Target prediction utilising negative bioactivity data covering large chemical space
© Mervin et al. 2015
Received: 30 April 2015
Accepted: 29 September 2015
Published: 24 October 2015
In silico analyses are increasingly being used to support mode-of-action investigations; however many such approaches do not utilise the large amounts of inactive data held in chemogenomic repositories. The objective of this work is concerned with the integration of such bioactivity data in the target prediction of orphan compounds to produce the probability of activity and inactivity for a range of targets. To this end, a novel human bioactivity data set was constructed through the assimilation of over 195 million bioactivity data points deposited in the ChEMBL and PubChem repositories, and the subsequent application of a sphere-exclusion selection algorithm to oversample presumed inactive compounds.
A Bernoulli Naïve Bayes algorithm was trained using the data and evaluated using fivefold cross-validation, achieving a mean recall and precision of 67.7 and 63.8 % for active compounds and 99.6 and 99.7 % for inactive compounds, respectively. We show the performances of the models are considerably influenced by the underlying intraclass training similarity, the size of a given class of compounds, and the degree of additional oversampling. The method was also validated using compounds extracted from WOMBAT producing average precision-recall AUC and BEDROC scores of 0.56 and 0.85, respectively. Inactive data points used for this test are based on presumed inactivity, producing an approximated indication of the true extrapolative ability of the models. A distance-based applicability domain analysis was also conducted; indicating an average Tanimoto Coefficient distance of 0.3 or greater between a test and training set can be used to give a global measure of confidence in model predictions. A final comparison to a method trained solely on active data from ChEMBL performed with precision-recall AUC and BEDROC scores of 0.45 and 0.76.
A principle challenge faced by the information gleaned from phenotypic screening is that many of the assessed compounds remain orphan ligands, as the respective mode-of-action (MOA) remains undetermined in the first instance . Consequently, the subsequent identification of the modulated targets for active compounds, known as ‘target deconvolution’, is required . Various biochemical affinity purification methods provide direct approaches to discover target proteins binding small molecules of interest for this purpose [3–6]. Although these elucidation experiments can explicitly determine compound-target interactions, such procedures are costly and time-consuming [4, 7, 8]. These procedures require large amounts of protein extract and stringent experimental conditions, while many techniques have also been deemed best suited for situations where a high affinity ligand binds to a protein [1, 9]. Other difficulties involve the challenge of preparing and immobilizing affinity reagents (targets) that still retain their cellular activity (i.e. ensuring that proteins still interact with the small molecule while it is bound to the solid surface) . Such caveats are responsible for the increased interest in novel computational target deconvolution strategies for drug discovery .
In silico protein target prediction is a well-established computational technique that offers an alternative avenue to infer target-ligand interactions by utilizing known bioactivity information . These methods have played an important role in the field of efficacy prediction and the prediction of toxicity [13–17]. Such approaches are designed to predict targets for orphan compounds early in the drug development phase, with the predictions forming the base of an experimental confirmation afterwards. Both structure-based and ligand-based methods exist for the prediction of protein targets for small molecule ligands [12, 18–22]. The former methods generally describe approaches that exploit the structural information of the protein, combined with scoring functions, in an attempt to predict ligand–target pairs [23–25]. Ligand-based methods investigate an identified structure–activity relationship (SAR) space, using similarity searching on large numbers of annotated protein–ligand pairs obtained from chemogenomic databases [26–29]. Such predictive models are founded upon the principle of chemical similarity, relying on the relevant similarities of compound features from targets which are likely to be responsible for binding activity [30, 31]. The focus of this work is concerned with improving current ligand-based methods for target prediction.
Similarity searching for ligand-based target prediction is considered the simplest form of in silico target prediction and has long been established within the literature [12, 17, 22, 27, 32–38]. In these models, predictions are based on the principle of molecular similarity to identified bioactive compounds from chemogenomic databases [31, 35]. The simplistic nature of these methods means that they are only able to consider the structure of a molecule as a whole, which significantly hinders the predictive power of these models. Building on this theme, data-mining algorithms are capable of considering multiple combinations of compound fragments by applying pattern recognition techniques. They have gained traction in target prediction due to their demonstrated ability to more efficiently extrapolate predictions , and are less time consuming when compared to other approaches such as molecular docking . One of the earliest and most widely used examples of data-mining target elucidation is the continuously curated and expanded Prediction of Activity Spectra for Substances (PASS) software , which was assimilated from the bioactivites of more than 270,000 compound-ligand pairs. The authors trained the model on multilevel neighbourhoods of atoms (MNA) descriptors, producing predictions based on Bayesian estimates of probabilities.
