Yesterday, I posted an example of DGL (almost same as original example code).
And I could make regression model with my own dataset. Fortunately DGL developer provides a code for visualize atom weights of trained model.
It means that, after building the model with AttentiveFP, you can visualize atom weight of the give molecule which means how much each atom contribute to the target value.
I saw many example of the approach but never tried it by myself. So I tried to it today.
Following code is same as yesterday.
%matplotlib inline
import matplotlib.pyplot as plt
import os
from rdkit import Chem
from rdkit import RDPaths
import dgl
import numpy as np
import random
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.data import DataLoader
from torch.utils.data import Dataset
from dgl import model_zoo
from dgl.data.chem.utils import mol_to_complete_graph, mol_to_bigraph
from dgl.data.chem.utils import atom_type_one_hot
from dgl.data.chem.utils import atom_degree_one_hot
from dgl.data.chem.utils import atom_formal_charge
from dgl.data.chem.utils import atom_num_radical_electrons
from dgl.data.chem.utils import atom_hybridization_one_hot
from dgl.data.chem.utils import atom_total_num_H_one_hot
from dgl.data.chem.utils import one_hot_encoding
from dgl.data.chem import CanonicalAtomFeaturizer
from dgl.data.chem import CanonicalBondFeaturizer
from dgl.data.chem import ConcatFeaturizer
from dgl.data.chem import BaseAtomFeaturizer
from dgl.data.chem import BaseBondFeaturizer
from dgl.data.chem import one_hot_encoding
from dgl.data.utils import split_dataset
from functools import partial
from sklearn.metrics import roc_auc_score
def chirality(atom):
try:
return one_hot_encoding(atom.GetProp('_CIPCode'), ['R', 'S']) + \
[atom.HasProp('_ChiralityPossible')]
except:
return [False, False] + [atom.HasProp('_ChiralityPossible')]
def collate_molgraphs(data):
"""Batching a list of datapoints for dataloader.
Parameters
----------
data : list of 3-tuples or 4-tuples.
Each tuple is for a single datapoint, consisting of
a SMILES, a DGLGraph, all-task labels and optionally
a binary mask indicating the existence of labels.
Returns
-------
smiles : list
List of smiles
bg : BatchedDGLGraph
Batched DGLGraphs
labels : Tensor of dtype float32 and shape (B, T)
Batched datapoint labels. B is len(data) and
T is the number of total tasks.
masks : Tensor of dtype float32 and shape (B, T)
Batched datapoint binary mask, indicating the
existence of labels. If binary masks are not
provided, return a tensor with ones.
"""
assert len(data[0]) in [3, 4], \
'Expect the tuple to be of length 3 or 4, got {:d}'.format(len(data[0]))
if len(data[0]) == 3:
smiles, graphs, labels = map(list, zip(*data))
masks = None
else:
smiles, graphs, labels, masks = map(list, zip(*data))
bg = dgl.batch(graphs)
bg.set_n_initializer(dgl.init.zero_initializer)
bg.set_e_initializer(dgl.init.zero_initializer)
labels = torch.stack(labels, dim=0)
if masks is None:
masks = torch.ones(labels.shape)
else:
masks = torch.stack(masks, dim=0)
return smiles, bg, labels, masks
atom_featurizer = BaseAtomFeaturizer(
{'hv': ConcatFeaturizer([
partial(atom_type_one_hot, allowable_set=[
'B', 'C', 'N', 'O', 'F', 'Si', 'P', 'S', 'Cl', 'As', 'Se', 'Br', 'Te', 'I', 'At'],
encode_unknown=True),
partial(atom_degree_one_hot, allowable_set=list(range(6))),
atom_formal_charge, atom_num_radical_electrons,
partial(atom_hybridization_one_hot, encode_unknown=True),
lambda atom: [0], # A placeholder for aromatic information,
atom_total_num_H_one_hot, chirality
],
)})
bond_featurizer = BaseBondFeaturizer({
'he': lambda bond: [0 for _ in range(10)]
})
train=os.path.join(RDPaths.RDDocsDir, 'Book/data/solubility.train.sdf')
test=os.path.join(RDPaths.RDDocsDir, 'Book/data/solubility.test.sdf')
train_graph =[mol_to_bigraph(mol,
atom_featurizer=atom_featurizer,
bond_featurizer=bond_featurizer) for mol in train_mols]
test_graph =[mol_to_bigraph(mol,
atom_featurizer=atom_featurizer,
bond_featurizer=bond_featurizer) for mol in test_mols]
def run_a_train_epoch(n_epochs, epoch, model, data_loader,loss_criterion, optimizer):
model.train()
total_loss = 0
losses = []
for batch_id, batch_data in enumerate(data_loader):
batch_data
smiles, bg, labels, masks = batch_data
