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generate_scaffold[source]

generate_scaffold(smi:str, include_chirality:bool=False)

Compute the Bemis-Murcko scaffold for a SMILES string.

• parameters:
  • mol: A SMILES string.
  • include_chirality: Whether to include chirality.
• return: the SMILES of the scaffold

scaffold_to_smiles[source]

scaffold_to_smiles(data, use_indices:bool=False)

Computes scaffold for each smiles string and returns a mapping from scaffolds to sets of smiles.

• parameters:
  • mols: A list of SMILES strings.
  • use_indices: Whether to map to the smiles' index in all_smiles rather than mapping to the smiles string itself. This is necessary if there are duplicate smiles.
• return: A dictionary mapping each unique scaffold to all smiles (or smiles indices) which have that scaffold.

scaffold_split[source]

scaffold_split(data, sizes:Tuple[float, float, float]=(0.8, 0.1, 0.1), balanced:bool=True, seed:int=0)

Split a dataset by scaffold so that no molecules sharing a scaffold are in the same split.

• parameters:
  • data: A list of smiles.
  • sizes: A length-3 tuple with the proportions of data in the train, validation, and test sets.
  • balanced: Try to balance sizes of scaffolds in each set, rather than just putting smallest in test set.
  • seed: Seed for reproducitility.
• return: two tuples
  • (1) containing the train, validation, and test splits of the data (SMILES)
  • (2) containing the train, validation, and test splits of the index.

random_split[source]

random_split(smi_list, sizes:Tuple[float, float, float]=(0.8, 0.1, 0.1), seed:int=0, shuffle:bool=True)

Split a dataset randomly.

• parameters
  • data: A list of smiles.
  • sizes: A length-3 tuple with the proportions of data in the train, validation, and test sets.
  • seed: Seed for reproducitility.
• return: two tuples
  • (1) containing the train, validation, and test splits of the data (SMILES)
  • (2) containing the train, validation, and test splits of the index.

generate_folds[source]

generate_folds(df, split_type:str='random', num_folds:int=1, sizes:Tuple[float, float, float]=(0.8, 0.1, 0.1), seed:int=0)

Split a dataset and add the result as a column: 0 is train, 1 is valid and 2 is test.

• parameters:
  • data: a pandas dataframe
  • split_type: Choose from ['random','scaffold']. By default, the data will be splited randomly.
  • num_folds: the number of times to split the dataset.
  • sizes: A length-3 tuple with the proportions of data in the train, validation, and test sets.
  • seed: Seed for reproducitility.

Example 1: Generating Scaffolds from SMILES

smiles = ['O=C(O)c1ccc(C[S](=O)=O)cc1',
         'O=C(O)c1ccccc1',
          'N[C@H](CCC=O)C(=O)O',
          'N[C@@H](CCC=O)C(=O)O',
          'O=c1cccc[nH]1',
          'Oc1ccccn1',
          'Cc1cc(Oc2nccc(CCC)c2)ccc1',
          'COc1cc(OC)c(S(=O)(=O)N2c3ccccc3CCC2C)cc1NC(=O)CSCC(=O)O',
          'Nc1ccc(-c2nc3ccc(O)cc3s2)cc1',
          'O=C(O)c1cccc(N2CCC(CN3CCC(Oc4ccc(Cl)c(Cl)c4)CC3)CC2)c1',
          'CSc1c(C(=O)NC2C3CC4CC(C3)CC2C4)cnn1-c1ccc(C(=O)O)cc1'
         ]

scaffolds = []
for smi in smiles:
    scaffolds.append(generate_scaffold(smi))

import pandas as pd
from rdkit.Chem.Draw import IPythonConsole
from rdkit.Chem import PandasTools

data = pd.DataFrame({'smiles': smiles, 'scaffold': scaffolds})
PandasTools.AddMoleculeColumnToFrame(data, smilesCol='smiles', molCol='Mol (SMILES)')
PandasTools.AddMoleculeColumnToFrame(data, smilesCol='scaffold', molCol='Mol (Scaffold)')

data.iloc[:,2:]

