Create LCIdb and CV datasets in different prediction scenarii

%load_ext autoreload
%autoreload 2

import matplotlib.pyplot as plt
import numpy as np
from sklearn import svm
import pandas as pd

Preprocessing

Download the file ‘Consensus_CompoundBioactivity_Dataset_v1.1.csv’ from https://zenodo.org/record/6398019#.Y6A4nrKZPn4

from komet import process_LCIdb
LCIdb = process_LCIdb('Consensus_CompoundBioactivity_Dataset_v1.1.csv', data_dir = "./", max_length_fasta = 1000, bioactivity_choice = "checkand1database",min_weight = 100, max_weight = 900,  interaction_plus = 1e-7, interaction_minus = 1e-4)

Create CV in different prediction scenarii

• Random

• Unseen_drugs

• Unseen_targets

• Orphan

Load LCIdb

The dataset, with default parameters, can be downloaded in Zenodo https://zenodo.org/records/10731712 as LCIdb_v2.csv.

LCIdb_path = 'LCIdb_v2.csv'

LCIdb = pd.read_csv(LCIdb_path,low_memory=False)
LCIdb.head()

	smiles	fasta	ChEMBL ID	PubChem ID	IUPHAR ID	Ligand names	Target	uniprot	mean	mean pIC50	...	mean pKd	min	min pIC50	min pKi	min pKd	max	max pIC50	max pKi	max pKd	score
0	BrC(=C\c1ccccc1)/C=N/n1cnnc1	MNPTLILAAFCLGIASATLTFDHSLEAQWTKWKAMHNRLYGMNEEG...	CHEMBL3190095	6861939.0	NaN	sid24812872	ctsl	P07711	4.2	4.2	...	NaN	4.2	4.2	NaN	NaN	4.2	4.2	NaN	NaN	0.5
1	BrC(Cn1ncc2c(N3CCCC3)ncnc21)c1ccccc1	MGSNKSKPKDASQRRRSLEPAENVHGAGGGAFPASQTPSKPASADG...	CHEMBL382012	11617657.0	NaN	1-(2-bromo-2-phenylethyl)-4-(pyrrolidin-1-yl)-...	src	P12931	5.0	NaN	...	NaN	5.0	NaN	5.0	NaN	5.0	NaN	5.0	NaN	0.5
2	BrC(Cn1ncc2c(N3CCCCC3)ncnc21)c1ccccc1	MGSNKSKPKDASQRRRSLEPAENVHGAGGGAFPASQTPSKPASADG...	CHEMBL203922	11696703.0	NaN	1-(2-bromo-2-phenylethyl)-4-(piperidin-1-yl)-1...	src	P12931	5.8	NaN	...	NaN	5.8	NaN	5.8	NaN	5.8	NaN	5.8	NaN	0.5
3	BrC(Cn1ncc2c(NCc3ccccc3)ncnc21)c1ccccc1	MGSNKSKPKDASQRRRSLEPAENVHGAGGGAFPASQTPSKPASADG...	CHEMBL382153	11545984.0	NaN	n-benzyl-1-(2-bromo-2-phenylethyl)-1h-pyrazolo...	src	P12931	5.5	NaN	...	NaN	5.5	NaN	5.5	NaN	5.5	NaN	5.5	NaN	0.5
4	BrC1=CC2CNCCC2S1	MSGADRSPNAGAAPDSAPGQAAVASAYQRFEPRAYLRNNYAPPRGD...	CHEMBL398475	44447166.0	NaN	2-bromo-3a,4,5,6,7,7a-hexahydrothieno[3,2-c]py...	pnmt	P11086	5.9	NaN	...	NaN	5.9	NaN	5.9	NaN	5.9	NaN	5.9	NaN	0.5

5 rows × 21 columns

Compute CV folds

Loads the interaction data from a CSV file, preprocesses the data to generate numerical indices for unique smiles (molecules) and fasta (proteins), and splits the data into cross-validation training and testing datasets based on the specified split type.