if torch.cuda.is_available():
bg.to(torch.device('cuda:0'))
labels = labels.to('cuda:0')
masks = masks.to('cuda:0')
prediction = model(bg, bg.ndata['hv'], bg.edata['he'])
loss = (loss_criterion(prediction, labels)*(masks != 0).float()).mean()
#loss = loss_criterion(prediction, labels)
#print(loss.shape)
optimizer.zero_grad()
loss.backward()
optimizer.step()
losses.append(loss.data.item())
#total_score = np.mean(train_meter.compute_metric('rmse'))
total_score = np.mean(losses)
print('epoch {:d}/{:d}, training {:.4f}'.format( epoch + 1, n_epochs, total_score))
return total_score
model = model_zoo.chem.AttentiveFP(node_feat_size=39,
edge_feat_size=10,
num_layers=2,
num_timesteps=2,
graph_feat_size=200,
output_size=1,
dropout=0.2)
model = model.to('cuda:0')
train_loader = DataLoader(dataset=list(zip(train_smi, train_graph, train_sol)), batch_size=128, collate_fn=collate_molgraphs)
test_loader = DataLoader(dataset=list(zip(test_smi, test_graph, test_sol)), batch_size=128, collate_fn=collate_molgraphs)
loss_fn = nn.MSELoss(reduction='none')
optimizer = torch.optim.Adam(model.parameters(), lr=10 ** (-2.5), weight_decay=10 ** (-5.0),)
n_epochs = 100
epochs = []
scores = []
for e in range(n_epochs):
score = run_a_train_epoch(n_epochs, e, model, train_loader, loss_fn, optimizer)
epochs.append(e)
scores.append(score)
model.eval()
OK, I build the predictive model. (of course build model can save with torch.save(model.state_dict(), PATH) method.)
Let’s visualize molecule with atom weights!
At first, import packages for molecule visualization.
import copy from rdkit.Chem import rdDepictor from rdkit.Chem.Draw import rdMolDraw2D from IPython.display import SVG from IPython.display import display import matplotlib import matplotlib.cm as cm
Then define visualization function. Following code is borrowed from original repository thanks a lot. DGL model has get_node_weight option, which returns node_weight of the graph. The model has two layers of GRU so timestep must be 0 or 1 following code I used 0 as timestep.
def drawmol(idx, dataset, timestep):
smiles, graph, _ = dataset[idx]
print(smiles)
bg = dgl.batch([graph])
atom_feats, bond_feats = bg.ndata['hv'], bg.edata['he']
if torch.cuda.is_available():
print('use cuda')
bg.to(torch.device('cuda:0'))
atom_feats = atom_feats.to('cuda:0')
bond_feats = bond_feats.to('cuda:0')
_, atom_weights = model(bg, atom_feats, bond_feats, get_node_weight=True)
assert timestep < len(atom_weights), 'Unexpected id for the readout round'
atom_weights = atom_weights[timestep]
min_value = torch.min(atom_weights)
max_value = torch.max(atom_weights)
atom_weights = (atom_weights - min_value) / (max_value - min_value)
norm = matplotlib.colors.Normalize(vmin=0, vmax=1.28)
cmap = cm.get_cmap('bwr')
plt_colors = cm.ScalarMappable(norm=norm, cmap=cmap)
atom_colors = {i: plt_colors.to_rgba(atom_weights[i].data.item()) for i in range(bg.number_of_nodes())}
mol = Chem.MolFromSmiles(smiles)
rdDepictor.Compute2DCoords(mol)
drawer = rdMolDraw2D.MolDraw2DSVG(280, 280)
drawer.SetFontSize(1)
op = drawer.drawOptions()
mol = rdMolDraw2D.PrepareMolForDrawing(mol)
drawer.DrawMolecule(mol, highlightAtoms=range(bg.number_of_nodes()),
highlightBonds=[],
highlightAtomColors=atom_colors)
drawer.FinishDrawing()
svg = drawer.GetDrawingText()
svg = svg.replace('svg:', '')
if torch.cuda.is_available():
atom_weights = atom_weights.to('cpu')
return (Chem.MolFromSmiles(smiles), atom_weights.data.numpy(), svg)
Draw test dataset molecules. The model predicts solubility and color indicates that red is positive effect for solubility and blue is negative impact.
target = test_loader.dataset
for i in range(len(target)):
mol, aw, svg = drawmol(i, target, 0)
display(SVG(svg))
For personally, hydroxyl group has positive effect for solubility I think but the model shows it is not always true. Hmm my code is something wrong? Or I need to think about more details of the model?
I would like to try more predictive task and write helper code for DGL’s AttentiveFP for convenient way to molecular visualization and model building.
Today’s whole code is uploaded below.
https://gist.github.com/iwatobipen/72a2d9dd616322f1f20469a152f2bb58
Any comments or suggestions will be highly appreciated. ;)
