Classification of Cyclooxygenase-2 Inhibitors using Support Vector

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Computational Chemistry

Classification of Cyclooxygenase-2 Inhibitors using Support Vector Machine and Random Forest Methods Zijian Qin, Yao Xi, Shengde Zhang, Guiping Tu, and Aixia Yan J. Chem. Inf. Model., Just Accepted Manuscript • DOI: 10.1021/acs.jcim.8b00876 • Publication Date (Web): 14 Feb 2019 Downloaded from http://pubs.acs.org on February 14, 2019

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Journal of Chemical Information and Modeling

Classification of Cyclooxygenase-2 Inhibitors using Support Vector Machine and Random Forest Methods Zijian Qin, Yao Xi, Shengde Zhang, Guiping Tu, Aixia Yan*

State Key Laboratory of Chemical Resource Engineering, Department of Pharmaceutical Engineering, P.O. Box 53, Beijing University of Chemical Technology, 15 BeiSanHuan East Road, Beijing 100029, P. R. China. Tel: +86-10-64455320 Fax: +86-10-64416428 E-mail: [email protected]

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Abstract This work reports the classification study conducted on the biggest COX-2 inhibitor dataset so far. Using 2925 diverse COX-2 inhibitors collected from 168 literature, we applied machine learning methods, support vector machine (SVM) and random forest (RF) to develop twelve classification models. The best SVM and RF models resulted in MCC values of 0.73 and 0.72, respectively. The 2925 COX-2 inhibitors were reduced to a dataset of 1630 molecules by removing intermediately active inhibitors and twelve new classification models were constructed, yielding MCC values above 0.72. The best MCC value of the external test set was predicted to be 0.68 by the RF model using ECFP_4 fingerprints. Moreover, the 2925 COX-2 inhibitors were clustered into eight subsets, and the structural features of each subset were investigated. We identified substructures important for activity including halogen, carboxyl, sulfonamide, and methanesulfonyl groups, as well as the aromatic nitrogen atoms. The models developed in this study could serve as useful tools for compound screening prior to lab tests.

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1. Introduction Cyclooxygenase (COX) is an enzyme that catalyzes the synthesis of prostaglandins from the substrate arachidonic acid (AA) and is also a target of Nonsteroidal Anti-Inflammatory Drugs (NSAIDs). COX-2 is an inducible form of the COX enzyme while COX-1 is considered as a “housekeeper” molecule.1 In most tissues, COX-2 was either detected at very low levels or undetectable, but COX-2 was induced to high expression in inflamed tissue by inflammatory mediators.1,2 Due to several side effects occurring during the inhibition of COX-1, such as gastric ulceration, bleeding and perforation, researchers have been increasingly focusing on selective COX-2 inhibitors.3–5 However, COX-2 inhibitors showed side effects in cardiovascular terms, such as arterial hypertension, heart failure, and stroke, as well as in the gastrointestinal system.6 The developments of novel COX-2 inhibitors is still important because they might have benefits on the therapies of cancer progression7–10 and oxidative stress.11 To date, five drugs targeting COX-2 have been approved by the U.S. Food and Drug Administration (FDA)12; however, two of them were discontinued (Table 1). In particular, CONSENSI, with a new combination of amlodipine besylate and celecoxib, was the most recently approved drug (2018). This drug was used to treat hypertension and osteoarthritis in adult patients. Several clinical agents acting as COX-2 inhibitors are shown in Table 2. Celecoxib, valdecoxib, and rofecoxib were old drugs gaining new use. These old drugs were used to treat various cancers, such as head and neck cancer, cervical, lung, and breast cancer, as well as to other conditions including hypertension, coronary artery disease, and duodenal polyps.

Table 1. Approved drugs as COX-2 inhibitors and their marketing status. Drug Name

Active Ingredients

Marketing Status

BEXTRA

Valdecoxib

Discontinued

IC50 = nM13

183

CELEBREX

Celecoxib

Prescription

IC50 = nM14

50

Molecular Structures

3


Activity Value


CELECOXIB

Celecoxib

CONSENSI

Amlodipine Celecoxib

VIOXX

Rofecoxib

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Prescription

besylate;

Prescription

IC50 = nM15

Discontinued

211

Table 2. Clinical agents as COX-2 inhibitors and their research progress in clinical trials. Drug Name

Conditions

Phases

Ossification, heterotopic

Phase 4

Molecular Structures

IC50 = 81 nM16

Etoricoxib

Parecoxib sodium

HIV

Phase 2

Post-operative pain

Phase 4

Musculoskeletal pain

Phase 4

Prodrug to valdecoxib

IC50 = 130 nM17

Lumiracoxib

Cimicoxib

Activity Value

Osteoarthritis, knee

Phase 3

Major depression

Phase 2

IC50 = 66 nM18

Quantitative structure-activity relationship (QSAR) studies are a useful tool from computational chemistry to predict the structural requirements for biological activity. Based on the types of predicted properties, QSAR models can be classified into qualitative prediction (classification) models and 4


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quantitative prediction (regression) models. For a series of compounds, classification models can unravel the relationships between structural features and their biochemical properties, and then predict the properties of new compounds based on this knowledge. So far, several QSAR models that focused on a small number of selective COX-2 inhibitors with the same scaffold have been reported. Dwivedi et al. built QSAR models based on 10 COX-2 inhibitors with the N-1 and C-3 substituted indole Schiff bases scaffold, with a coefficient of determination (i.e. R2) of the best model of 0.8287.19 Liu et al. built several classification models of 85 selective COX-2 inhibitors with the 1, 5-diarylimidazoles scaffold using a support vector machine method. The accuracies of the training and test sets of the best classification models were 91.2% and 88.2%, respectively.20 Soltani et al. performed QSAR analyses on 54 COX-2 inhibitors with the trans-stilbenoid diaryl scaffold using a multiple linear regression method and a partial least square method. The root of R2 (i.e., R) of their best model was 0.91.21 Dawood et al. built QSAR models on 21 COX-2 inhibitors of coumarin derivatives, and the R2 of the best model was 0.908.22 Yadav et al. performed Gaussian-based 3D-QSAR studies on 58 selective COX-2 inhibitors with the benzopyran scaffold, with an R2 of the training and test sets of 0.917 and 0.807, respectively. They also performed molecular docking simulations and protein–ligand interaction pattern analyses.3 From 2016 to 2018, a number of 2D-23–25 and 3D-QSAR studies26–30 on small datasets with different COX-2 inhibitor scaffolds, such as dihydropyridine and hydroquinoline derivatives,23 indole derivatives,24 1,5-disubstituted tetrazoles,25 hydroquinoline and thiazinan-4-one derivatives,26 resveratrol derivatives,27,29 and cis-stilbene derivatives30 have been reported. In addition, several molecular docking studies25,31 and molecular dynamics simulations32 were also carried out. However, few classification studies were reported on a large number of COX-2 inhibitors. In the present work, a large number of COX-2 inhibitors (2925 molecules) with more diverse scaffolds than previous studies has been used to develop a series of classification models. In addition, we clustered these COX-2 inhibitors into several subsets and investigated the structural features of each subset. Several QSAR models based on COX-1 inhibitors were published in our previous work.33 The goal of the present work is to develop classification models for categorizing COX-2 inhibitors into highly or weakly 5



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active inhibitors. We envision these models serving as practical screening tools for those researchers that are actively synthesizing new compounds.

