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calculate_statistics_single_prop.py
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import numpy as np
import os
import math
import pickle
import argparse
from tqdm import tqdm
from rdkit import Chem
from rdkit.Chem import Descriptors
from rdkit.Chem import Draw
from tdc import Oracle
from rdkit import DataStructs
from rdkit import RDLogger
from scipy import stats
RDLogger.DisableLog('rdApp.*')
import mflow.utils.environment as env
def check_validity(generated_all_smiles):
count = 0
valid_mols = []
for sm in generated_all_smiles:
mol = Chem.MolFromSmiles(sm)
if mol is not None:
valid_mols.append(sm)
count += 1
return count, valid_mols
def check_sim(train_smiles, gen_smiles):
train_fps = Chem.rdMolDescriptors.GetMorganFingerprintAsBitVect(Chem.MolFromSmiles(train_smiles), 4, nBits=2048)
gen_fps = Chem.rdMolDescriptors.GetMorganFingerprintAsBitVect(Chem.MolFromSmiles(gen_smiles), 4, nBits=2048)
dist = DataStructs.TanimotoSimilarity(train_fps, gen_fps, returnDistance=True)
score = np.mean(dist)
return score
GSK3B_scorer = Oracle(name = 'GSK3B')
SA_scorer = Oracle(name = 'SA')
DRD2_scorer = Oracle(name = 'DRD2')
JNK3_scorer = Oracle(name = 'JNK3')
def check_SA(gen_smiles):
score = SA_scorer(Chem.MolToSmiles(gen_smiles))
return score
def check_DRD2(gen_smiles):
score = DRD2_scorer(Chem.MolToSmiles(gen_smiles))
return score
def check_JNK3(gen_smiles):
score = JNK3_scorer(Chem.MolToSmiles(gen_smiles))
return score
def check_GSK3B(gen_smiles):
score = GSK3B_scorer(Chem.MolToSmiles(gen_smiles))
return score
def check_plogp(mol):
plogp = env.penalized_logp(mol)
return plogp
def cache_prop_pred():
prop_pred = {}
for prop_name, function in Descriptors.descList:
prop_pred[prop_name] = function
prop_pred['sa'] = check_SA
prop_pred['drd2'] = check_DRD2
prop_pred['jnk3'] = check_JNK3
prop_pred['gsk3b'] = check_GSK3B
prop_pred['plogp'] = check_plogp
return prop_pred
def check_unique(generated_all_smiles):
return len(set(generated_all_smiles))
def check_novelty(generated_all_smiles, train_smiles):
new_molecules = 0
for sm in generated_all_smiles:
if sm not in train_smiles:
new_molecules += 1
return new_molecules
parser = argparse.ArgumentParser()
parser.add_argument("--dataset", type=str, default='zinc250k')
parser.add_argument("--baseline", type=str, default='chemspace')
parser.add_argument("--mani_range", type=int, default=5)
parser.add_argument("--save_dir", type=str, default='./')
parser.add_argument("--num_samples", type=str, default=200)
parser.add_argument("--path_len", type=int, default=21)
parser.add_argument("--epsilon", type=str, default=0.05)
parser.add_argument("--gamma", type=str, default=0.05)
args = parser.parse_args()
# props = ['MaxEStateIndex', 'MinEStateIndex', 'MaxAbsEStateIndex', 'MinAbsEStateIndex', 'qed', 'MolWt', 'HeavyAtomMolWt', 'ExactMolWt', 'NumValenceElectrons', 'NumRadicalElectrons', 'MaxPartialCharge', 'MinPartialCharge', 'MaxAbsPartialCharge', 'MinAbsPartialCharge', 'FpDensityMorgan1', 'FpDensityMorgan2', 'FpDensityMorgan3', 'MWHI', 'MWLOW', 'CHGHI', 'CHGLO', 'LOGPHI', 'LOGPLOW', 'MRHI', 'MRLOW', 'BalabanJ', 'BertzCT', 'Chi0', 'Chi0n', 'Chi0v', 'Chi1', 'Chi1n', 'Chi1v', 'Chi2n', 'Chi2v', 'Chi3n', 'Chi3v', 'Chi4n', 'Chi4v', 'HallKierAlpha', 'Ipc', 'Kappa1', 'Kappa2', 'Kappa3', 'LabuteASA', 'VSA13', 'VSA14', 'TPSA', 'EState1', 'EState10', 'EState2', 'EState3', 'EState4', 'EState5', 'EState6', 'EState7', 'EState8', 'EState9', 'FractionCSP3', 'HeavyAtomCount', 'NHOHCount', 'NOCount', 'NumAliphaticCarbocycles', 'NumAliphaticHeterocycles', 'NumAliphaticRings', 'NumAromaticCarbocycles', 'NumAromaticHeterocycles', 'NumAromaticRings', 'NumHAcceptors', 'NumHDonors', 'NumHeteroatoms', 'NumRotatableBonds', 'NumSaturatedCarbocycles', 'NumSaturatedHeterocycles', 'NumSaturatedRings', 'RingCount', 'MolLogP', 'MolMR', 'noTert', 'ArN', 'N', 'NH', 'COO2', 'noCOO', 'S', 'HOCCN', 'Imine', 'NH0', 'NH1', 'NH2', 'Ndealkylation1', 'Ndealkylation2', 'Nhpyrrole', 'SH', 