def MolToMPL(mol, size=(300, 300), kekulize=True, wedgeBonds=True, imageType=None, fitImage=False,
options=None, **kwargs):
if not mol:
raise ValueError('Null molecule provided')
from rdkit.Chem.Draw.mplCanvas import Canvas
canvas = Canvas(size)
if options is None:
options = DrawingOptions()
options.bgColor = None
if fitImage:
options.dotsPerAngstrom = int(min(size) / 10)
options.atomLabelFontSize = 7
options.bondLineWidth = 2
options.coordScale = 1
options.wedgeDashedBonds = wedgeBonds
drawer = MolDrawing(canvas=canvas, drawingOptions=options)
omol = mol
if kekulize:
from rdkit import Chem
mol = Chem.Mol(mol.ToBinary())
Chem.Kekulize(mol)
if not mol.GetNumConformers():
from rdkit.Chem import AllChem
AllChem.Compute2DCoords(mol)
drawer.AddMol(mol, **kwargs)
omol._atomPs = drawer.atomPs[mol]
for k, v in omol._atomPs.items():
omol._atomPs[k] = canvas.rescalePt(v)
canvas._figure.set_size_inches(float(size[0]) / 100, float(size[1]) / 100)
return canvas._figure
def compute_mol_attributes(graph,
labels_dict,
actions_history_smi_pop,
actions_history_smi_removed,
actions_history_scores_pop,
actions_history_scores_removed,
legend_scores_keys_strat=None):
images_attributes = {}
scores_attributes = {}
draw_opt = DrawingOptions()
draw_opt.coordScale = 0.9
draw_opt.dotsPerAngstrom = 30
for action_history_k in labels_dict.keys():
if action_history_k in actions_history_smi_pop:
smi = actions_history_smi_pop[action_history_k]
img = MolToImage(MolFromSmiles(smi),
size=(800, 800),
options=draw_opt)
images_attributes[action_history_k] = crop_image_with_transparency(
img)
legend, _ = compute_mol_legend(action_history_k, smi,
actions_history_scores_pop,
legend_scores_keys_strat)
scores_attributes[action_history_k] = legend
else:
smi = actions_history_smi_removed[action_history_k]
img = MolToImage(MolFromSmiles(smi),
size=(800, 800),
options=draw_opt)
images_attributes[action_history_k] = crop_image_with_transparency(
img)
legend, _ = compute_mol_legend(action_history_k, smi,
actions_history_scores_removed,
legend_scores_keys_strat)
scores_attributes[action_history_k] = legend
nx.set_node_attributes(graph, images_attributes, "image")
nx.set_node_attributes(graph, scores_attributes, "score_label")