def scoring_function(smiles_list):
scores = []
for smiles in smiles_list:
adjacency, nodes, edges = smile_to_graph(smiles)
adjacency, nodes, edges = molgraph_collate_fn(
((adjacency, nodes, edges), ))
output = model.forward(nodes, edges, adjacency)
scores.append(float(m(output)))
return scores
def property_prediction(smiles,Property):
valid = {"t_half","logD","hml_clearance"}
if Property not in valid:
raise ValueError("property must be one of %r." % valid)
adjacency, nodes, edges = smile_to_graph(smiles)
adjacency, nodes, edges=molgraph_collate_fn(((adjacency, nodes, edges),))
if Property=="t_half":
model = EMNImplementation(node_features=40, edge_features=4,edge_embedding_size=50, message_passes=6, out_features=1,
edge_emb_depth=3, edge_emb_hidden_dim=120,
att_depth=3, att_hidden_dim=80,
msg_depth=3, msg_hidden_dim=80,
gather_width=100,
gather_att_depth=3, gather_att_hidden_dim=80,
gather_emb_depth=3, gather_emb_hidden_dim=80,
out_depth=2, out_hidden_dim=60)
checkpoint = torch.load(r"checkpoints/t_half.ckpt")
model.load_state_dict(checkpoint['state_dict'])
model.eval()
output = model.forward(nodes, edges,adjacency)
return output
if Property=="logD":
model = EMNImplementation(node_features=40, edge_features=4,edge_embedding_size=50, message_passes=6, out_features=1,
edge_emb_depth=3, edge_emb_hidden_dim=120,
att_depth=3, att_hidden_dim=80,
msg_depth=3, msg_hidden_dim=80,
gather_width=100,
gather_att_depth=3, gather_att_hidden_dim=80,
gather_emb_depth=3, gather_emb_hidden_dim=80,
out_depth=2, out_hidden_dim=60)
checkpoint = torch.load(r"checkpoints/logD.ckpt")
model.load_state_dict(checkpoint['state_dict'])
model.eval()
output = model.forward(nodes, edges,adjacency)
return output
if Property=="hml_clearance":
model = EMNImplementation(node_features=40, edge_features=4,edge_embedding_size=50, message_passes=6, out_features=1,
edge_emb_depth=3, edge_emb_hidden_dim=120,
att_depth=3, att_hidden_dim=60,
msg_depth=3, msg_hidden_dim=60,
gather_width=80,
gather_att_depth=3, gather_att_hidden_dim=80,
gather_emb_depth=3, gather_emb_hidden_dim=80,
out_depth=2, out_hidden_dim=60)
checkpoint = torch.load(r"checkpoints/hml_clearance.ckpt")
model.load_state_dict(checkpoint['state_dict'])
model.eval()
output = model.forward(nodes, edges,adjacency)
return torch.exp(output)
def visualizations(model, smile, feature_list, color_map=plt.cm.bwr):
model.eval()
adjacency, nodes, edges = smile_to_graph(smile)
mols = Chem.MolFromSmiles(smile)
ig = IntegratedGradients(model)
adjacency, nodes, edges = molgraph_collate_fn(
((adjacency, nodes, edges), ))
attr = ig.attribute(nodes,
additional_forward_args=(edges, adjacency),
target=0)
attr1 = torch.squeeze(attr, dim=0)
attr2 = attr1.sum(dim=1)
vmax = max(attr2.abs().max(), 1e-16)
vmin = -vmax
node_colors = get_colors(attr1, color_map)
node_colors = node_colors[:, :3]
fig, ax = plt.subplots(figsize=(6, 1))
fig.subplots_adjust(bottom=0.5)
norm = plt.Normalize(vmin, vmax)
fig.colorbar(cm.ScalarMappable(norm=norm, cmap=color_map),
cax=ax,
orientation='horizontal',
label='color_bar')
b = BytesIO()
b.write(
moltopng(mols,
node_colors=node_colors,
edge_colors={},
molSize=(600, 600)))
b.seek(0)
display(Image.open(b))
b.close()
symbols = {
i: f'{mols.GetAtomWithIdx(i).GetSymbol()}{i}'
for i in range(mols.GetNumAtoms())
}
x_pos = (np.arange(len(feature_list)))
y_pos = (np.arange(len(list(symbols.values()))))
plt.matshow(attr1, cmap=color_map)
plt.xticks(x_pos, feature_list, rotation='vertical')
plt.yticks(y_pos, list(symbols.values()))
plt.show()
visualize_importances(list(symbols.values()), attr2)
def prepare_data(self):
data = pd.read_csv('t_half.csv')
data_list = []
for index in range(len(data)):
adjacency, nodes, edges = smile_to_graph(data['Smiles'][index])
#targets = data.iloc[index,1:]
targets = np.expand_dims(data['Standard Value'][index], axis=0)
data_list.append(((adjacency, nodes, edges), targets))
l = len(data)
shuff = list(range(l))
random.shuffle(shuff)
data_list1 = [data_list[i] for i in shuff]
self.ggnn_train = tuple(data_list1[:int(l * 0.8)])
self.ggnn_val = tuple(data_list1[int(l * 0.8):int(l * 0.9)])
self.ggnn_test = tuple(data_list1[int(l * 0.9):])