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visualizesmartsmodel.py
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visualizesmartsmodel.py
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#!/usr/bin/env python
import numpy as np
import argparse, sys, pickle, math, rdkit, matplotlib
from sklearn.cross_decomposition import PLSRegression
from sklearn.linear_model import *
from rdkit.Chem import AllChem as Chem
import matplotlib.pylab as plt
from rdkit.Chem.Draw import SimilarityMaps
from matplotlib.colors import LinearSegmentedColormap
from matplotlib import colorbar
from matplotlib import cm
from numpy.random import randn
import matplotlib.patheffects as PathEffects
import matlab
import warnings
warnings.simplefilter('ignore', FutureWarning)
if __name__ == "__main__":
parser = argparse.ArgumentParser(description='Visually map model trained using smarts patterns onto molecule. Will output similarity maps.')
parser.add_argument('model', type=argparse.FileType('r'), help='Model file')
parser.add_argument('smarts', type=argparse.FileType('r'), help='SMARTS file')
parser.add_argument('smi', type=argparse.FileType('r'), help='SMILES file to annotate')
parser.add_argument('--size', type=int, default=250, help='Image size')
parser.add_argument('--labels', action='store_true', default=False, help='Show labels in image')
parser.add_argument('--weights', action='store_true', default=False, help='Show atom weights in image')
args = parser.parse_args()
# read model
model = pickle.load(args.model)
# read Smarts
smarts = []
for line in args.smarts:
if line.startswith('#') or len(line.strip()) == 0:
continue
tokens = line.split()
mol = Chem.MolFromSmarts(tokens[0])
smarts.append(mol)
# get coefficients from model
coefs = model.coef_
nonzeroes = np.nonzero(coefs)[0]
if len(coefs) != len(smarts):
print "Mismatch between model size and number fo smarts (%d vs %d)" % (len(coefs), len(smarts))
sys.exit(-1)
# process each cmpd, save weights and mol - want to
mols = []
weights = []
maxval = 0
for (i, line) in enumerate(args.smi):
if line.startswith('#') or len(line.strip()) == 0:
continue
tokens = line.split()
mol = Chem.MolFromSmiles(tokens[0])
if len(tokens) < 2:
name = str(i)
else:
name = tokens[1]
mol.title = name
mols.append(mol)
# apply weights
atomcontribs = np.zeros(mol.GetNumAtoms())
for c in nonzeroes:
pat = smarts[c];
w = coefs[c] / float(pat.GetNumAtoms())
matches = mol.GetSubstructMatches(pat, True)
for m in matches: # each match is a tuple of atom indices
for a in m:
atomcontribs[a] += w
weights.append(atomcontribs)
maxval = max(maxval, np.max(np.abs(atomcontribs)))
Chem.Compute2DCoords(mol)
# print index, len(annotateWeights)
# normalize the weights
normweights = [w / maxval for w in weights]
# make a custom red - white - green color map
# slight tint so O and Cl are still visible
cdict = {'red': ((0.0, 1.0, 1.0),
(0.5, 1.0, 1.0),
(1.0, 0.2, 0.2)),
'green': ((0.0, 0.2, 0.2),
(0.5, 1.0, 1.0),
(1.0, 1.0, 1.0)),
'blue': ((0.0, 0.2, 0.2),
(0.5, 1.0, 1.0),
(1.0, 0.2, 0.2))};
colors = LinearSegmentedColormap('RWG', cdict)
size = (args.size, args.size) # get errors if not square
# this is an awfully hacky way to standardize the color ranges across
# all the processed molecules. A major issue is that scale and sigma
# are in image coordinates
sigma = 0.05
if len(mols) and mols[0].GetNumBonds() > 0:
mol = mols[0]
rdkit.Chem.Draw.MolToMPL(mol, size=size) # initialize 2D coordinates
plt.clf()
bond = mol.GetBondWithIdx(0)
idx1 = bond.GetBeginAtomIdx()
idx2 = bond.GetEndAtomIdx()
sigma = 0.5 * math.sqrt(sum([(mol._atomPs[idx1][i] - mol._atomPs[idx2][i]) ** 2 for i in range(2)]))
scale = matlab.bivariate_normal(0, 0, sigma, sigma, 0, 0)
for (mol, w, normw) in zip(mols, weights, normweights):
fig = SimilarityMaps.GetSimilarityMapFromWeights(mol, normw, size=size, scale=scale, sigma=sigma, colorMap=colors, contourLines=1, alpha=0, coordscale=1) # alpha hides contour lines
if not args.labels:
# I don't like white boxes on labels
for elem in fig.axes[0].get_children():
if isinstance(elem, matplotlib.text.Text):
elem.set_visible(False)
plt.axis("off")
# this is the code that plots the weights to the compounds.
if args.weights:
for at in xrange(mol.GetNumAtoms()):
x = mol._atomPs[at][0]
y = mol._atomPs[at][1]
plt.text(x, y, '%.2f' % w[at], path_effects=[PathEffects.withStroke(linewidth=1, foreground="blue")])
plt.savefig('%s.png' % mol.title, bbox_inches='tight')