-
Notifications
You must be signed in to change notification settings - Fork 3
/
sort_and_slice_ecfp_featuriser.py
133 lines (85 loc) · 9.03 KB
/
sort_and_slice_ecfp_featuriser.py
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
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
import numpy as np
from rdkit.Chem import rdFingerprintGenerator
def create_sort_and_slice_ecfp_featuriser(mols_train,
max_radius = 2,
pharm_atom_invs = False,
bond_invs = True,
chirality = False,
sub_counts = True,
vec_dimension = 1024,
break_ties_with = lambda sub_id: sub_id,
print_train_set_info = True):
"""
Creates a function "ecfp_featuriser" that maps RDKit mol objects to vectorial extended-connectivity fingerprints (ECFPs) pooled via a trained Sort & Slice operator (instead of classical hash-based folding).
See also "Sort & Slice: A Simple and Superior Alternative to Hash-Based Folding for Extended-Connectivity Fingerprints" by Dablander, Hanser, Lambiotte and Morris (2024): https://arxiv.org/abs/2403.17954
INPUTS:
- mols_train (list) ... A list of RDKit mol objects [mol_1, mol_2, ...] that are used as the training set to calibrate the Sort & Slice substructure pooling operator.
- max_radius (int) ... The maximal radius up to which to generate the integer ECFP-substructure identifiers. Common choices are 1, 2 or 3 (corresponding to maximal diameters of 2, 4, or 6).
- pharm_atom_invs (bool) ... If False (= default), then the standard initial atomic invariants from RDKit (including ring membership) are used to generate the ECFPs.
If True, then instead binary pharmacophoric initial atomic invariants are used to generate a different type of ECFP also referred to as FCFPs.
- bond_invs (bool) ... Whether or not to take into account bond invariants when generating the integer ECFP-substructure identifiers (default = True).
- chirality (bool) ... Whether or not to take into account chirality when generating the integer ECFP-substructure identifiers (default = False).
- sub_counts (bool) ... Whether ecfp_featuriser should generate binary vectorial fingerprints (sub_counts = False) that indicate the mere presence or absence of substructures;
or integer fingerprints (sub_counts = True) that additionally indicate how many times a substructure is found in the input compound.
- vec_dimension (int) ... Length of the vectorial Sort & Slice ECFP. Common choices are 512, 1024, 2048 and 4096.
Only the vec_dimension most prevalent ECFP-substructures in the training set mols_train are included in the final vector representation.
- break_ties_with (function) ... Function to map the integer ECFP-substructure identifiers to values that are used to break ties when sorting the substructure identifiers according to their prevalence in mols_train.
The default is the identity map (i.e., lambda sub_id: sub_id) which breaks ties using the (arbitrary) ordering defined by the integer substructure identifier themselves.
If break_ties_with = None, then ties are broken automatically as part of the application of Python's sorted() command to sub_ids_to_prevs_dict.
- print_train_set_info (bool) ... Whether or not to print out the number of compounds and the number of unique integer ECFP-substructure identifiers with the specified parameters in mols_train.
OUTPUT:
- ecfp_featuriser (function) ... A function that maps RDKit mol objects to vectorial ECFPs (1-dimensional NumPy arrays of length vec_dimension) via a Sort & Slice substructure pooling operator trained on mols_train.
EXAMPLE:
First select a training set of RDKit mol objects
mols_train = [mol_1, mol_2, ...]
that should be used to calibrate the Sort & Slice operator. This training set can then be employed along with a set of desired ECFP hyperparameter settings to construct a molecular featurisation function:
ecfp_featuriser = construct_sort_and_slice_ecfp_featuriser(mols_train = mols_train,
max_radius = 2,
pharm_atom_invs = False,
bond_invs = True,
chirality = False,
sub_counts = True,
vec_dimension = 1024)
Then ecfp_featuriser(mol) is a 1-dimensional numpy array of length vec_dimension representing the vectorial ECFP for mol pooled via a Sort & Slice operator calibrated on mols_train.
More specifically, the function ecfp_featuriser can be thought of as
1. first generating the (multi)set of integer ECFP-substructure identifiers for mol based on the ECFP hyperparameters (max_radius, pharm_atom_invs, bond_invs, chirality, sub_counts) and then
2. vectorising this (multi)set via a Sort & Slice operator calibrated on mols_train with output dimension vec_dimension (rather than vectorising it via classical hash-based folding).
To now turn any list of RDKit mol objects mols_list into a feature matrix X whose rows correspond to vectorial Sort & Slice ECFPs one can simply run
X = np.array([ecfp_featuriser(mol) for mol in mols_list])
"""
# create a function sub_id_enumerator that maps a mol object to a dictionary whose keys are the integer substructure identifiers in mol and whose values are the associated substructure counts (i.e., how often each substructure appears in mol)
morgan_generator = rdFingerprintGenerator.GetMorganGenerator(radius = max_radius,
atomInvariantsGenerator = rdFingerprintGenerator.GetMorganFeatureAtomInvGen() if pharm_atom_invs == True else rdFingerprintGenerator.GetMorganAtomInvGen(includeRingMembership = True),
useBondTypes = bond_invs,
includeChirality = chirality)
sub_id_enumerator = lambda mol: morgan_generator.GetSparseCountFingerprint(mol).GetNonzeroElements()
# construct dictionary that maps each integer substructure identifier sub_id in mols_train to its associated prevalence (i.e., to the total number of compounds in mols_train that contain sub_id at least once)
sub_ids_to_prevs_dict = {}
for mol in mols_train:
for sub_id in sub_id_enumerator(mol).keys():
sub_ids_to_prevs_dict[sub_id] = sub_ids_to_prevs_dict.get(sub_id, 0) + 1
# create list of integer substructure identifiers sorted by prevalence in mols_train
sub_ids_sorted_list = sorted(sub_ids_to_prevs_dict, key = lambda sub_id: (sub_ids_to_prevs_dict[sub_id], break_ties_with(sub_id)), reverse = True)
# create auxiliary function that generates standard unit vectors in NumPy
def standard_unit_vector(dim, k):
vec = np.zeros(dim, dtype = int)
vec[k] = 1
return vec
# create one-hot encoder for the first vec_dimension substructure identifiers in sub_ids_sorted_list; all other substructure identifiers are mapped to a vector of 0s
def sub_id_one_hot_encoder(sub_id):
return standard_unit_vector(vec_dimension, sub_ids_sorted_list.index(sub_id)) if sub_id in sub_ids_sorted_list[0: vec_dimension] else np.zeros(vec_dimension)
# create a function ecfp_featuriser that maps RDKit mol objects to vectorial ECFPs via a Sort & Slice substructure pooling operator trained on mols_train
def ecfp_featuriser(mol):
# create list of integer substructure identifiers contained in input mol object (multiplied by how often they are structurally contained in mol if sub_counts = True)
if sub_counts == True:
sub_id_list = [sub_idd for (sub_id, count) in sub_id_enumerator(mol).items() for sub_idd in [sub_id]*count]
else:
sub_id_list = list(sub_id_enumerator(mol).keys())
# create molecule-wide vectorial representation by summing up one-hot encoded substructure identifiers
ecfp_vector = np.sum(np.array([sub_id_one_hot_encoder(sub_id) for sub_id in sub_id_list]), axis = 0)
return ecfp_vector
# print information on training set
if print_train_set_info == True:
print("Number of compounds in molecular training set = ", len(mols_train))
print("Number of unique circular substructures with the specified parameters in molecular training set = ", len(sub_ids_to_prevs_dict))
return ecfp_featuriser