2. Materials and Methods There are three main steps in developing a classification model: data collection, descriptors calculation and selection, and model development and validation. 2.1 Data collection Dataset 1. We collected the molecular structures and their IC50 values of 2925 COX-2 inhibitors of human from ChEMBL34 and 168 literature references 5,13–18,35–195 to build a dataset. The IC50 values ranged from 1000 μΜ to 0.02 nM. As the 168 references were derived from different authors and the IC50 values were tested by different assays, there could be biases in the IC50 values of all 2925 inhibitors. Therefore, a threshold of 1 μM was used to classify these inhibitors into highly/weakly active COX-2 inhibitors, based on the assumption that if an inhibitor was a highly active in a particular testing assay, it could also perform as a highly active inhibitor in other testing assays. This assumption enabled a threshold to reduce the bias among different testing assays. The reasons for choosing a threshold of 1 μM were: (i) to balance the number of highly and weakly active COX-2 inhibitors in the dataset. With the threshold of 1 μM, the ratio of highly and weakly active COX-2 inhibitors was close to 1:1. (ii) models were built with thresholds of 1 μM and 10 μM, and a better model performance with 1 μM was obtained. (iii) Literature showed that specific activity values were no longer measured when IC50 value neared to 1 μM (Almansa et al.18 and Singh et al. 180). A compound whose IC50 value was less or equal to 1 μΜ was regarded as a highly active inhibitor and represented as ‘1’, while a compound whose IC50 value was greater than 1 μΜ was regarded as a weakly active inhibitor and represented by ‘0’. In this way, the 2925 COX-2 inhibitors were divided into 1266 highly and 1659 weakly active inhibitors. Subsequently, we used two stratified splitting methods, a random approach and a Kohonen selforganizing map (SOM), to divide the dataset into a training and a test set. For the random splitting method, both the highly and weakly active inhibitors were respectively divided into training and test sets by a 4:1 ratio using the function StratifiedSplit of the python toolkit scikit-learn.196 The SOM method was carried 6


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out by the program SONNIA.197 For the SOM splitting method, all the inhibitors were first clustered into the neurons of a Kohonen self-organizing map. The stratified splitting method (described above) was carried out for the inhibitors in each of the neuron. The random method dealt with 2340 inhibitors in the training set (1032 highly and 1308 weakly active inhibitors) and 585 inhibitors in the test set (234 highly and 351 weakly active inhibitors). The SOM method dealt with 2362 inhibitors in the training set (1000 highly and 1362 weakly active inhibitors) and 563 inhibitors in the test set (266 highly and 297 weakly active inhibitors). In an ideal splitting, the chemical space of the training set should overlap that of the test set. Therefore, we performed a scatter plot of ‘Weight’ (i.e., molecular weight) versus ‘XlogP’ based on the training and test sets obtained from the two splitting methods to evaluate their chemical space (Figure 1). Figure 1 confirms that the chemical space of the training set obtained from the two splitting methods basically covers that of the test set. The training and test sets obtained by the SOM splitting method are more evenly distributed and are slightly better than by the random splitting method.

Figure 1. Scatter plots of ‘Weight’ (i.e., molecular weight) versus ‘XlogP’ based on the training and test sets, (a) for the random splitting method; (b) for the Kohonen self-organizing map (SOM) splitting method. 7



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Dataset 2. Based on dataset 1, we further divided the 2925 COX-2 inhibitors into three groups followed by a new threshold rule: highly active (≤ 0.1 μM), intermediately active (> 0.1 μM and < 10 μM), and weakly active (≥ 10 μM). The highly and weakly active inhibitors were retained to form dataset 2, while the intermediately active inhibitors were removed. This resulted in 1630 COX-2 inhibitors in dataset 2: 667 highly and 963 weakly active inhibitors. In order to keep the consistence of the training and test sets, the intermediately active inhibitors of the training and test sets from the dataset 1 were also removed to obtain the training and test sets of dataset 2. As a result, for the random splitting method, there were 1299 inhibitors in the training set (536 highly and 763 weakly active inhibitors) and 331 inhibitors in the test set (131 highly and 200 weakly active inhibitors). For the SOM splitting method, there were 1309 inhibitors in the training set (525 highly and 784 weakly active inhibitors) and 321 inhibitors in the test set (142 highly and 179 weakly active inhibitors). Dataset of an external test set. We collected 259 COX-2 inhibitors from newly reported work198–217 to build an external test set, which included 44 highly active inhibitors (IC50 ≤ 0.1 μM) and 215 weakly active inhibitors (IC50 ≥ 10 μM). All these 259 inhibitors were different from the 2925 inhibitors in dataset 1. Dataset of decoys. In addition, 13155 decoys were collected from DUD218 to validate the performances of the classification models. 2.2 Descriptors calculation and selection All the inhibitors and the decoys were represented by two types of descriptors: fingerprints, including MACCS fingerprints and ECFP_4 fingerprints, and CORINA descriptors. Fingerprints represent the sub-structural features or fragments of compounds. For a particular substructure, "1" or "0" is used to indicate the presence or absence of the substructure. In this work, we calculated 166 bits MACCS fingerprints and 1024 bits ECFP_4 fingerprints by using the python toolkit RDKit.219

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CORINA descriptors represent the structural, topological, geometrical, electrostatic, and other physicochemical features of compounds. The program CORINA Symphony220 provides six main categories of molecular descriptors: global molecular descriptors, size and shape descriptors, 2D property-weighted autocorrelation, 3D property-weighted autocorrelation, property-weighted radial distribution functions (RDF), and autocorrelation of surface properties. In this work, we calculated 22 global molecular descriptors and 96 3D property-weighted autocorrelation by using the program CORINA Symphony. These 118 descriptors were initially selected by the Pearson correlation coefficient between each descriptor and the activity.33 Briefly, if the absolute Pearson correlation coefficient between the descriptor and the activity was less than 0.1, this descriptor will be removed; if the absolute Pearson correlation coefficient between two descriptors was greater than 0.9, i.e., two descriptors with high inner correlations, one of the descriptors will be removed. The remaining descriptors were sorted by their information gain (IG) values using the program Weka.221 For dataset 1, the top 17 descriptors, containing 4 global molecular descriptors and 13 3D property-weighted autocorrelation values, were selected to develop classification models. For dataset 2, the top 14 descriptors, containing 4 global molecular descriptors and 10 3D property-weighted autocorrelation, were selected to develop classification models. The inner Pearson correlation coefficients of the top 14 descriptors selected for dataset 2 are shown in Table 3. Before training, the selected descriptors were scaled to a [0.1, 0.9] range. Table 3. The inner Pearson correlation coefficients of the top 14 descriptors selected by the dataset 2. Activity

D1

D2

D3

D4

D5

D6

D7

D8

D1

-0.21

1.00

D2

0.11

-0.24

1.00

D3

0.17

-0.60

-0.05

1.00

D4

0.19

-0.38

0.86

-0.02

1.00

D5

0.18

-0.25

0.84

-0.14

0.87

1.00

D6

-0.26

0.18

-0.56

0.06

-0.55

-0.71

1.00

D7

0.26

-0.59

0.60

0.14

0.66

0.59

-0.53

1.00

D8

0.13

-0.03

0.83

-0.27

0.80

0.77

-0.51

0.45

1.00

D9

D10

D11

D12

D13

D9

0.16

-0.30

0.84

-0.07

0.86

0.83

-0.48

0.57

0.70

1.00

D10

0.16

-0.34

0.87

-0.05

0.86

0.84

-0.57

0.79

0.75

0.82

1.00

D11

-0.21

0.75

-0.40

-0.31

-0.56

-0.45

0.08

-0.55

-0.22

-0.60

-0.51

1.00

D12

0.24

-0.31

0.77

-0.03

0.81

0.81

-0.77

0.71

0.76

0.62

0.77

-0.28

1.00

D13

0.22

0.03

0.32

0.00

0.23

0.42

-0.90

0.24

0.29

0.18

0.28

0.19

0.51

1.00

D14

0.22

-0.59

0.64

0.13

0.66

0.57

-0.48

0.88

0.45

0.65

0.69

-0.60

0.63

0.18

D1: 3DACorr:PiChg:Cor3D:ori1_1; D2: 3DACorr:Ident:Cor3D:ori1_4; D3: 3DACorr:PiChg:Cor3D:ori1_2; D4: Complex; D5: Weight; D6: LogS; D7: 3DACorr:PiEN:Cor3D:ori1_6; D8: 3DACorr:Ident:Cor3D:ori1_3; D9: 9



