 Research article
 Open Access
 Published:
Improving chemical similarity ensemble approach in target prediction
Journal of Cheminformatics volume 8, Article number: 20 (2016)
Abstract
Background
In silico target prediction of compounds plays an important role in drug discovery. The chemical similarity ensemble approach (SEA) is a promising method, which has been successfully applied in many drugrelated studies. There are various models available analogous to SEA, because this approach is based on different types of molecular fingerprints. To investigate the influence of training data selection and the complementarity of different models, several SEA models were constructed and tested.
Results
When we used a test set of 37,138 positive and 42,928 negative ligandtarget interactions, among the five tested molecular fingerprint methods, at significance level 0.05, Topologicalbased model yielded the best precision rate (83.7 %) and \({F_{0.25}{\text{}}Measure}\) (0.784) while Atom pairbased model yielded the best \(F_{0.5}{\text{}}Measure\) (0.694). By employing an election system to combine the five models, a flexible prediction scheme was achieved with precision range from 71 to 90.6 %, \(F_{0.5}{\text{}}Measure\) range from 0.663 to 0.684 and \(F_{0.25}{\text{}}Measure\) range from 0.696 to 0.817.
Conclusions
The overall effectiveness of all of the five models could be ranked in decreasing order as follows: Atom pair \(\approx\) Topological > Morgan > MACCS > Pharmacophore. Combining multiple SEA models, which takes advantages of different models, could be used to improve the success rates of the models. Another possibility of improving the model could be using targetspecific classes or more active compounds.
Background
In recent years, with the increasing cost of drug development and the inconsistent and slow speed of drug approval, predicting new targets for approved drugs has become a popular research area [1–8]. It is well known that drugs interact with multiple targets rather than with a single target (called the offtarget effect), and this fact can be beneficial [9] or harmful [10] (known as side effects or toxicity). Drug discovery methods that take advantage of the polypharmacological nature of drugs are becoming more popular [11], because drug discovery starting from approved drugs can benefit from the elimination of many toxicological and pharmacokinetic assessments.
With the everincreasing public availability of bioactivity data [12], it is possible to construct reliable targetprediction models using statistical or machine learning methods. Paolini et al. [13] identified different types of targets within the human pharmacological interaction network using Bayesian classification models. Using activity data from the ChEMBL17 database, Afzal et al. [1] evaluated a multilabel multiclass classification model and a singlelabel multiclass classification model. In 2007, Keiser et al. [5] developed the chemical similarity ensemble approach (SEA), which relates proteins to one another based on the chemical similarity among their bound ligands. Since then, the SEA and SEAlike methods have been successfully applied in new target identification for old drugs [3, 5, 8]/natural products [14], for sideeffect prediction [15] and for the prediction of potential anatomical therapeutic indications (ATCs) of approved drugs [16]. Moreover, studies [17] have shown that there is a startling difference between ligandbased and sequencebased approaches, and in most case the ligandbased similarity approach is more informative for pharmacology than the sequencebased approach [4]. Therefore, relating proteins on the basis of the chemical similarity of their ligands, which is motivated by the BLAST theory [18], rather than by their protein sequences, could provide new insights into the relationships between structurally dissimilar but functional related proteins.
An SEA model can be built based on different types of molecular fingerprints. Hert et al. [4] evaluated the performance of several commonly used fingerprints in SEA, and their results showed that ECFP_4 (extended connectivity fingerprint with radius equals 4) yielded the best performance, but the others were comparable. Hence, the chemical similarity criteria of small molecules play key roles in SEA modeling. In this study, to investigate the influence of different fingerprints, training data sets, and activity thresholds on SEA models, we constructed five SEA models based on five fingerprints—Morgan, Atom pair, Topological, MACCS (molecular access system) keys and Pharmacophore—and also a multivoting SEA model based on the 5 different fingerprintbased SEA models. Finally, we tested the performance of the six SEA models.