The Naïve Bayes (NB) classifiers are a popular family of algorithms used for the prediction of bioactivity for compounds [39–42]. These methods offer a quick training and prediction times and are relatively insensitive to noise . Nidhi et al. , employed a multi-class Naïve Bayes classification algorithm trained on a data set comprised of over 960 target proteins extracted from the ‘World Of Molecular BioAcTivity’ (WOMBAT) database . Another target prediction algorithm developed by Koutsoukas et al. , is able to predict structure activity relationships (SARs) for orphan compounds using either a Laplacian-modified Naïve Bayes classifier or a Parzen-Rosenblatt Window (PRW) learner. The algorithm was trained on more than 155,000 ligand–protein pairs from the ChEMBL14 database , encompassing 894 different human protein targets. After benchmarking experiments , it was found that the PRW learner outperformed the Naïve Bayes algorithm overall, achieving a recall and precision of 66.6 and 63.3 %, respectively.
Support Vector Machines (SVMs) have also been employed for task of target prediction. These models have utilised smaller amounts of data in comparison to NB models, mostly due to the computational expense of the SVM algorithm. Examples of SVM methods include a model obtaining more than 80 % sensitivity consistently for five different protein targets ; however the limitation of this implementation to five molecular targets only allows for a very narrow view of compound activity. Nigsch et al. . investigated the use of a Winnow algorithm for target prediction and compared the learner to the performance of a Naïve Bayes model, comprising a set of 20 targets extracted from WOMBAT. The comparison of the overall performance of the two algorithms did not yield significant differences, but the overall findings supported the view that ensembled machine-learning models could be used to yield superior predictions.
Ligand-based predictions provide a basis for further analysis when attempting to rationalise mechanism-of-action. Liggi et al. , extended the predictions from a PRW target prediction algorithm, by annotating enriched targets implicated with a cytotoxic phenotype, using associated pathways from the GO [47, 48] and KEGG [49, 50] pathway databases. The annotation of enriched targets with pathways gave improved insight into understanding the MOA of the phenotype, underlining the signalling pathways involved in cytotoxicity. Other examples include the combination of target prediction and classification tree techniques for rationalising phenotypic readouts from a rat model for hypnotics . The authors derived interpretable decision trees for the observed phenotypes and inferred the combination of modulated targets which contribute towards good sleeping patterns. A review of the known mechanisms of hypnotics suggested that the results were consistent with current literature in many cases.
Such publications illustrate the real-world application of predictions obtained from in silico target prediction methods for both on-target and off-target bioactivity predictions . Although most of these methods produce a probability of activity for an orphan compound against a given target, the approaches mentioned here do not utilise inactive bioactivity data . The objective of this work is hence concerned with the construction of an in silico target prediction approach that is able to consider both the probability for activity and inactivity of orphan compounds against a range of biological targets, thus, giving a more holistic perspective of chemical space for factors that contribute and counteract bioactivity.
The realized target prediction tool is validated both through cross-validation and an external validation set. Five-fold cross validation was employed to evaluate the model and was also used to generate class-specific activity thresholds, based on the optimum cut-off value for a range of different metrics. The performance of the tool when applied to test data extracted from the WOMBAT database is also evaluated. This provides insight into the models ability to extrapolate predictions to a real-world setting. Finally, a comparison to a model generated solely from activity data was also conducted to evaluate the value of the incorporation of inactivity data in the models.
Results and discussion
Internal validation results
The high density of points towards the top right of the plot of Fig. 1 depicts that a significant number of models obtained high precision and recall values. Upon further investigation, it was found that 141 of the 157 targets (89.8 %) that score precision and recall scores above 0.97 belong to targets comprised of sphere excluded (SE) inactive compounds. Such a high performance is a result of the sphere exclusion (SE) algorithm that requires that molecules must be suitably dissimilar from actives. In these cases, the SE inactive compounds can more easily be distinguished apart actives in comparison to PubChem inactive compounds during cross validation. Conversely, due to the absence of a dissimilarity selection requirement, experimentally confirmed inactive compounds from PubChem are likely to be more skeletally similar to actives from ChEMBL, as inactive compounds tend to originate from structurally similar scaffolds to actives (Additional file 1: Table S1). This trend blurs the boundary of the hyperplane between the active and inactive classes. The results from internal validation also indicate that the models frequently perform with low recall, which is most likely a consequence of the class imbalance of the data, when active compounds are predicted as false negatives due to the apparent over representation of features in the inactive classes.
External validation results
Validation using WOMBAT
The performance of the models was analysed using an external data set extracted from WOMBAT. It is important to recognize that the bioactivity information included for the compounds-protein associations contained in WOMBAT do not contain experimentally confirmed inactive compounds, and that molecules without an annotation for a target are simply considered as inactive for that protein. Although this assumption may be correct in many cases, there are likely to be certain instances when a compound may actually be active. If these compounds were correctly annotated as active by the target prediction tool (a ‘false false positive’, and hence a ‘true positive’), this prediction would be penalised as a false positive due to the assumptions applied by the test data.