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3DACorr:SigEN:Cor3D:ori1_2; D10: 3DACorr:SigEN:Cor3D:ori1_6; D11: 3DACorr:TotChg:Cor3D:ori1_1; D12: 3DACorr:Polariz:Cor3D:ori1_1; D13: XlogP; D14: 3DACorr:PiEN:Cor3D:ori1_4

2.3 Machine learning method Two machine learning methods were used for building classification models: a support vector machine (SVM)222 method and a random forest (RF)223 method. The SVM is a powerful machine learning method developed by Vapnik222 to deal with non-linear problems for classification. In brief, an SVM uses is that carries out a kernel function for mapping the input features to a high-dimensional feature space and a hyperplane of separation is constructed in this feature space. The RF is a widely used QSAR method due to its high prediction accuracies, ease of use, and robustness to adjustable parameters. The RF method has become something like a “gold standard” for the comparison with other QSAR methods.224 2.4 Model validation The performances of classification models were evaluated by the following metrics: accuracy (Q), cross validated (5-CV, 10-CV, and leave-one-out) accuracies, true positive (TP), true negative (TN), false positive (FP), false negative (FN), sensitivity (SE), specificity (SP), and Matthews correlation coefficient (MCC).

3. Results and Discussion Based on the two splitting methods, two threshold rules, three types of descriptors, and two machine learning methods, 24 classification models were built: 12 SVM models (models 1A, 1B, 1C, 1D, 1E, 1F, 1A-S, 1B-S, 1C-S, 1D-S, 1E-S, 1F-S) and 12 RF models (models 2A, 2B, 2C, 2D, 2E, 2F, 2A-S, 2B-S, 2C-S, 2D-S, 2E-S, 2F-S). The parameter optimization, model development and evaluation of all these 24 classification models were carried out by using the python toolkit scikit-learn.196 3.1 SVM models An SVM model can be determined by three parameters: a kernel function, a kernel coefficient, and a penalty parameter. In this work, in terms of kernel function, all the SVM models were built with a radial based function (rbf). The penalty parameter C and the kernel coefficient gamma were optimized by a grid search process. Briefly, the C and gamma values obtained from the best 10-fold cross validated grid search 10


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models with the highest MCC values, were used as optimum parameters for modeling. The optimum parameters of the twelve SVM models and their performances are shown in Table 4.

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Table 4. Parameters and performances of the twelve classification models built by a support vector machine. Training

Input Descriptors

/ test seta

Typeb

nc

Cd

gammae

Q (%)f

5-CV (%)g

10-CV (%)h

LOO (%)i

SE (%)j

SP (%)k

Q (%)l

MCCm

Model 1A

2340/585

MACCS

166

2

0.125

92.09

80.30

80.89

80.73

81.20

82.34

81.88

0.63

Model 1B

2362/563

MACCS

166

2

0.03125

84.25

77.99

78.37

78.45

84.96

87.21

86.15

0.72

Model 1C

2340/585

ECFP_4

1024

2

0.03125

94.02

81.92

82.05

81.97

85.47

78.35

81.20

0.63

Model 1D

2362/563

ECFP_4

1024

2

0.03125

94.03

79.13

79.76

79.38

84.59

88.55

86.68

0.73

Model 1E

2340/585

CORINA

17

64

8

88.16

77.43

78.37

77.74

82.91

74.36

77.78

0.56

Model 1F

2362/563

CORINA

17

16

32

93.52

75.49

76.38

77.18

81.20

90.24

85.97

0.72

Model 1A-S

1299/331

MACCS

166

4

0.0625

95.30

86.30

87.30

87.45

82.44

91.00

87.61

0.74

Model 1B-S

1309/321

MACCS

166

8

0.03125

94.35

84.26

86.02

87.62

88.73

91.06

90.03

0.80

Model 1C-S

1299/331

ECFP_4

1024

2

0.03125

97.77

87.61

88.14

88.07

91.60

87.00

88.82

0.77

Model 1D-S

1309/321

ECFP_4

1024

2

0.03125

98.01

88.08

87.85

88.39

87.32

91.06

89.41

0.79

Model 1E-S

1299/331

CORINA

14

2

64

96.69

84.68

85.68

85.76

90.08

88.00

88.82

0.77

Model 1F-S

1309/321

CORINA

14

8

64

98.32

84.64

85.18

85.94

84.51

91.62

88.47

0.77

Model

Parameters

Training set

aThe

Test set

number of COX-2 inhibitors in the training set or test set. “2340/585” represents the random splitting method on dataset 1 (2925 COX-2 inhibitors); “2362/563” represents the SOM splitting method on dataset 1; “1299/331” represents the random splitting method on dataset 2 (1630 COX-2 inhibitors); “1309/321” represents the SOM splitting method on dataset 2. bThe type of input descriptors. cThe number of input descriptors. dThe penalty parameter. eThe kernel coefficient. fThe accuracy on the training set. g5-fold cross validation. h10-fold cross validation. iLeave-one-out cross validation. jSensitivity. kSpecificity. lThe accuracy on the test set. mMatthews correlation coefficient.

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The performances of the SVM models built on dataset 1 (2925 COX-2 inhibitors) were not as good as the corresponding models built on dataset 2 (1630 COX-2 inhibitors). The removal of intermediately active inhibitors in dataset 2 led to better performances in modeling development. The performances of models built by the SOM splitting method were better than those by the random splitting method in terms of MCC values (Table 4). The ROC (Receiver Operating Characteristics) curves of models built by the SOM splitting method (Models 1B, 1D, 1F, 1B-S, 1D-S, and 1F-S) were further analyzed (Figure 2). The greater the AUC (Area Under Curve) values, the better the performance of models.

Figure 2. The receiver operating characteristics (ROC) curves of models 1B, 1D, 1F, 1B-S, 1D-S, and 1F-S (by the SOM splitting method). (a) The ROC curve of Model 1B. The AUC values on the training and test sets are 0.924 and 0.933, respectively; (b) The ROC curve of Model 1D. The AUC values on the training and test sets are 0.988 and 0.941, respectively; (c) The ROC curve of Model 1F. The AUC values on the training and test sets are 0.982 and 0.919, respectively; (d) The ROC curve of Model 1B-S. The AUC values on the training and test sets are 0.984 and 0.955, respectively; (e) The ROC curve of Model 1D-S. The AUC values on the training and test sets are 0.998 and 0.952, respectively; (f) The ROC curve of Model 1F-S. The AUC values on the training and test sets are 0.999 and 0.924, respectively. 13



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For the six SVM models (Models 1A, 1B, 1C, 1D, 1E, and 1F) built on dataset 1, Model 1D was the best SVM model in terms of the Q, MCC, and AUC values of the test set. The accuracies of the training and test sets were 94.03% and 86.68%, respectively. The MCC and AUC values of test set were 0.73 and 0.941, respectively. While for the six SVM models (Models 1A-S, 1B-S, 1C-S, 1D-S, 1E-S, and 1F-S) built on dataset 2, Model 1B-S was the best SVM model in terms of the Q, MCC, and AUC values of the test set. The accuracies of the training and test sets were 94.35% and 90.03%, respectively. The MCC and AUC values of test set were 0.80 and 0.955, respectively. 3.2 RF models An RF model is determined by three parameters: the number of trees in the forest n_estimators, the function to measure the quality of a split criterion, and the number of features to consider when looking for the best split max_features. These three parameters were also optimized by a grid search process, similar to the SVM models mentioned above. The optimum parameters of these twelve RF models and their prediction performances are shown in Table 5.