Methods
Data sets and preparation
The ChEMBL database is a good open access data source for drug discovery [12]. In this study, the activity data from ChEMBL19 were used for the training set, whereas the newly reported activity data in ChEMBL20, compared to ChEMBL19, were used as the test set. The following steps were performed to create the training sets. First, as shown in Fig. 1, molecules were curated by removing salt and fragments and by filtering out molecules with MWs (molecule weights) larger than 1000. Second, for targetligand pairs with multiple activity values, the geometric mean was used. Only targets labeled with SINGLE PROTEIN were used, and targets with fewer than 5 ligands were also excluded; Third, three different activity thresholds (pChEMBL values 5, 6, and 7—the pChEMBL value is a ChEMBLconverted value, which is a negative logarithm of the published activity [19], so 10 μm equals a pChEMBL value of 5) were applied to generate three datasets. Fourth, considering computational efficiency and data balance, although SEA has a robust set size [5], 3000 diverse ligands were picked for targets with ligand set size exceeding 3000. To prepare the test set, the same procedure was applied but with the difference that only one activity threshold (pChEMBL \(\ge\) 5) was used. In addition, to test the SEA on a specific protein family, a kinasespecific training set and a test set were created using the same strategy from the kinase activity data of ChEMBL19 and ChEMBL20. Finally, six data sets—training sets with activity thresholds \(10\), \(1\) and \(0.1\ \upmu {\text{m}}\), a test set, a kinase training set and a kinase test set—were generated (see Additional files 1, 2, 3, 4, 5, 6). The data statistics are shown in Table 1.
Similarity evaluation and performance validation measures
Only 2D structural similarities were considered in this study. Six different molecular representations were calculated including Morgan (RDKit [20] implementation, similar to the ECFP/FCFP fingerprint [21]), Atom pair fingerprints [22], Topological torsions fingerprints, MACCS keys fingerprints, 2D pharmacophore fingerprints and SHED descriptors [23]. The first five fingerprints are binary vectors that encode the presence or absence of a predefined feature (e.g., a fragment), and the SHED descriptors were calculated based on the informationtheoretical concept of Shannon entropy to quantify the variability in a featurepair distribution [23]. A SHED descriptor is a 10dimensional array, in which each variable ranges from 0 to 20. The average similarities of the 5 binary fingerprints and SHED descriptors on the active molecules of 2089 ligand sets (of different targets) from the training set were summarized in the (see Additional file 7: Fig. S1).
For binary fingerprint similarity measurements, the Tanimoto coefficient (TC) was used, which is given by Eq. 1:
where S represents the coefficient, a and b are the on bits of A and B, and c is common to both bits. Moreover for SHED descriptors, the similarity of A and B is given by Eq. 2:
where DIST(A, B) denotes the Euclidean distance between A and B.
The performances of each model were evaluated with respect to accuracy, precision, sensitivity, specificity and \(F_\beta{\text{}}Measure\) as shown in the Eqs. (3–7). The \(F_\beta{\text{}}Measure\) is the harmonic mean of precision and sensitivity. It combines precision and sensitivity in a single metric. More specifically, the \(F_\beta{\text{}}Measure\) is a weighted harmonic mean of precision and sensitivity in which \(\beta\) measures the effectiveness of retrieval with respect to a user who attaches \(\beta\) times as much importance to sensitivity as precision. For example, the \(F_{0.5}{\text{}}Measure\) and \(F_{0.25}{\text{}}Measure\) weights precision two and four times more than sensitivity, respectively. In this study, due to the incomplete experimental evidence of the relationship of all ligandtarget pairs in both test and training data set, the multilabel classification problem, that a ligand may be active against more than one target, was convert to binary classification. Thus, the false positive rate obtained is underrated, which will be discussed in the result section. Under this circumstances, precision is more important than sensitivity, therefore, two variations of \(F_\beta{\text{}}Measure\), \(F_{0.5}{\text{}}Measure\) and \(F_{0.25}{\text{}}Measure\) together with precision, were mainly used to examine and discuss the results of different models.
where TP, FP, TN and FN denote true positive, false positive, true negative and false negative respectively.