	Mol (SMILES)	Mol (Scaffold)
0
1
2
3
4
5
6
7
8
9
10

Example 2: Collecting scaffolds from a list of SMILES

lipo_data = pd.read_csv('../clean_data/Lipophilicity.csv')
lipo_data.shape

(4200, 2)

lipo_data.head(1)

	smiles	exp
0	Cn1c(CN2CCN(c3ccc(Cl)cc3)CC2)nc2ccccc21	3.54

scaffolds = scaffold_to_smiles(lipo_data.smiles, use_indices=True)

counts = 0
for i in scaffolds.keys():
    if len(scaffolds[i]) ==1:
        counts +=1
print(f'There are {counts} ({counts/len(scaffolds):.2f} of the original data) scafoolds appearing only once.')

There are 1857 (0.77 of the original data) scafoolds appearing only once.

max_counts = 0
for i in scaffolds.keys():
    if len(scaffolds[i]) >= max_counts:
        max_counts = len(scaffolds[i])
        scaffold_max = i
print(f'The scaffold {scaffold_max} appears {max_counts} times, which is the most.')
Chem.MolFromSmiles(scaffold_max)

The scaffold c1ccccc1 appears 76 times, which is the most.

Example 3: Scaffold Split

splits, splits_index = scaffold_split(lipo_data.smiles, balanced=True, seed = 0)

Total scaffolds = 2,408 | train scaffolds = 1,915 | val scaffolds = 248 | test scaffolds = 245

train, val, test = splits
print(len(train), len(val), len(test))

3360 420 420

Example 4: Random Split

splits, splits_index = random_split(lipo_data.smiles, seed = 0)

train, val, test = splits
print(len(train), len(val), len(test))

3360 420 420

Example 5: Add multiple splits to a dataset.

lipo_splits = generate_folds(lipo_data, 'random', num_folds=10)
lipo_splits.head(1)

	smiles	exp	random-fold-0	random-fold-1	random-fold-2	random-fold-3	random-fold-4	random-fold-5	random-fold-6	random-fold-7	random-fold-8	random-fold-9
0	Cn1c(CN2CCN(c3ccc(Cl)cc3)CC2)nc2ccccc21	3.54	0	0	0	0	0	0	2	0	0	0

We can further add scaffold split to the dataset.

lipo_splits = generate_folds(lipo_splits, 'scaffold', num_folds=10)
lipo_splits.head(1)

Total scaffolds = 2,408 | train scaffolds = 1,951 | val scaffolds = 211 | test scaffolds = 246
Total scaffolds = 2,408 | train scaffolds = 1,947 | val scaffolds = 260 | test scaffolds = 201
Total scaffolds = 2,408 | train scaffolds = 1,887 | val scaffolds = 235 | test scaffolds = 286
Total scaffolds = 2,408 | train scaffolds = 1,984 | val scaffolds = 191 | test scaffolds = 233
Total scaffolds = 2,408 | train scaffolds = 1,906 | val scaffolds = 234 | test scaffolds = 268
Total scaffolds = 2,408 | train scaffolds = 2,041 | val scaffolds = 183 | test scaffolds = 184
Total scaffolds = 2,408 | train scaffolds = 1,894 | val scaffolds = 263 | test scaffolds = 251
Total scaffolds = 2,408 | train scaffolds = 1,907 | val scaffolds = 247 | test scaffolds = 254
Total scaffolds = 2,408 | train scaffolds = 1,917 | val scaffolds = 249 | test scaffolds = 242
Total scaffolds = 2,408 | train scaffolds = 1,905 | val scaffolds = 247 | test scaffolds = 256

	smiles	exp	random-fold-0	random-fold-1	random-fold-2	random-fold-3	random-fold-4	random-fold-5	random-fold-6	random-fold-7	...	scaffold-fold-0	scaffold-fold-1	scaffold-fold-2	scaffold-fold-3	scaffold-fold-4	scaffold-fold-5	scaffold-fold-6	scaffold-fold-7	scaffold-fold-8	scaffold-fold-9
0	Cn1c(CN2CCN(c3ccc(Cl)cc3)CC2)nc2ccccc21	3.54	0	0	0	0	0	0	2	0	...	0	0	0	0	0	0	2	0	0	0

1 rows × 22 columns