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Table 5. Parameters and performances of the twelve classification models built by a random forest. Model

Training / test seta

Input Descriptors Typeb

nc

n_estimatorsd

Parameters criterione

max_featuresf

Q (%)g

5-CV (%)h

Training set 10-CV (%)i

LOO (%)j

SE (%)k

SP (%)l

Q (%)m

MCCn

Model 2A Model 2B Model 2C Model 2D Model 2E Model 2F Model 2A-S Model 2B-S Model 2C-S Model 2D-S Model 2E-S Model 2F-S

2340/585 2362/563 2340/585 2362/563 2340/585 2362/563 1299/331 1309/321 1299/331 1309/321 1299/331 1309/321

MACCS MACCS ECFP_4 ECFP_4 CORINA CORINA MACCS MACCS ECFP_4 ECFP_4 CORINA CORINA

166 166 1024 1024 17 17 166 166 1024 1024 14 14

77 75 94 44 81 100 82 55 62 25 63 25

gini gini entropy entropy gini gini gini gini entropy gini entropy entropy

sqrt sqrt log2 sqrt None None sqrt None log2 sqrt sqrt None

95.90 95.81 98.80 98.69 99.83 99.79 97.92 97.86 99.38 99.24 99.92 99.69

78.72 76.25 80.47 77.94 77.01 75.57 86.22 83.04 87.53 84.95 84.45 81.67

79.10 75.87 81.24 78.41 76.96 76.12 87.99 85.18 88.14 87.62 84.53 84.49

79.70 76.29 80.26 78.45 77.26 76.04 86.91 85.26 87.84 86.78 83.83 83.35

80.77 83.83 85.47 82.71 77.78 77.82 86.26 88.03 90.08 86.62 85.50 85.21

79.20 88.22 79.49 87.54 79.49 88.89 87.50 91.62 86.00 87.71 87.00 91.06

79.83 86.15 81.88 85.26 78.80 83.66 87.01 90.03 87.61 87.23 86.40 88.47

0.59 0.72 0.64 0.70 0.57 0.67 0.73 0.80 0.75 0.74 0.72 0.77

a

Test set

The number of COX-2 inhibitors in the training set or test set. “2340/585” represents the random splitting method on dataset 1 (2925 COX-2 inhibitors); “2362/563” represents the SOM splitting method on dataset 1; “1299/331” represents the random splitting method on dataset 2 (1630 COX-2 inhibitors); “1309/321” represents the SOM splitting method on dataset 2. bThe type of input descriptors. cThe number of input descriptors. dThe number of trees in the forest. eThe function to measure the quality of a split. fThe number of features to consider when looking for the best split. gThe accuracy of training set. h5-fold cross validation. i10-fold cross validation. jLeave-one-out cross validation. kSensitivity. lSpecificity. mThe accuracy of test set. nMatthews correlation coefficient.

15



Page 16 of 52

The performances of the RF models built on dataset 1 (2925 COX-2 inhibitors) were not as good as those of the corresponding models built on dataset 2 (1630 COX-2 inhibitors). In general, the performances of models that were built by a random forest were as good as those by support vector machine. In terms of accuracies and MCC values of the test set, the performances of models built by the SOM splitting method were better than those by random splitting method (Table 5). The ROC curves of models built by the SOM splitting method (Models 2B, 2D, 2F, 2B-S, 2D-S, and 2F-S) were also further analyzed (Figure 3).

Figure 3. The receiver operating characteristics (ROC) curves of models 2B, 2D, and 2F (by the SOM splitting method). (a) The ROC curve of Model 2B. The AUC values on the training and test sets are 0.994 and 0.94, respectively; (b) The ROC curve of Model 2D. The AUC values on the training and test sets are 0.999 and 0.936, respectively; (c) The ROC curve of Model 2F. The AUC values on the training and test sets are 1.0 and 0.919, respectively; (d) The ROC curve of Model 2B-S. The AUC values on the training and test sets are 0.998 and 0.957, respectively; (e) The ROC curve of Model 2D-S. The AUC values on the training and test sets are 1.0 and 0.942, respectively; (f) The ROC curve of Model 2F-S. The AUC values on the training and test sets are 1.0 and 0.943, respectively.

16


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For the six RF models (Models 2A, 2B, 2C, 2D, 2E, and 2F) built on dataset 1, Model 2B was the best RF model, with an MCC of 0.72. The accuracies of the training and test sets were 95.81% and 86.15%, respectively, and the AUC values of the test set was 0.94. While for the six RF models (Models 2A-S, 2B-S, 2C-S, 2D-S, 2E-S, and 2F-S) built on dataset 2, Model 2B-S was the best RF model in terms of the Q, MCC, and AUC values of the test set. The accuracies of the training and test sets were 97.86% and 90.03%, respectively. The MCC and AUC values of the test set were 0.80 and 0.957, respectively. 3.3 Prediction of the decoys and the external test set We calculated the prediction accuracy, i.e., the specificity, of 13155 decoys. A decoy, which is usually considered as a negative compound, is a compound that has similar physical properties (e.g. molecular weight, calculated LogP) as inhibitors but dissimilar topology.218 The twelve classification models showed an overall prediction accuracy of 74.75% ~ 99.63% on decoys. This result suggested that the models had good performances for negative compounds. Moreover, models 2A-S~F-S, built by RF methods produced higher prediction accuracies on decoys than other models. We used an external test set to validate the performances of the twelve classification models based on dataset 2 (Table 6). The SP values of the external test set for all the twelve models are greater than 89%. This result also suggested that the models had good performances for weakly active inhibitors. According to Table 6, the SE values are relatively low in all the twelve models, and this result may be caused by the following reason: several active inhibitors with new structural features that were not included in the training set, such as carboxylates incorporating trimellitimides202, benzimidazothiazole derivatives209, new pyrazoles and pyrozolo [3,4-b] pyridines204, were predicted as weakly active inhibitors in the external test set. This fact caused the large FN values of the external test set. In addition, the SE values of the six RF models (2A-S~F-S) are greater than those of the corresponding SVM models (1A-S~F-S). An SVM model was built by all the training samples and all the fingerprint bits or selected descriptors. An RF model was a combination of several decision tree models (n_estimators in Table 5), and different decision tree models were built by different training samples (i.e., bootstrap of the training samples) and different fingerprint bits or selected descriptors (max_features of log2 or sqrt in Table 5). This result caused the RF 17



Page 18 of 52

models are more robust than the SVM models. The performances of the models built with ECFP_4 fingerprints are better than those built with MACCS fingerprints and CORINA descriptors. The best model Model 2D-S, which was built by the RF model using ECFP_4 fingerprints, had an MCC value of 0.68 on the external test set. Model 2D-S can be used as effective virtual screening tools for finding active COX-2 inhibitors. Table 6. The prediction accuracies on decoys of twelve classification models built by support vector machine (SVM) and random forest (RF). Model Model 1A-S

Dataset of decoys Numa 13155

External test set Numc

TPd

88.38

259

8

213

16

206

SP/Q

(%)b

TNe

FPf 2

FNg 36

SE (%)h 18.18

SP (%)i 99.07

Q (%)j 85.33

MCCk 0.34

9

28

36.36

95.81

85.71

0.41

Model 1B-S

13155

74.75

259

Model 1C-S

13155

98.56

259

21

208

7

23

47.73

96.74

88.42

0.54

24

193

22

20

54.55

89.77

83.78

0.44

Model 1D-S

13155

98.94

259

Model 1E-S

13155

95.69

259

12

209

6

32

27.27

97.21

85.33

0.36

Model 1F-S

13155

93.45

259

15

209

6

29

34.09

97.21

86.49

0.43

9

214

1

35

20.46

99.54

86.10

0.39

Model 2A-S

13155

97.08

259

Model 2B-S

13155

90.38

259

20

215

0

24

45.46

100.00

90.73

0.64

21

206

9

23

47.73

95.81

87.65

0.51

Model 2C-S

13155

99.63

259

Model 2D-S

13155

99.40

259

33

202

13

11

75.00

93.95

90.73

0.68

Model 2E-S

13155

95.60

259

18

206

9

26

40.91

95.81

86.49

0.45

93.76

259

21

194

21

23

47.73

90.23

83.01

0.39

Model 2F-S aThe

13155 bThe

cThe

number of decoys. specificity or accuracy of decoys. number of inhibitors in the external test set. dTrue e f g h positive. True negative. False positive. False negative, Sensitivity. iSpecificity. jAccuracy of the external test set. kMatthews correlation coefficient.