SEA model implementation
The procedures for building SEA models were derived from a reference [5], with minor changes. Here, a brief summary is provided. The chemical similarity of two sets of ligands can be accessed by the sum of the chemical similarities between each pair of ligands. However, this process will render the value very sensitive to the size of the data, to noise and to false positive data. To minimize the influence of noise, the original SEA [5] method introduced the Raw Score (RS) (Eqs. 8, 9), which was defined as the sum of the ligandpair TCs over all of the pairs with \(TC \ge TS\) (Tanimoto threshold). Then, RS was converted to a Zscore and P value (see eqs.10–14), which were used to indicate the significance of the RS. In addition, TS was determined by the best fitness of EVD (extreme value distribution) using the chisquare test, indicating that only significant similarities were considered contributions to setset similarity. This work followed Keiser et al.’s [5] procedures to fit TS, with RS calculated for all TC thresholds from 0.00 to 0.99 with a step size of 0.01. As described in Fig. 1, after data curation, the background data sets were randomly created with set sizes ranging from 10 to 1000 and an interval step of 10, which results in 4950 pairs of molecular data set. Then, pairwise RS of data sets were calculated, this RS calculation procedures is described in detail using its pseudo code (illustrated in Algorithm 1). This procedure was repeated 100 times. More details of the procedure can be found in the original work [5].
where
where s is the product of set A and B, \(F_{mean}\) and \(F_{sd}\) are:
Functions \(F_{mean}\) and \(F_{sd}\) were used to calculate the expected raw score mean and standard deviation, and the parameters \(\mu\), \(\phi\) and \(\eta\) were determined by fitting the random background statistical model (see the Additional file 7: Fig. S2 and S3). Considering the fact that for \(z \ge 28\), computing \(e^z\) exceeds the numerical precision of most programming languages, therefore a Taylor expansion is employed instead [5]. Then, the P value of a Zscore (z) was calculated:
where
Results and discussion
Activity threshold
Generally, \(10\ \upmu {\text{m}}\) has been used as activity cutoff in many works [24, 25]. However, to investigate the influence of different activity thresholds, three SEA models were constructed with activity thresholds of \(10\), \(1\) and \(0.1\ \upmu {\text{m}}\), respectively. All the three models were built based on Morgan fingerprint. The result, as shown in Table 2, showed that, at the significance level of P value \(\le \ \)0.05, the model with a threshold of \(0.1\ \upmu {\text{m}}\) yielded the best precision of 95.8 % and specificity of 99.7 %, but a very low sensitivity (true positive rate or recall) of 7.2 %; however, the model with a threshold of \(10\ \upmu {\text{m}}\) yielded the best accuracy (67.6 %), sensitivity (38.2 %), and \(F_\beta{\text{}}Measure\) (\(F_{0.5}{\text{}}Measure = 0.57\), \(F_{0.25}{\text{}}Measure = 0.772\)). And the performance of the model with \(1\ \upmu {\text{m}}\) as threshold is in between the above two models. This result should not come as surprise because a higher activity threshold indicates a higher quality of the training set, as well as a smaller size of the set. It must be point that, of the 1190 * 26,489 ligandtarget pairs in test set, Morgan model with threshold \(10\ \upmu {\text{m}}\) gave 65,772 pair of positive predictions (P value \(\le\)0.05), and most of these predictions haven’t been proved by experiment. Here we took a conservative estimate of the real result that the false positive rate was underestimated. Therefore, in the following sections, \(F_{0.5}{\text{}}Measure\) and \(F_{0.25}{\text{}}Measure\) were used as the measure. On the other side, at the significance level of Pvalue ≤0.01, the precision, accuracy \(F_{0.5}{\text{}}Measure\) and \(F_{0.25}{\text{}}Measure\) of the model with a threshold of \(10\ \upmu {\text{m}}\) reached at 91.6, 67.9 %, 0.684 and 0.883 respectively but with the expense of reduction of sensitivity (33.9 %). Thus, in practice, it depends on the researchers to decide which model to use, according to the actual situation, need broader alternatives of ligandtarget interaction pair for a few of potential molecule or a higher predictive accuracy rate for highthroughput target identification for a large molecule set. For consistency, hereafter in this paper, unless otherwise specified, the models were built using the training data set, filtered with an activity threshold of \(10\ \upmu {\text{m}}\).
