output_kind):
h_protein = self.protein_atom_emb(protein_atom_feature)
h_ligand = self.ligand_atom_emb(ligand_atom_feature)
h_ctx, pos_ctx, batch_ctx = compose_context_prop(
h_protein=h_protein,
h_ligand=h_ligand,
pos_protein=protein_pos,
pos_ligand=ligand_pos,
batch_protein=batch_protein,
batch_ligand=batch_ligand,
)
h_ctx = self.encoder(
node_attr=h_ctx,
pos=pos_ctx,
batch=batch_ctx,
) # (N_p+N_l, H)
# Aggregate messages
pre_out = scatter(h_ctx, index=batch_ctx, dim=0, reduce='sum') # (N, F)
output = self.out_block(pre_out) # (N, C)
if output_kind is not None:
output_mask = F.one_hot(output_kind - 1, self.output_dim)
output = torch.sum(output * output_mask, dim=-1, keepdim=True)
return output
def get_loss(self, batch, pos_noise_std, return_pred=False):
protein_noise = torch.randn_like(batch.protein_pos) * pos_noise_std
ligand_noise = torch.randn_like(batch.ligand_pos) * pos_noise_std
pred = self(
protein_pos=batch.protein_pos + protein_noise,
protein_atom_feature=batch.protein_atom_feature.float(),
ligand_pos=batch.ligand_pos + ligand_noise,
ligand_atom_feature=batch.ligand_atom_feature_full.float(),
batch_protein=batch.protein_element_batch,
batch_ligand=batch.ligand_element_batch,
output_kind=batch.kind,
# output_kind=None
)
# pred = pred * y_std + y_mean
loss_func = nn.MSELoss()
loss = loss_func(pred.view(-1), batch.y)
if return_pred:
return loss, pred
else:
return loss
class PropPredNetEnc(nn.Module):
def __init__(self, config, protein_atom_feature_dim, ligand_atom_feature_dim,
enc_ligand_dim, enc_node_dim, enc_graph_dim, enc_feature_type=None, output_dim=1):
super(PropPredNetEnc, self).__init__()
self.config = config
self.hidden_dim = config.hidden_channels
self.output_dim = output_dim
self.enc_ligand_dim = enc_ligand_dim
self.enc_node_dim = enc_node_dim
self.enc_graph_dim = enc_graph_dim
self.enc_feature_type = enc_feature_type
self.protein_atom_emb = nn.Linear(protein_atom_feature_dim, self.hidden_dim)
self.ligand_atom_emb = nn.Linear(ligand_atom_feature_dim + enc_ligand_dim, self.hidden_dim)
# self.mean = target_mean
# self.std = target_std
# self.register_buffer('target_mean', target_mean)
# self.register_buffer('target_std', target_std)
self.encoder = get_encoder(config.encoder)
if self.enc_node_dim > 0:
self.enc_node_layer = nn.Sequential(
nn.Linear(self.hidden_dim + self.enc_node_dim, self.hidden_dim),
nn.ReLU(),
nn.Linear(self.hidden_dim, self.hidden_dim),
)
self.out_block = nn.Sequential(
nn.Linear(self.hidden_dim + self.enc_graph_dim, self.hidden_dim),
ShiftedSoftplus(),
nn.Linear(self.hidden_dim, output_dim),
)
def forward(self, protein_pos, protein_atom_feature, ligand_pos, ligand_atom_feature, batch_protein, batch_ligand,
output_kind, enc_ligand_feature, enc_node_feature, enc_graph_feature):
h_protein = self.protein_atom_emb(protein_atom_feature)
if enc_ligand_feature is not None:
ligand_atom_feature = torch.cat([ligand_atom_feature, enc_ligand_feature], dim=-1)
h_ligand = self.ligand_atom_emb(ligand_atom_feature)
h_ctx, pos_ctx, batch_ctx = compose_context_prop(
h_protein=h_protein,
h_ligand=h_ligand,
pos_protein=protein_pos,
pos_ligand=ligand_pos,
batch_protein=batch_protein,
batch_ligand=batch_ligand,
)
h_ctx = self.encoder(
node_attr=h_ctx,
pos=pos_ctx,
batch=batch_ctx,
) # (N_p+N_l, H)
if enc_node_feature is not None:
h_ctx = torch.cat([h_ctx, enc_node_feature], dim=-1)
h_ctx = self.enc_node_layer(h_ctx)
# Aggregate messages
pre_out = scatter(h_ctx, index=batch_ctx, dim=0, reduce='sum') # (N, F)
if enc_graph_feature is not None:
pre_out = torch.cat([pre_out, enc_graph_feature], dim=-1)
output = self.out_block(pre_out) # (N, C)
if output_kind is not None:
output_mask = F.one_hot(output_kind - 1, self.output_dim)
output = torch.sum(output * output_mask, dim=-1, keepdim=True)
return output
def get_loss(self, batch, pos_noise_std, return_pred=False):
protein_noise = torch.randn_like(batch.protein_pos) * pos_noise_std
ligand_noise = torch.randn_like(batch.ligand_pos) * pos_noise_std
# add features
enc_ligand_feature, enc_node_feature, enc_graph_feature = None, None, None
if self.enc_feature_type == 'nll_all':
enc_graph_feature = batch.nll_all # [num_graphs, 22]
elif self.enc_feature_type == 'nll':
enc_graph_feature = batch.nll # [num_graphs, 20]
elif self.enc_feature_type == 'final_h':
enc_node_feature = batch.final_h # [num_pl_atoms, 128]
elif self.enc_feature_type == 'pred_ligand_v':
enc_ligand_feature = batch.pred_ligand_v # [num_l_atoms, 13]
elif self.enc_feature_type == 'pred_v_entropy_pre':
enc_ligand_feature = batch.pred_v_entropy # [num_l_atoms, 1]
elif self.enc_feature_type == 'pred_v_entropy_post':
enc_graph_feature = scatter(batch.pred_v_entropy, index=batch.ligand_element_batch, dim=0, reduce='sum') # [num_graphs, 1]
elif self.enc_feature_type == 'full':
enc_graph_feature = torch.cat(
[batch.nll_all, scatter(batch.pred_v_entropy, index=batch.ligand_element_batch, dim=0, reduce='sum')], dim=-1)
enc_node_feature = batch.final_h
enc_ligand_feature = torch.cat([batch.pred_ligand_v, batch.pred_v_entropy], -1)
else:
raise NotImplementedError
pred = self(
protein_pos=batch.protein_pos + protein_noise,
protein_atom_feature=batch.protein_atom_feature.float(),
ligand_pos=batch.ligand_pos + ligand_noise,
ligand_atom_feature=batch.ligand_atom_feature_full.float(),
batch_protein=batch.protein_element_batch,
batch_ligand=batch.ligand_element_batch,
output_kind=batch.kind,
# output_kind=None,
enc_ligand_feature=enc_ligand_feature,
enc_node_feature=enc_node_feature,
enc_graph_feature=enc_graph_feature
)
# pred = pred * y_std + y_mean
loss_func = nn.MSELoss()
loss = loss_func(pred.view(-1), batch.y)
if return_pred:
return loss, pred
else:
return loss
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