3.4 Diversity of COX-2 inhibitors The 2925 COX-2 inhibitors comprise a variety of structures such as pyrimidine, benzopyran, carbolines, and methanesulfonylphenyl. In order to evaluate the structural diversity between every two inhibitors, we calculated their Tanimoto similarity coefficient of every two inhibitors based on their MACCS fingerprints. The greater the Tanimoto coefficient, the similar are the two inhibitors. In general, two inhibitors are considered dissimilar when having a Tanimoto coefficient of less than 0.70. The frequency distribution of the entire Tanimoto coefficient is shown in Figure 4. In our dataset 94.92% of the molecules showed Tanimoto coefficient values of less than 0.70. Therefore, these COX-2 inhibitors are diverse.

18


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Figure 4. Frequency distribution histogram of Tanimoto similarity coefficient of each two COX-2 inhibitors based on MACCS fingerprints.

3.5 Clustering of COX-2 inhibitors by k-means In order to explore the structural features of the 2925 COX-2 inhibitors, we clustered these compounds based on their 166 MACCS fingerprints using the k-means225 clustering method provided by scikit-learn.196 The k-means algorithm clusters data by separating samples into n groups of equal variance and minimizing a criterion known as the inertia within-cluster sum-of-squares. This algorithm requires the number of clusters to be specified, and eight clusters with the highest score were used as the optimal parameters for clustering. In addition, a dimensionality reduction using t-distributed stochastic neighbor embedding (tSNE) was carried out before parameter optimization and clustering. T-SNE is a tool to visualize highdimensional data.226 It converts similarities between data points to joint probabilities and minimizes the Kullback-Leibler divergence between the joint probabilities of the low-dimensional embedding and the high-dimensional data. We visualized the eight subsets divided from 2925 COX-2 inhibitors using t-SNE (Figure 5). TSNE-1 and TSNE-2 are the two dimensions reduced from 166 dimensions of the MACCS fingerprints by the dimensionality reduction method t-SNE. For each subset, the central compound was also identified with its activity (Figure 6).

19



Page 20 of 52

Figure 5. Eight subsets divided from 2925 COX-2 inhibitors, which were clustered by the k-means method, then reduced the dimensionality and visualized the data by t-distributed stochastic neighbor embedding (t-SNE). Pink crosses represent the central compound of each subset. TSNE-1 and TSNE-2 are the two dimensions reduced from 166 dimensions of the MACCS fingerprints by the dimensionality reduction method t-SNE.

20


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Figure 6. Central compounds and their corresponding activities in the eight subsets.

The 2925 COX-2 inhibitors were mainly divided into two clusters: subsets 1 to 3 formed the first cluster and subsets 4 to 8 formed the second cluster. The first cluster comprised relatively simple structures, whereas the second cluster contained more complex structures. Subset 1 was composed of benzene derivatives with few substituents. It contained ester and amide derivatives of indomethacin, indanone derivatives, and some structural units such as pyridine, phenylacetic acid, 3-formylchromones, diphenyl hydrazides and benzopyrrole. Lumiracoxib17 belonged to this subset. There were a total of 456 inhibitors in this subset: 167 highly active and 289 weakly active COX-2 inhibitors. 21



Page 22 of 52

Among them, a series of thiazole analogues of indomethacin,144 some enantiospecific,150 a class of novel acid-type cyclooxygenase-2 inhibitors,67 and lumiracoxib and its derivatives17 were highly active COX-2 inhibitors. A series of fluorobenzoylated di- and tripeptides,61 several α,β-unsaturated carbonyl based synthetic compounds,84 a class of resveratrol amide derivatives,76 several benzamide-type compounds,181 and some carprofen derivatives70 were weakly active COX-2 inhibitors. Inhibitors in subset 2 were rich in oxygen atoms: the number of oxygen atoms ranged from 3 to 7. Several carboxyl groups, or alkoxy groups directly attached to the benzene or pyran ring. There were a total of 376 inhibitors in this subset: 110 highly active and 266 weakly active COX-2 inhibitors. Among them, a series of deuterated benzopyran derivatives82 were highly active COX-2 inhibitors, and a class of flurbiprofen derivatives131 were rather highly active COX-2 inhibitors. A series of loxoprofen derivatives,48 several stellatin derivatives,51 and a class of 2-acetoxyphenyl alkyl sulfides123 were weakly active COX-2 inhibitors. Subset 3 was composed of inhibitors with two aromatic rings connected by a carbonyl group, a nitrogen atom, a sulfur atom, or an ethylene group. There were a total of 296 inhibitors in this subset: 54 highly active and 242 weakly active COX-2 inhibitors. More than 80% of the inhibitors in subset 3 were weakly active COX-2 inhibitors. Among them, a series of benzimidazole/benzothiazole and benzoxazole derivatives,158 several pyrazol-3-propanoic acid derivatives,53 a class of 2-mercaptobenzoxazole based 1,2,3-triazoles98 were weakly active COX-2 inhibitors. Subset 4 comprized diaryl-substituted compounds. The unique structural feature of subset 4 was the methanesulfonylphenyl substituent. Most compounds had a central ring consisting of pyrone, furanone, cyclopentenes, cyclopentenones, and cyclobutenes. Rofecoxib15 belonged to this subset. There were a total 311 of inhibitors in this subset: 167 highly active and 144 weakly active COX-2 inhibitors. Among them, a series of 1,2-diarylcyclobutenes,101 a class of diarylspiro[2.4]heptenes90 were highly active COX-2 inhibitors. A series of diarylthiophenes and terphenyls,112 several triaryl (Z)-olefins,39 and a class of 2phenylpyran-4-ones167 were weakly active COX-2 inhibitors.

22


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Subset 5 comprized diaryl-substituted rings. The unique structural feature of subset 5 was that the halogen abundance. Many central rings were pyridine derivatives. Etoricoxib16 belonged to this subset. There were a total 356 of inhibitors in this subset: 226 highly active and 130 weakly active COX-2 inhibitors. Among them, a series of 4-aryl-5-(4-(methylsulfonyl)phenyl)-2-alkylthio and -2-alkylsulfonyl-2Himidazole derivatives,78 and a class of 2-pyridinyl-3-(4-methylsulfonyl) phenylpyridines16 were highly active COX-2 inhibitors. Subset 6 consisted of diaryl-substituted rings. The central rings of these inhibitors were mostly attached with electron withdrawing groups. Inhibitors in subset 6 were surrounded by subsets 5, 7, and 8 in Figure 5; thus, the structural feature of subset 6 was similar to that of subsets 5, 7, and 8. There were a total of 400 inhibitors in this subset: 179 highly active and 221 weakly active COX-2 inhibitors. Among them, most of the 1,3,4-triaryl-3-pyrrolin-2-ones139 were highly active COX-2 inhibitors. A series of Nsubstituted-3,5-diphenyl-2-pyrazoline derivatives,44 and several 4-thiazolidinone derivatives69 were weakly active COX-2 inhibitors. Subset 7 comprized diaryl-substituted rings. The central ring was a 5-membered ring with nitrogen, such as pyrroles, pyrazoles, imidazole, triazole, tetrazole, and thiazole. Celecoxib14 and cimicoxib18 belonged to this subset. There were a total of 356 inhibitors in this subset: 184 highly active and 172 weakly active COX-2 inhibitors. Among them, a series of 1,5-diarylpyrazoles,110 and several celecoxib analogues148 were highly active COX-2 inhibitors. Most of the urea-containing pyrazoles,56 and several dihydropyrazole sulfonamide derivatives109 were weakly active COX-2 inhibitors. Subset 8 also consisted of diaryl-substituted rings substituted with a sulfonamide group. One of the substituent in this subset was benzenesulfonamide. Valdecoxib13 belonged to this subset. There were a total of 374 inhibitors in this subset: 179 highly active and 195 weakly active COX-2 inhibitors. Among them, a series of substituted heterocyclic analogs in the flosulide class,129 and a class of sulfonamide-substituted 4,5-diaryl thiazoles127 were highly active COX-2 inhibitors. Most of the several 4-benzylideneamino- and 4-phenyliminomethyl-benzenesulfonamides,179 a class of pyridine acyl sulfonamide derivatives,47 and a series of phenylazobenzenesulfonamides171 were weakly active COX-2 inhibitors. 23