'aldehyde', 'carbamate', 'halide', 'oxid', 'amide', 'amidine', 'aniline', 'methyl', 'azide', 'azo', 'barbitur', 'benzene', 'benzodiazepine', 'bicyclic', 'diazo', 'dihydropyridine', 'epoxide', 'ether', 'furan', 'guanido', 'halogen', 'hdrzine', 'hdrzone', 'imidazole', 'imide', 'isocyan', 'isothiocyan', 'ketone', 'Topliss', 'lactam', 'lactone', 'methoxy', 'morpholine', 'nitrile', 'nitro', 'arom', 'nonortho', 'nitroso', 'oxazole', 'oxime', 'hydroxylation', 'phenol', 'noOrthoHbond', 'acid', 'piperdine', 'piperzine', 'priamide', 'prisulfonamd', 'pyridine', 'quatN', 'sulfide', 'sulfonamd', 'sulfone', 'acetylene', 'tetrazole', 'thiazole', 'thiocyan', 'thiophene', 'alkane', 'urea']
# props = ['qed', 'SA', 'DRD2', 'JNK3', 'GSK3B', 'MolWt', 'MolLogP', 'BalabanJ', 'BertzCT', 'CHGHI', 'CHGLO', 'acetylene', 'tetrazole', 'thiazole', 'thiocyan']
with open('data/'+args.dataset+'.txt') as f:
train_smiles = [line.strip("\r\n ") for line in f]
prop_pred = cache_prop_pred()
prop_range_pd = pickle.load(open('data/zinc250k_range.pkl','rb'))
prop_range_pd = prop_range_pd.T
print(prop_range_pd.keys())
# prop_range_pd = pickle.load(open(f'{args.dataset}_range.pkl','rb'))
# prop_names = list(prop_pred.keys())
prop_names = ['qed']
print(prop_names)
prop_range = {}
for prop_name in prop_names:
prop_range[prop_name] = prop_range_pd[prop_name][1] - prop_range_pd[prop_name][0]
# base_dir = './'+args.dataset+'_'+args.baseline+'_manipulation_'+str(args.mani_range)
# files = os.listdir(base_dir)
base_dir = './'+args.dataset+'_'+args.baseline+'_manipulation_random_'
files = ['qed_plogp']
success_rate_strict = []
success_rate_soft = []
success_rate_soft_local = []
generated_all_smiles = []
record_message_strict = []
record_message_soft = []
record_message_soft_local = []
corr_coef_all = []
record_message_corr = []
for idx, fi in tqdm(enumerate(files)):
prop_name = fi
if prop_name in prop_pred:
prop_dir = os.path.join(base_dir,fi)
smiles = os.listdir(prop_dir)
success_strict = 0
success_soft = 0
success_soft_local = 0
corr_coef_result = []
for idx1 in range(args.num_samples):
one_smile = []
one_prop = []
for idx2 in range(args.path_len):
smiles_dir = prop_dir+'/smiles_'+str(idx1)+'_'+str(idx2)+'.npy'
try:
one_smile.append(np.load(smiles_dir).tolist())
except:
continue
generated_all_smiles.extend(one_smile)
one_prop = []
new_smiles = []
for smi in one_smile:
mol = Chem.MolFromSmiles(smi)
if mol is not None:
one_prop.append(prop_pred[prop_name](mol))
new_smiles.append(smi)
one_smile = new_smiles
one_prop = [v for v in one_prop if not (math.isinf(v) or math.isnan(v))]
if len(one_prop) < 2:
corr_coef = [0]
else:
corr_coef = stats.pearsonr(np.arange(len(one_prop)), one_prop)
corr_coef_result.append(np.abs(corr_coef[0]))
# strict test
if ((all(one_prop[idx] <= one_prop[idx+1] for idx in range(len(one_prop)-1)) or all(one_prop[idx] >= one_prop[idx+1] for idx in range(len(one_prop)-1))) and all(check_sim(new_smiles[idx],new_smiles[0]) <= check_sim(new_smiles[idx+1],new_smiles[0]) for idx in range(len(new_smiles)-1))) and len(set(one_smile)) != 1:
success_strict += 1
labels_for_success = ['{:.2f}'.format(label_score) for mol, label_score in zip(one_smile, one_prop)]
record_message_strict.append(str(success_strict)+' success '+str(success_strict)+'/'+str(idx1))
if len(one_prop) > 0:
prop_range_ = np.max(one_prop) - np.min(one_prop)
if (all(one_prop[idx] <= one_prop[idx+1]+args.epsilon*prop_range_ for idx in range(len(one_prop)-1)) or all(one_prop[idx]+args.epsilon*prop_range_ >= one_prop[idx+1] for idx in range(len(one_prop)-1))) and all(check_sim(new_smiles[idx],new_smiles[0]) <= check_sim(new_smiles[idx+1],new_smiles[0])+args.gamma for idx in range(len(new_smiles)-1)) and len(set(one_smile)) != 1:
success_soft_local += 1
labels_for_success = ['{:.2f}'.format(label_score) for mol, label_score in zip(one_smile, one_prop)]
record_message_soft_local.append(str(success_soft_local)+' success '+str(success_soft_local)+'/'+str(idx1))
