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
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
|
//
// Copyright (C) 2017 Novartis Institutes for BioMedical Research
//
// @@ All Rights Reserved @@
// This file is part of the RDKit.
// The contents are covered by the terms of the BSD license
// which is included in the file license.txt, found at the root
// of the RDKit source tree.
//
#ifndef RGROUP_SCORE_H
#define RGROUP_SCORE_H
#include "RGroupMatch.h"
#include <vector>
#include <deque>
#include <set>
namespace RDKit {
//! iterate through all possible permutations of the rgroups
struct CartesianProduct {
std::vector<size_t> permutation;
std::vector<size_t> sizes;
std::deque<size_t> bases;
size_t maxPermutations;
size_t permutationCount;
CartesianProduct(const std::vector<size_t> &inputSizes)
: permutation(inputSizes.size(), 0),
sizes(inputSizes),
permutationCount(0) {
maxPermutations = 1;
for (unsigned long size : sizes) {
bases.push_front(maxPermutations);
maxPermutations *= size; // may overflow....
}
}
bool next() {
++permutationCount;
if (permutationCount == 1) {
return true;
}
return increment(0);
}
size_t value(const std::vector<size_t> &p) const {
size_t v = 0;
for (size_t i = 0; i < p.size(); ++i) {
v += bases[i] * p[i];
}
return v;
}
size_t value() { return value(permutation); }
bool increment(size_t rowToIncrement) {
if (permutationCount > maxPermutations) {
return false;
}
permutation[rowToIncrement] += 1;
size_t max_index_of_row = sizes[rowToIncrement] - 1;
if (permutation[rowToIncrement] > max_index_of_row) {
permutation[rowToIncrement] = 0;
return increment(rowToIncrement + 1);
}
return true;
}
};
class RDKIT_RGROUPDECOMPOSITION_EXPORT RGroupScorer {
public:
RGroupScorer() {};
RGroupScorer(const std::vector<std::vector<size_t>> &permutations,
double score);
//! score the passed permutation of matches
double matchScore(const std::vector<size_t> &permutation,
const std::vector<std::vector<RGroupMatch>> &matches,
const std::set<int> &labels);
//! set the passed permutation and score as the best one
void setBestPermutation(const std::vector<size_t> &permutation, double score);
//! return the best permutation found so far
const std::vector<size_t> &getBestPermutation() const {
return d_saved.permutation;
}
//! called when process() starts to initialize State
void startProcessing();
//! store the passed tied permutation for subsequent processing
void pushTieToStore(const std::vector<size_t> &permutation);
//! find the best permutation across the tied ones that were stored
void breakTies(const std::vector<std::vector<RGroupMatch>> &matches,
const std::set<int> &labels,
const std::unique_ptr<CartesianProduct> &iterator,
const std::chrono::steady_clock::time_point &t0,
double timeout);
//! clear all stored tied permutations
void clearTieStore();
//! number of stored tied permutations
size_t tieStoreSize() const { return d_store.size(); }
//! return the best score found so far
double getBestScore() const { return d_bestScore; }
private:
void restoreInitialState() { d_current = d_initial; }
struct RLabelData {
int numRGroups = 0;
std::vector<std::map<std::string, unsigned int>> matchSetVect;
std::map<std::set<int>, size_t> linkerMatchSet;
};
// The State structure stores the state of the RGroupScorer
// This allows more efficient scoring of permutations, in that
// the score of pruned permutations, which are effectively frozen,
// are cached in the State rather than being recomputed on-the-fly
// while only permutations in the last chunk are actually scored
struct State {
// compute the criteria according to which the best
// permutation is found across the tied ones
void computeTieBreakingCriteria(
const std::vector<std::vector<RGroupMatch>> &matches,
const std::vector<int> &orderedLabels, std::vector<int> &heavyCounts) {
// heavyCounts is a vector which has the same size of labels
// for each label we add an increment if a molecule
// bears an R-group at that label
PRECONDITION(permutation.size() <= matches.size(),
"permutation.size() should be <= matches.size()");
size_t offset = matches.size() - permutation.size();
// numMatchedUserRGroups counts the total number of user labelled r
// groups filled in this permutation. We want to maximize this number
size_t i = 0;
for (int label : orderedLabels) {
for (size_t m = 0; m < permutation.size(); ++m) { // for each molecule
// Negative labels are assigned to R-groups that were found along
// the way (when onlyMatchAtRGroups=false) rather than being
// user-specified. For each molecule, check if we add an R-group at
// this negative label; if we do, count it once. So we know how many
// different negative labels we have filled: we prefer permutations
// which fill less, as it means we have added less groups on different
// positions
const auto &match = matches[m + offset][permutation[m]];
auto rg = match.rgroups.find(label);
if (rg != match.rgroups.end() && !rg->second->is_hydrogen) {
if (label < 0 && heavyCounts.at(i) == 0) {
++numAddedRGroups;
} else if (label > 0) {
++numMatchedUserRGroups;
}
++heavyCounts[i];
}
}
++i;
}
}
int N = 0;
int numAddedRGroups = 0;
int numMatchedUserRGroups = 0;
std::map<int, int> heavyCountPerLabel;
std::map<int, RLabelData> labelDataMap;
std::vector<size_t> permutation;
};
double d_bestScore = 0.0;
// the current State
State d_current;
// the initial state when process() is called
State d_initial;
// the best State found so far
State d_saved;
// the States associated to each tied permutation
std::deque<State> d_store;
};
} // namespace RDKit
#endif
|