Page 24 of 52

3.6 Descriptors analysis 3.6.1 MACCS fingerprints analysis Model 1B and 2B were divided by the SOM splitting method using the MACCS fingerprints, both yielding an MCC value of 0.72. We applied the information gain (IG) values to pinpoint the important MACCS fingerprints on the training set using the program Weka.221 The IG values and the differences of the top 19 MACCS fingerprints in the highly and weakly active COX-2 inhibitors in the dataset are shown in Table 7. Table 7. Details of the top 19 MACCS fingerprints. no. MACCS Description IG values p_high (%) p_weakly (%) Diff (%) Rate 1 MACCS_112 AA(A)(A)A 0.04675 84.12 60.88 23.24 1.38 2 MACCS_107 XA(A)A 0.04489 76.38 52.08 24.30 1.47 3 MACCS_134 X(HALOGEN) 0.04326 76.54 52.68 23.86 1.45 4 MACCS_58 QSQ 0.03656 73.78 52.08 21.70 1.42 5 MACCS_55 OSO 0.03656 73.78 52.08 21.70 1.42 6 MACCS_51 CSO 0.03598 73.70 52.14 21.56 1.41 7 MACCS_61 AS(A)A 0.03587 73.78 52.26 21.52 1.41 8 MACCS_60 S=O 0.03587 73.78 52.26 21.52 1.41 9 MACCS_67 QS 0.03475 73.93 52.92 21.01 1.40 10 MACCS_73 S=A 0.03383 73.78 53.04 20.73 1.39 11 MACCS_136 O=A > 1 0.02939 83.33 65.34 17.99 1.28 12 MACCS_87 X!A$A 0.0255 59.56 39.60 19.96 1.50 13 MACCS_59 Snot%A%A 0.02511 71.96 53.35 18.61 1.35 14 MACCS_42 F 0.02353 53.00 34.48 18.52 1.54 15 MACCS_119 N=A 0.02299 3.71 14.95 -11.24 0.25 16 MACCS_64 A$A!S 0.02286 71.17 53.35 17.82 1.33 17 MACCS_62 A$A!A$A 0.02193 72.99 55.46 17.53 1.32 18 MACCS_102 QO 0.02179 75.12 58.53 16.59 1.28 19 MACCS_81 SA(A)A 0.02063 75.59 59.31 16.28 1.27 p_high (%): percentage of each substructure in the high activity inhibitors; p_weakly (%): percentage of each substructure in the weakly active inhibitors; Diff (%): p_high (%) - p_weakly (%), difference of percentages for each substructure in the highly and weakly active inhibitors; Rate: p_high (%) / p_weakly (%), rate of percentages for each substructure in the highly and weakly active inhibitors. Atom symbols: A=any valid periodic table element symbol; Q=hetero atoms; any non-C or non-H atom; X= halogens; F, Cl, Br, I. Bond types: $=ring bond; $ before a bond type specifies ring bond; !=chain or non-ring bond; ! before a bond type specifies chain bond; =: double; %=an aromatic query bond.

Based on the results of IG values, fragments contributing to highly/weakly active COX-2 inhibitors were identified. Highly active COX-2 inhibitors were likely to contain structural features of AA(A)(A)A (Table 7, MACCS_112) and XA(A)A (Table 7, MACCS_107). In addition, they may contain halogens, (Table 7, MACCS_107 and MACCS_134). Weakly active COX-2 inhibitors may contain the fragment “N=” (Table 7, MACCS_119), i.e. a nitrogen atom connected by a double bond. 24


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3.6.2 ECFP_4 fingerprints analysis For the ECFP_4 fingerprint analyses, we calculated the IG values of each ECFP_4 fingerprint based on the training set of Model 1D and 2D (by the SOM splitting method) using the program Weka. The IG values and the differences of the top 19 ECFP_4 fingerprints in highly or weakly active COX-2 inhibitors in the dataset are shown in Table 8. Table 8. Details of the top 19 ECFP_4 fingerprints. no.

ECFP_4

IG values

p_high (%)

p_low (%)

Diff (%)

Rate

1

ECFP4_137

0.03721

66.82

43.22

23.61

1.55

2

ECFP4_897

0.03518

60.58

37.85

22.73

1.60

3

ECFP4_320

0.02907

66.98

46.35

20.63

1.45

4

ECFP4_351

0.0271

72.83

54.37

18.46

1.34

5

ECFP4_453

0.02545

73.46

55.64

17.82

1.32

6

ECFP4_716

0.02517

66.82

47.26

19.57

1.41

7

ECFP4_808

0.02503

32.78

52.02

-19.24

0.63

8

ECFP4_905

0.02236

53.00

34.96

18.04

1.52

9

ECFP4_411

0.02059

22.35

9.04

13.31

2.47

10

ECFP4_695

0.02036

3.48

12.84

-9.36

0.27

11

ECFP4_430

0.02016

30.25

16.27

13.98

1.86

12

ECFP4_1018

0.01877

1.97

9.34

-7.37

0.21

13

ECFP4_165

0.01826

44.94

28.33

16.61

1.59

14

ECFP4_141

0.01701

2.37

9.16

-6.79

0.26

15

ECFP4_1010

0.01671

2.61

10.19

-7.58

0.26

16

ECFP4_115

0.01665

33.49

19.89

13.60

1.68

17

ECFP4_950

0.01572

8.93

2.11

6.82

4.23

18

ECFP4_253

0.01529

45.18

29.96

15.22

1.51

19

ECFP4_579

0.015

2.53

9.52

-7.00

0.27

p_high (%): percentage of each substructure in the high activity inhibitors; p_weakly (%): percentage of each substructure in the weakly active inhibitors; Diff (%): p_high (%) - p_weakly (%), difference of percentages for each substructure in the highly and weakly active inhibitors; Rate: p_high (%) / p_weakly (%), rate of percentages for each substructure in the highly and weakly active inhibitors.

Based on the results of IG values on ECFP_4 fingerprints, fragments contributing to highly/weakly active COX-2 inhibitors are shown in Figure 7.

25



Page 26 of 52

Figure 7. Important fragments in ECFP_4 fingerprints. (a) Structural fragments that contribute to highly active COX-2 inhibitors; (b) Structural fragments that contribute to weakly active COX-2 inhibitors.