# smile_slide = [Chem.MolFromSmiles(sms) for sms in one_smile]
# img = Draw.MolsToGridImage(smile_slide, legends=labels_for_success, molsPerRow=7,
# subImgSize=(200,200))
# if not os.path.exists(os.path.join(args.save_dir, args.dataset+'_boundaries_soft_'+str(args.mani_range)+'/'+fi)):
# os.makedirs(os.path.join(args.save_dir, args.dataset+'_boundaries_soft_'+str(args.mani_range)+'/'+fi))
# img.save(os.path.join(args.save_dir, args.dataset+'_boundaries_soft_'+str(args.mani_range)+'/'+fi+'/'+str(success_soft)+'_'+fi+'.png'))
# soft test
if (all(one_prop[idx] <= one_prop[idx+1]+args.epsilon*prop_range[prop_name] for idx in range(len(one_prop)-1)) or all(one_prop[idx]+args.epsilon*prop_range[prop_name] >= one_prop[idx+1] for idx in range(len(one_prop)-1))) and all(check_sim(new_smiles[idx],new_smiles[0]) <= check_sim(new_smiles[idx+1],new_smiles[0])+args.gamma for idx in range(len(new_smiles)-1)) and len(set(one_smile)) != 1:
success_soft += 1
labels_for_success = ['{:.2f}'.format(label_score) for mol, label_score in zip(one_smile, one_prop)]
record_message_soft.append(str(success_soft)+' success '+str(success_soft)+'/'+str(idx1))
# smile_slide = [Chem.MolFromSmiles(sms) for sms in one_smile]
# img = Draw.MolsToGridImage(smile_slide, legends=labels_for_success, molsPerRow=7,
# subImgSize=(200,200))
# if not os.path.exists(os.path.join(args.save_dir, args.dataset+'_boundaries_strict_'+str(args.mani_range)+'/'+fi)):
# os.makedirs(os.path.join(args.save_dir, args.dataset+'_boundaries_strict_'+str(args.mani_range)+'/'+fi))
# img.save(os.path.join(args.save_dir, args.dataset+'_boundaries_strict_'+str(args.mani_range)+'/'+fi+'/'+str(success_strict)+'_'+fi+'.png'))
success_rate_strict.append(success_strict*100/args.num_samples)
success_rate_soft.append(success_soft*100/args.num_samples)
success_rate_soft_local.append(success_soft_local*100/args.num_samples)
corr_coef_result_temp = np.array(corr_coef_result)
corr_coef_result_temp = np.nan_to_num(corr_coef_result_temp)
corr_coef_all.append(np.mean(corr_coef_result_temp))
record_message_corr.append(f'{fi} corr {corr_coef_all[-1]}')
record_message_strict.append(fi+' success '+str(success_rate_strict[-1]))
record_message_soft.append(fi+' success '+str(success_rate_soft[-1]))
record_message_soft_local.append(fi+' success '+str(success_rate_soft_local[-1]))
f = open(os.path.join(args.save_dir,args.dataset+'_'+args.baseline+'_'+str(args.mani_range)+'_qed_combined_corr.txt'),'w+')
for record in record_message_corr:
f.write(record+'\n')
f = open(os.path.join(args.save_dir,args.dataset+'_'+args.baseline+'_'+str(args.mani_range)+'_qed_combined_soft_global.txt'),'w+')
for record in record_message_soft:
f.write(record+'\n')
f = open(os.path.join(args.save_dir,args.dataset+'_'+args.baseline+'_'+str(args.mani_range)+'_qed_combined_strict.txt'),'w+')
for record in record_message_strict:
f.write(record+'\n')
f = open(os.path.join(args.save_dir,args.dataset+'_'+args.baseline+'_'+str(args.mani_range)+'_qed_combined_soft_local.txt'),'w+')
for record in record_message_soft_local:
f.write(record+'\n')
final_record = []
final_record.append('total soft global success rate ' + str(np.mean(success_rate_soft)))
final_record.append('total strict success rate ' + str(np.mean(success_rate_strict)))
final_record.append('total soft local success rate ' + str(np.mean(success_rate_soft_local)))
final_record.append('total corr ' + str(np.mean(corr_coef_all)))
validity, valid_mols = check_validity(generated_all_smiles)
final_record.append('validity ' + str(validity)+ '/'+ str(len(generated_all_smiles)))
novelty = check_novelty(valid_mols,train_smiles)
final_record.append('novelty ' + str(novelty) + '/' + str(len(generated_all_smiles)))
uniqueness = check_unique(valid_mols)
final_record.append('uniqueness ' + str(uniqueness) + '/' + str(len(generated_all_smiles)))
f = open(os.path.join(args.save_dir,args.dataset+'_'+args.baseline+'_'+str(args.mani_range)+'_qed_combined_final_result.txt'),'w+')
for final_r in final_record:
f.write(final_r+'\n')