Fuzzy representation of compounds
The twodimensional Pharmacophore fingerprint implemented in the RDKit [20] package was employed to investigate the influence of the “fuzziness” of the representation of compound structures in the SEA model. Details of the definition can be found in the RDKit online document (http://rdkit.org/docs/RDKit_Book.html). The different levels of fuzziness were controlled by the number of points of the pharmacophore and the shapes of the bins. The fingerprint definition from Gobbi’s work [26], which is also implemented in RDKit, was used in this study. Table 3 demonstrates the target prediction performances of 3 types of pharmacophore definitions. With the same 2 to 3 points in a pharmacophore, the comparison between differently shaped bins showed that rougher bin selection, indicating a fuzzier fingerprint, yielded higher sensitivity (43.6 vs. 42 %) but lower accuracy rate (64.2 vs. 66.7 %), precision (67 vs. 75.2 %), \(F_{0.5}{\text{}}Measure\) (0.61 vs. 0.65) and \(F_{0.25}{\text{}}Measure\) (0.657 vs. 0.719). However, an “extremely fuzzy” fingerprint with only 2 points in a pharmacophore was not sufficiently informative to build an SEA model because it yielded a poor precision rate of 47.9 %, which indicates the false positive rate is more than 50 %. Pharmacophorebased fingerprints are a type of flexible molecular representation because the definition of the pharmacophore and the shape of the bin can vary, resulting in different levels of fuzziness. Fuzzy pharmacophores can also be used to identify compounds with similar pharmacological functions but structural differences [27, 28]. The results in this section indicated that the fuzziness of the pharmacophore impacted the performance of the SEA greatly, and a welldesigned pharmacophore scheme might improve the performance significantly. In the following sections, pharmacophore fingerprintbased SEA was built with point numbers of 2 and 3, and bin shapes (2,3), (3,4), (4,5), (5,6), (6,7), and (7,20).
SHED descriptors and Euclidean distance
We also tested the probability of SHED in building an SEA model. SHED is a pharmacophorebased descriptor schema including 4 pharmacophore definitions—hydrophobic, donor, acceptor and aromatic—as well as 10 pairwise descriptors. As stated in the Methods section, Euclidean distance together with a normalized Eq. (2), was used as a similarity criterion. Unlike with EVD, the Zscores achieved from SHED followed a Gaussian distributions more closely. Although SHED has been successfully used in some works [29, 30], the test results in this study showed that this type of schema is not proper for SEA models with poor precision (45.4 %) as well as \(F_{0.5}{\text{}}Measure\) (0.481) and \(F_{0.25}{\text{}}Measure\) (0.462), indicating that SHED, with 10 dimensional arrays, is not sufficiently informative to build an accurate SEA model.
SEA with different types of fingerprints
To analyze the predictive power of different fingerprints in SEA models, in addition to Morgan and pharmacophore models, another 3 SEA models were also built, including Atom pair, MACCS keys and Topological models. Table 4 shows the test results of 5 fingerprintbased SEA models. The prediction precision rates of the five fingerprintbased SEA models ranged from 75.2 to 83.7 % (at a P value \(\le\)0.05) or from 85.6 to 92.1 % (at a P value \(\le\) 0.01). More specifically, at significance level 0.05, The Topological model yielded the best precision rate (83.7 %) and \(F_{0.25}{\text{}}Measure\) (0.784) while The Atom pair model yielded the best \(F_{0.5}{\text{}}Measure\) (0.694). Therefore, the overall effectiveness of all of the models could be ranked in decreasing order as follows: Atom pair \(\approx\) Topological > Morgan > MACCS > Pharmacophore. However, as can be observed from Table 4, in general, all the five models are comparable which consisted with previous work [4].