3.6.3 CORINA Symphony descriptors analysis In Model 1E, 1F, 2E, and 2F, 17 CORINA descriptors, including 4 global molecular descriptors and 13 3D property-weighted autocorrelation descriptors were applied. In Model 1E-S, 1F-S, 2E-S, and 2F-S, 14 CORINA descriptors including 4 global molecular descriptors and 10 3D property-weighted autocorrelation descriptors were used. Pearson correlation coefficient between each CORINA descriptor and the activity in dataset 1 and dataset 2 were calculated (Table 9). Eleven CORINA descriptors were the same, although they were selected from different datasets. Moreover, the Pearson correlation coefficient between each descriptor and the activity based in dataset 2 was higher than the corresponding ones based in dataset 1. This result suggest that a better distinction of the effects on activity was obtained by discarding intermediately active inhibitors. We identified that σ atom electronegativities, π atom charges, π atom electronegativities, and effective atom polarizabilities were important to these classification models. 3D property-weighted autocorrelation descriptors sum up atom pair properties based on certain 3D distance intervals. Heteroatoms such as sulfur, oxygen and nitrogen atoms of the carboxyl, sulfonamide, and methanesulfonyl groups, as well as the aromatic nitrogen atoms contribute significantly in atom pair properties. In the study the following rules was identified about the 3D distance intervals. The six CORINA descriptors

(Table

9,

3DACorr:SigEN:Cor3D:ori1_2,

3DACorr:PiChg:Cor3D:ori1_1,

3DACorr:PiChg:Cor3D:ori1_2, 3DACorr:PiEN:Cor3D:ori1_2, 3DACorr:Polariz:Cor3D:ori1_1, and 26


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3DACorr:TotChg:Cor3D:ori1_1) revealed the electronegativities and charge properties in the range of 1–3 Å. First, the 3D distance between the three atoms of the carboxyl group (i.e. C=O, C-O, and O-O) from the central compounds of subsets 1, 2, and 7 (as shown in Figure 5) was in the range of 1–3 Å. Second, the 3D distance of the sulfonamide and methanesulfonyl groups (S=O and O-O of sulfonamide and methanesulfonyl, and O-N of sulfonamide) from the central compounds of subsets 4~8 (as shown in Figure 5) was in the range of 1–3 Å. The six CORINA descriptors (Table 9, 3DACorr:SigEN:Cor3D:ori1_5, 3DACorr:SigEN:Cor3D:ori1_6,

3DACorr:SigEN:Cor3D:ori1_7,

3DACorr:PiChg:Cor3D:ori1_5,

3DACorr:PiEN:Cor3D:ori1_6, and 3DACorr:Polariz:Cor3D:ori1_5) revealed the electronegativities and charge properties in the range of 5–8 Å. The 3D distance between the terminal sulfur atom (i.e., the sulfur atom of a sulfonamide and a methanesulfonyl group) and the aromatic nitrogen atom of the central ring, such as the central compounds of subsets 5~7 in Figure 5, was in the range of 5–8 Å. These results suggest that the substructure containing at the right-hand side a sulfonamide or a methanesulfonyl group, a central benzene ring, and at the left-hand side a nitrogen-containing aromatic ring, such as the central compounds of subsets 5~7 in Figure 5, was a significant feature of the COX-2 inhibitors. Table 9. Description of CORINA descriptors and the Pearson correlation coefficient (CC) with the activity in dataset 1 and dataset 2. Descriptors LogS

Typea Description

CC-1b CC-2c

1&2 Solubility of the molecule in water in [log units] Octanol/water partition coefficient in [log units] of the molecule following the XlogP 1&2 approach 1&2 Molecular complexity according to theapproach by Hendrickson

-0.19

-0.26

0.16

0.22

0.13

0.19

1&2 Molecular weight derived from the gross formula 3D autocorrelation weighted by σ atom electronegativities, where d is in the range 1&2 of 2–3 Å 3D autocorrelation weighted by σ atom electronegativities, where d is in the range 1 of 3–4 Å 3D autocorrelation weighted by σ atom electronegativities, where d is in the range 1 of 5–6 Å 3D autocorrelation weighted by σ atom electronegativities, where d is in the range 1&2 of 6–7 Å 3D autocorrelation weighted by σ atom electronegativities, where d is in the range 1 of 7–8 Å

0.13

0.18

0.13

0.16

0.12

\

0.16

\

0.13

0.16

0.18

\

3DACorr:PiChg:Cor3D:ori1_1 1&2 3D autocorrelation weighted by π atom charges, where d is in the range of 1–2 Å

-0.14

-0.21

3DACorr:PiChg:Cor3D:ori1_2 1&2 3D autocorrelation weighted by π atom charges, where d is in the range of 2–3 Å

0.11

0.17

3DACorr:PiChg:Cor3D:ori1_5

0.10

\

0.15

\

0.18

0.22

0.20

0.26

XlogP Complex Weight 3DACorr:SigEN:Cor3D:ori1_2 3DACorr:SigEN:Cor3D:ori1_3 3DACorr:SigEN:Cor3D:ori1_5 3DACorr:SigEN:Cor3D:ori1_6 3DACorr:SigEN:Cor3D:ori1_7

3DACorr:PiEN:Cor3D:ori1_2 3DACorr:PiEN:Cor3D:ori1_4 3DACorr:PiEN:Cor3D:ori1_6

1 3D autocorrelation weighted by π atom charges, where d is in the range of 5–6 Å 3D autocorrelation weighted by π atom electronegativities, where d is in the range 1 of 2–3 Å 3D autocorrelation weighted by π atom electronegativities, where d is in the range 1&2 of 4–5 Å 3D autocorrelation weighted by π atom electronegativities, where d is in the range 1&2 of 6–7 Å 27



3D autocorrelation weighted by effective atom polarizabilities, where d is in the range of 1–2 Å 3D autocorrelation weighted by effective atom polarizabilities, where d is in the 1 range of 5–6 Å

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3DACorr:Polariz:Cor3D:ori1_1 1&2

0.17

0.24

3DACorr:Polariz:Cor3D:ori1_5

0.17

\

3DACorr:Ident:Cor3D:ori1_3

2 3D autocorrelation weighted by atom identities, where d is in the range of 3–4 Å

\

0.13

3DACorr:Ident:Cor3D:ori1_4

2 3D autocorrelation weighted by atom identities, where d is in the range of 4–5 Å

\

0.11

\

-0.21

3DACorr:TotChg:Cor3D:ori1_1 2

3D autocorrelation weighted by total atom charges (sum of σ and π charges), where d is in the range of 1–2 Å

a“1” represents the selected CORINA descriptors based on dataset 1 ,and “2” represents the selected CORINA descriptors based on dataset 2. bThe correlation coefficient (CC) between each CORINA descriptors and the activity in dataset 1. cThe correlation coefficient (CC) between each CORINA descriptors and the activity in dataset 2.

3.6.4 Descriptors analysis between three types of descriptors Pearson correlation coefficient between MACCS fingerprints, ECFP_4 fingerprints, and 17 selected CORINA descriptors based on 2925 inhibitors of dataset 1 were calculated to explore the correlation between the three types of descriptors (Figure 8).

Figure 8. Frequency distribution histogram of the absolute Pearson correlation coefficient between MACCS fingerprints, ECFP_4 fingerprints, and CORINA descriptors. (a) MACCS fingerprints vs. ECFP_4 fingerprints; (b) CORINA descriptors vs. MACCS fingerprints; (c) CORINA descriptors vs. ECFP_4 fingerprints. 28