Options are

• “full”

• “unseen_drug”

• “unseen_target”

• “Orphan”

from komet import make_CV_train_test

train_arr, test_arr = make_CV_train_test('LCIdb_v2.csv', "Orphan", "./", 5)

cpu
nombre de smiles:  271180
nombre de fasta:  2060

/Users/gguichaoua/Dropbox/gwenn/these/chemogenomique/komet/docs/source/vignettes/komet.py:2025: FutureWarning: In a future version of pandas all arguments of DataFrame.drop except for the argument 'labels' will be keyword-only.
  df = df.drop(indsmiles_index_with_nan,0)
/Users/gguichaoua/Dropbox/gwenn/these/chemogenomique/komet/docs/source/vignettes/komet.py:2028: FutureWarning: In a future version of pandas all arguments of DataFrame.drop except for the argument 'labels' will be keyword-only.
  df = df.drop(indfasta_index_with_nan,0)

matrice d'interactions:  (2060, 271180)
train (352100, 3)
nb of interactions + in test 43653
number of interactions + deleted in test 0
number of interactions + in test 43653
number of interactions - (7965, 2)
number of np.nan (557958247, 2)
i_end 474771903
number of interactions - in test 43653
test (87306, 3)
train (335638, 3)
nb of interactions + in test 46706
number of interactions + deleted in test 0
number of interactions + in test 46706
number of interactions - (7965, 2)
number of np.nan (557958247, 2)
i_end 491762422
number of interactions - in test 46706
test (93412, 3)
train (321062, 3)
nb of interactions + in test 50310
number of interactions + deleted in test 0
number of interactions + in test 50310
number of interactions - (7965, 2)
number of np.nan (557958247, 2)
i_end 360985565
number of interactions - in test 50310
test (100620, 3)
train (331310, 3)
nb of interactions + in test 47831
number of interactions + deleted in test 0
number of interactions + in test 47831
number of interactions - (7965, 2)
number of np.nan (557958247, 2)
i_end 467274895
number of interactions - in test 47831
test (95662, 3)
train (310338, 3)
nb of interactions + in test 52719
number of interactions + deleted in test 0
number of interactions + in test 52719
number of interactions - (7965, 2)
number of np.nan (557958247, 2)
i_end 445551151
number of interactions - in test 52719
test (105438, 3)
Train/test datasets prepared.
Train datasets prepared.

train = train_arr[0]
test = test_arr[0]

train.head()

cpu

	SMILES	Target Sequence	Label
0	BrC[C@H]1CC[C@H](c2nnn3cnc4[nH]ccc4c23)CC1	MGMACLTMTEMEGTSTSSIYQNGDISGNANSMKQIDPVLQVYLYHS...	1
1	BrC[C@H]1CC[C@H](c2nnn3cnc4[nH]ccc4c23)CC1	MQYLNIKEDCNAMAFCAKMRSSKKTEVNLEAPEPGVEVIFYLSDRE...	1
2	BrC[C@H]1CC[C@H](c2nnn3cnc4[nH]ccc4c23)CC1	MKTPWKVLLGLLGAAALVTIITVPVVLLNKGTDDATADSRKTYTLT...	0
3	BrC[C@H]1CC[C@H](c2nnn3cnc4[nH]ccc4c23)CC1	MLEICLKLVGCKSKKGLSSSSSCYLEEALQRPVASDFEPQGLSEAA...	0
4	Brc1cc(CN2CCC(c3nnn4cnc5[nH]ccc5c34)CC2)sc1Br	MGMACLTMTEMEGTSTSSIYQNGDISGNANSMKQIDPVLQVYLYHS...	1

print("number of drugs in train", train['SMILES'].nunique())
print("number of drugs in test", test['SMILES'].nunique())

print("*"*50)
print("number of proteins in train", train['Target Sequence'].nunique())
print("number of proteins in test", test['Target Sequence'].nunique())

number of drugs in train 135084
number of drugs in test 62676
**************************************************
number of proteins in train 1180
number of proteins in test 880

print("number of interactions in train")
train.value_counts('Label')

number of interactions in train

Label
0    176050
1    176050
dtype: int64

print("number of interactions in test")
test.value_counts('Label')

number of interactions in test

Label
1    43653
0    43652
dtype: int64