Multiplevoting SEA model
Kogej et al.’s [31] work demonstrated that much overlap was observed in selecting compounds using different fingerprints, and the combination of different fingerprints yielded better performance [31]. Therefore, it was worthwhile to determine whether combining multiple SEA models could improve the predictive power. First, we calculated the overlaps of the number of true positive predictions of different fingerprintbased SEA models. Table 5 shows that most of the predictions of different models overlapped with each other. Taking Atom pairbased model as an example, of the 15,944 true positive prediction, only 736 predictions overlapped with none of the predictions from other models. This finding was consistent with Kogej et al.’s work. Then, we constructed a multivoting SEA model, as described in the following. To combine the 5 models, an election system was built by employing the Pvalue of each model as a vote. For example, if we took 3 votes into consideration (3vote scheme), a ligandtarget pair was significant only if there were more than three Pvalues less than the Pvalue cutoff from the five SEA models. The test results of the 1 to 5vote SEA models are also included in Table 4. As expected, it can be found that precision increase with the vote cutoff of the model. Figure 2 presents the number of positive prediction, true positive prediction and the accuracy rates of different vote schemes at significance level 0.05. The 1vote scheme yielded 27,676 predictions, of which 19,644 were correct, and this number was more than half of the test set. However, the precision rate was relatively low (71 %). In contrast, the 5vote scheme yielded a high precision of 90.6 % but a relatively small number of positive predictions at 13,122 (11,882 were true positive). Moreover, with a significance level of 0.01, the 5vote scheme yielded a high accuracy of 94.1 % (see the Additional file 7: Fig. S4). Our results indicated that combining different fingerprints did improve the predictive performance of the SEA model. Because different fingerprints take charge of different aspects and features of a compound, the multivoting SEA could be very robust (using a 1vote scheme) for predicting targetligand pairs and also accurate in its results (using the 5vote scheme).
Kinase specific model
The Target classspecific model, by removing unrelated protein families or noise information, should improve the predictive performance. To confirm this assumption, a kinasespecific SEA model was constructed using a kinase training set based on Morgan fingerprint. When running on the kinase test set (2,192 positives, 818 negatives), at significance level 0.05, the kinasespecific SEA model outperform MorganSEA5 in precision 100 vs. 94.8 %, but MorganSEA5 model gave better \(F_{0.5}{\text{}}Measure\) (0.667 vs. 0.326) and \(F_{0.25}{\text{}}Measure\) (0.843 vs. 0.621) result. Our results indicated that a target classspecific SEA model could improve the prediction precision rate, all positive prediction were correct in this case. Therefore, a kinasespecific SEA model is useful and reliable (due to its high prediction accuracy) for capturing target relationships within the kinase families. As stated above, chemical similarity of the targets may not consist with their sequence similarity. For enzyme activity classes, many targets were pharmacologically similar, with the higher ligands chemical similarity, but sequence dissimilar [5]. Research has also shown that linkage between two targets determined by chemical structural similarity rather than protein sequence might be more useful for drug discovery [4, 32]. Figure 3 shows a target relation network created using the kinasespecific SEA model. For clarity of the graphic illustration, only the most significant predictions are shown in the network (P value ≤\(10^{80}\)). Despite the connection inside the subfamily of kinase, more than half (105 of 202) of the connections were across kinase subfamilies. For example, serine/threonineprotein kinase PAK7 and AMPactivated protein kinase alpha2 subunit share 374 active compounds, and 16 of them are drugs; therefore there is a linkage between these two targets, although they are biologically unrelated (belonging to the STE protein kinase group and the CAMK protein kinase group, respectively).