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MACCS fingerprints vs. ECFP_4 fingerprints. The absolute Pearson correlation coefficients between each MACCS fingerprint (166 bits) and each ECFP_4 fingerprint (1024 bits) were calculated, and the frequency distribution histogram of these coefficients (i.e., 166×1024=169984) are shown in Figure 8(a). For the two fingerprints, MACCS fingerprints usually represent certain structural features, while ECFP_4 fingerprints represent the atomic environments, i.e., the bond types connected to the atoms. There was a closer correlation between MACCS fingerprints and ECFP_4 fingerprints, as some of the absolute Pearson correlation coefficients were higher than 0.8. For example, the structural features represented by ECFP4_320 (Figure 7, selected ECFP_4 fingerprints) include those represented by MACCS_51, MACCS_73, MACCS_59 (Table 7, no. 6, 10, and 13), etc. Moreover, the Pearson correlation coefficients between ECFP4_320 and MACCS_51, MACCS_73, MACCS_59 were 0.85, 0.84, and 0.84, respectively. This observation suggested that several substructures derived from different fingerprints were similar. CORINA descriptors vs. MACCS fingerprints. The absolute Pearson correlation coefficients between each CORINA descriptor (17 selected descriptors) and MACCS fingerprints (166 bits) were calculated, and the frequency distribution histogram of these coefficients (i.e., 17×166=2822) are shown in Figure 8(b). The data showed that only a few coefficients were greater than 0.6, and most of them were obtained between the 3DACorr:PiChg:Cor3D:ori1_1 and the selected MACCS fingerprints (Table 7). For example, the Pearson correlation coefficients between 3DACorr:PiChg:Cor3D:ori1_1 and MACCS_102, MACCS_58, MACCS_51 (Table 7, no. 18, 4, and 6) were 0.78, 0.72, and 0.71, respectively. This result suggested that the substructures with hetero atoms, especially sulfur atoms (Table 7, no. 4~10), could indirectly represent the CORINA descriptor 3DACorr:PiChg:Cor3D:ori1_1. CORINA descriptors vs. ECFP_4 fingerprints. The absolute Pearson correlation coefficients between each CORINA descriptor (17 selected descriptors) and ECFP_4 fingerprint (1024 bits) were calculated, and the frequency distribution histogram of these coefficients (i.e., 17×1024=17408) are shown in Figure 8(c). The result indicated that only a few coefficients were greater than 0.6, and most of them were obtained between the 3DACorr:PiChg:Cor3D:ori1_1 and the selected ECFP_4 fingerprints (Table 8, Figure 7). For example, the Pearson correlation coefficients between 3DACorr:PiChg:Cor3D:ori1_1 and 29



Page 30 of 52

ECFP4_320, ECFP4_351, ECFP4_453, and ECFP4_716 (Table 8, no. 3~6) were 0.64, 0.67, 0.70, and 0.73, respectively.

4. Conclusion In the present work, we developed twelve classification models based on 2925 human COX-2 inhibitors. The molecular structures of these COX-2 inhibitors were quite diverse according to their Tanimoto coefficients. Two splitting methods, a random method and a Kohonen self-organizing map (SOM), were used to divide the dataset into a training set and a test set at a ratio of 4:1. Two classes of descriptors were used to represent the inhibitors, fingerprints (the MACCS and ECFP_4 fingerprints) and CORINA descriptors. Two machine learning methods, a support vector machine and a random forest, were used to develop classification models. The best SVM and RF models produced an MCC values of 0.73 and 0.72, respectively. We further divided the 2925 COX-2 inhibitors into three groups, and removed intermediately active inhibitors, and obtained 1630 COX-2 inhibitors with a better distinction of the effects on activity. Based on the reduced dataset, we developed twelve new classification models. The MCC values of all the twelve new models were greater than 0.72. The performances of the twelve new models based on 1630 inhibitors were better than those of the corresponding models based on 2925 inhibitors. The best MCC value of an external test set containing 259 new COX-2 inhibitors was predicted to be 0.68 by the RF model using ECFP_4 fingerprints. We also clustered the 2925 COX-2 inhibitors into eight subsets based on their MACCS fingerprints by t-SNE and k-means and identified the structural features responsible for activity of each subset. Based on analyses of the MACCS fingerprints and ECFP_4 fingerprints, it was identified that highly active inhibitors usually contained halogen and the weakly active inhibitors often contained the substructure “N=” (i.e., a nitrogen atom connected by a double bond). According to the analysis of CORINA descriptors, it was found that the carboxyl, sulfonamide, and methanesulfonyl groups, as well as the aromatic nitrogen atoms were important to the activity. The correlation between the three types of descriptors was also explored. We found that there was a close correlation between MACCS fingerprints and ECFP_4 fingerprints. The 3DACorr:PiChg:Cor3D:ori1_1 also had a close correlation with several important MACCS and ECFP4 fingerprints. 30


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ASSOCIATED CONTENT Supporting Information The molecular structures and the activities of the 2925 COX-2 inhibitors in dataset 1 (SDF); the molecular structures and the activities of the 1630 COX-2 inhibitors in dataset 2 (SDF).

ACKNOWLEDGEMENTS This work was supported by the National Natural Science Foundation of China (21675010), and “Chemical Grid Project” of Beijing University of Chemical Technology. We thank the Molecular Networks GmbH, Nuremberg, Germany for providing the programs CORINA Symphony and SONNIA for our scientific work. We thank Prof. J. Gasteiger and Dr. L. Tan for their comments.

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Figure 1. Scatter plots of ‘Weight’ (i.e., molecular weight) versus ‘XlogP’ based on the training and test sets, (a) for the random splitting method; (b) for the Kohonen self-organizing map (SOM) splitting method. 136x185mm (150 x 150 DPI)


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Figure 2. The receiver operating characteristics (ROC) curves of models 1B, 1D, 1F, 1B-S, 1D-S, and 1F-S (by the SOM splitting method). (a) The ROC curve of Model 1B. The AUC values on the training and test sets are 0.924 and 0.933, respectively; (b) The ROC curve of Model 1D. The AUC values on the training and test sets are 0.988 and 0.941, respectively; (c) The ROC curve of Model 1F. The AUC values on the training and test sets are 0.982 and 0.919, respectively; (d) The ROC curve of Model 1B-S. The AUC values on the training and test sets are 0.984 and 0.955, respectively; (e) The ROC curve of Model 1D-S. The AUC values on the training and test sets are 0.998 and 0.952, respectively; (f) The ROC curve of Model 1F-S. The AUC values on the training and test sets are 0.999 and 0.924, respectively. 459x466mm (96 x 96 DPI)



Figure 3. The receiver operating characteristics (ROC) curves of models 2B, 2D, and 2F (by the SOM splitting method). (a) The ROC curve of Model 2B. The AUC values on the training and test sets are 0.994 and 0.94, respectively; (b) The ROC curve of Model 2D. The AUC values on the training and test sets are 0.999 and 0.936, respectively; (c) The ROC curve of Model 2F. The AUC values on the training and test sets are 1.0 and 0.919, respectively; (d) The ROC curve of Model 2B-S. The AUC values on the training and test sets are 0.998 and 0.957, respectively; (e) The ROC curve of Model 2D-S. The AUC values on the training and test sets are 1.0 and 0.942, respectively; (f) The ROC curve of Model 2F-S. The AUC values on the training and test sets are 1.0 and 0.943, respectively. 458x465mm (96 x 96 DPI)


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Figure 4. Frequency distribution histogram of Tanimoto similarity coefficient of each two COX-2 inhibitors based on MACCS fingerprints. 152x101mm (300 x 300 DPI)



Figure 5. Eight subsets divided from 2925 COX-2 inhibitors, which were clustered by the k-means method, then reduced the dimensionality and visualized the data by t-distributed stochastic neighbor embedding (tSNE). Pink crosses represent the central compound of each subset. TSNE-1 and TSNE-2 are the two dimensions reduced from 166 dimensions of the MACCS fingerprints by the dimensionality reduction method t-SNE. 170x92mm (300 x 300 DPI)


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Figure 6. Central compounds and their corresponding activities in the eight subsets. 174x165mm (300 x 300 DPI)



Figure 7. Important fragments in ECFP_4 fingerprints. (a) Structural fragments that contribute to highly active COX-2 inhibitors; (b) Structural fragments that contribute to weakly active COX-2 inhibitors. 182x131mm (96 x 96 DPI)


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Figure 8. Frequency distribution histogram of the absolute Pearson correlation coefficient between MACCS fingerprints, ECFP_4 fingerprints, and CORINA descriptors. (a) MACCS fingerprints vs. ECFP_4 fingerprints; (b) CORINA descriptors vs. MACCS fingerprints; (c) CORINA descriptors vs. ECFP_4 fingerprints. 150x289mm (300 x 300 DPI)



Table of Contents 340x160mm (300 x 300 DPI)


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Classification of Cyclooxygenase-2 Inhibitors using Support Vector

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