/usr/share/RDKit/Contrib/pzc/readme.md is in rdkit-data 201403-1.
This file is owned by root:root, with mode 0o644.
The actual contents of the file can be viewed below.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 | Documentation for p_con (https://github.com/pzc/rdkit/blob/master/Contrib/pzc/p_con.html)
from p_con import p_con
pco = p_con("P43088")
pco.verbous = True
pco.step_0_get_chembl_data() # Download Compounds for P43088 from ChEMBL
(or pco.load_mols("sdf-file.sdf.gz"))
pco.step_1_keeplargestfrag() # remove small Fragments from compounds
pco.step_2_remove_dupl() # remove duplicate-Entries
pco.step_3_merge_IC50() # merge IC50 from Entries with same canonical smiles into one compound
pco.step_4_set_TL(4000,ic50_tag="value") # set TrafficLights, value > 4000nm: 0, else 1
pco.step_5_remove_descriptors() # remove Descriptors from compounds
pco.step_6_calc_descriptors() # calculate new Descriptors which are used to create prediction-models
pco.step_7_train_models() # train up to 10 models
pco.save_model_info("model_info.csv",mode="csv") # create csv with data for each model
pco.save_model_info("model_info.html",mode="html") # create html -#-
for i in range(len(pco.model)):
pco.save_model("model_%d.pkl" % i,i)
for i in range(len(pco.model)):
act,inact = pco.predict(i)
print "Model %d active: %d\tinactive: %d" % (i,act,inact)
# to Check compounds using Models
pco2 = p_con("P43088")
pco2.verbous = True
pco2.load_mols("P43088.sdf.gz")
models = ["model1.pkl","model2.pkl"]
pco2.load_models(models)
print "\n#Model\tActive\tInactive"
for i in range(len(self.model)):
act,inact = pco2.predict(i)
print "%d\t%d\t%d" % (i,act,inact)
|