Conclusion
In this work, we tested different aspects of SEA models, with the purpose of improving the accuracy rate of an SEA, indicating the activity threshold selection and the use of classspecific sets. The results showed that using stricter (activity cutoffs of 1 or 0.1 μm) and more specific training data could improve the prediction accuracy rate of the SEA model but at the price of a smaller number of correct, positive predictions, indicating a higher false negative rate. To investigate the fuzzy nature of fingerprints, 3 pharmacophore fingerprintbased SEA models were constructed and the comparison indicated that fuzzy fingerprints can yield larger numbers of predictions with overly rough representation, which could lead to very low accuracy rates or even an impractical model. The comparison results of five different models showed that the Topological fingerprintbased SEA model outperformed the other models with the highest precision rate, and the Atom pairbased fingerprint yielded the greatest number of correct, positive predictions. The overall effectiveness of all of the models could be ranked in decreasing order as follows: Atom pair \(\approx\) Topological> Morgan> MACCS> Pharmacophore. Although most of the predictions of each model were overlapped, the multivoting model showed that combining multiple SEA models is a promising method for target prediction. With a tunable vote number, the multivoting scheme can be flexible in its results, with either a high quality of prediction or a greater number of potential alternatives. It should be noted that the test results in this paper were optimistic because the test set used consisted of newly published data; thus, there were a great number of predictions that could not be proved for now and were not considered in the test results. Targetspecific SEA could also improve the prediction accuracy.
An inherent assumption that molecules with similar structures tend to have similar responses to a target underlays SEA method. Thus, the challenge of improving SEA seems to be the same as “the traditional” ligandbased drug discovery methods, such as Quantitative StructureActivity Relationship or Virtual Screening. These methods suffered from the problem of the activity cliff, which is defined as pairs of structurally similar molecules with large differences in potency [33, 34]. Fingerprints capable of distinguishing these compounds [28] could be used to improve SEA models.
Abbreviations
 AUC:

area under receiver operating characteristic curve
 ECFP_4:

extended connectivity fingerprint with radius equals 4
 EVD:

extreme value distribution
 MACCS:

molecular access system
 MW:

molecular weight
 RS:

raw Score
 SEA:

similarity ensemble approach
 SHED:

Shannon entropy descriptors
 TC:

tanimoto coefficient
 TS:

tanimoto threshold
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Authors’ contributions
ZW and LL collected the dataset. ZW and LL performed the study. JL, ZY and ZW wrote the paper. All authors read and approved the final manuscript.
Acknowledgements
This work was supported by the National Basic Research Program (973 Program, No. 2011CBA00800 and No. 2013CB911100).
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The authors declare that they have no competing interests.
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13321_2016_130_MOESM7_ESM.pdf
Additional file 7. Figure S1 The average similarity of five fingerprints (Atom pair, Morgan, MACCS, Topological and Pharmacophore, which are implemented in RDKit package [http://rdkit.org/].) and SHED descriptor. The similarity criteria for SHED is the normalized Euclidean distance (see the main manuscript) and for the other five fingerprints are Tanimoto coefficient. Figure S2 Statistical model fits for Morgan based SEA on the random background data set create from ChEMBL 19. Figure S3 Zscore distribution (Morgan fingerprint) of the random background data set created from ChEMBL 19 database. Figure S4 The predictive performance of different vote schemes with significant level Pvalue ≤ 0.01. The upper plot illustrates the total number of positive (in red) and true positive prediction (in blue), and the lower plot is the corresponding precision.
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Wang, Z., Liang, L., Yin, Z. et al. Improving chemical similarity ensemble approach in target prediction. J Cheminform 8, 20 (2016). https://doi.org/10.1186/s133210160130x
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DOI: https://doi.org/10.1186/s133210160130x
Keywords
 Fingerprint
 Similarity
 Offtarget effect
 Target identification