Average precision and recall for the different set of thresholds applied to WOMBAT
The precision values achieved by the models may initially seem low, but when this is compared to the values expected at random across all of the 1080 models (which would be in the area of ca. 1/1080, so about 0.1 % for each), the models are actually capable of retaining comparably high precision. The performance for the application of the F1-score, accuracy, precision and recall decision-based thresholds are also shown in Fig. 4. The results show a trade-off between precision and recall metrics for the models, with the F1-score, accuracy and precision metrics obtaining visually similar distributions. In comparison to the other metrics, the recall-based thresholds produce a distinctly separate profile with elevated recall and lowered precision, with many of the model performances populating an area of high recall and precision.
The varying performance profiles generated for each metric produce different model behaviour, and an ideal activity cut-off is dependent on the question proposed by a user and the application of the target predictions. For example, the stringent thresholds generated using the precision metric can be applied in cases such as candidate safety profiling, when outputs require few false negatives and accurate identification of dangerous compounds. Conversely, recall-based thresholds may be applied in circumstances requiring lenient assurance of predicted targets, perhaps during hit identification stages and mechanism-of-action studies. These thresholds will increase the chance of returning a more complete list of truly targeted proteins, at the cost of increasing the numbers of false positives.
A summary of decision threshold performance upon application to the WOMBAT database
The precision threshold is calculated to be the most enriched metric when considering the average and median enrichment scores for the models (Table 2). Upon application of this threshold boundary, 374 of the 418 targets benefitted, producing a positive enrichment score. Indeed, 154 of the targets show significantly high (≥0.5) enrichment values, revealing that the binary predictions produce considerably superior predictions in many cases. Overall, the enrichments in F1 values support the view that Precision-based thresholds provide the optimal metric when considering a medium between precision and recall.
Analysis of the applicability domain
Influence of sphere excluded molecules for targets with 1000 or more confirmed inactives
Comparison to an activity-only based model and other tools for target prediction
Average precision and recall in the Top-k positions achieved for WOMBAT using the activity-only based model
Active data set
ChEMBL  (Version 18) was used to construct the active bioactivity training data set. This version of the database encompasses over 12 million manually curated bioactivites, spanning over more than 9000 protein targets and over 1 million distinct compounds. Bioactivities were extracted for activity values (IC50/EC50/Ki/Kd) of 10 μM or lower, with a CONFIDENCE_SCORE of 5 or greater for ‘binding’ or ‘functional’ human protein assays. The 10 μM cut-off for activity specified here is in accordance with the method employed in the study of Koutsoukas et al. , representing both marginally and highly active compounds. Finally, ChEMBL polymer_flag and inorganic_type flags were included to ensure only suitable compounds qualified for the resulting training set.
After extraction, compound SMILES were standardized using the ChemAxon Command-Line Standardizer , with options “Remove Fragment” (keep largest), “Neutralize”, “RemoveExplicitH”, “Clean2d”, “Mesomerize”, and “Tautomerize” specified in the configuration file. The standardized canonical SMILES were then filtered for small or large compounds (100 Da < MW < 900 Da) and checked for duplicate ligand structures to ensure only one set of protein–ligand pairs were retained. Protein classes comprising less than 10 compounds were discarded since they did not comprise sufficient amounts of training data to learn from.
The complete active data set encompasses over 295,000 bioactivities covering 1080 protein classes. A large proportion of the classes included in the model are enzymes and membrane receptors, encompassing 57 and 17 % of the data respectively. A considerable percentage of compounds (33 %) were annotated for more than one target.
Effect of confidence score on activity data sets
Descriptions used for the ChEMBL confidence scores
Default value—target assignment has yet to be curated
Target assigned is non-molecular
Target assigned is molecular non-protein target
Multiple homologous protein targets may be assigned
Multiple direct protein targets may be assigned
Homologous protein complex subunits assigned
Direct protein complex subunits assigned
Homologous single protein target assigned
Direct single protein target assigned
Ranges of confidence scores (between ≥5 and ≥9) were applied to the ChEMBL18 database and the structure of the data sets extracted and the number of classes were calculated (Additional file 1: Table S2). A confidence score of 5 gives 1080 target activity classes containing 10 or more data points, with the number of classes decreasing with higher scores. A total of 306 classes (28.4 % of the data) were removed between the scores 5 to 9. The sharpest increase in mean and median class size and the total number of classes are observed between the scores 9 to 8, increasing from 774 to 959, respectively. A confidence score of 5 was applied in this study, since this threshold enables data with “multiple direct protein targets”, providing a suitable trade-off between confidence of the reliability of the data and training data size. Higher score values exclude “Homologous multi-domain protein associations” (scores ≥6) and “Direct multi-domain protein associations” (scores ≥7), such as the GABA receptors.
Inactive data set
In order to ensure only appropriate molecules are retained in the inactive data set, RDKit was used to flag structures without a carbon molecule and molecules containing unwanted heavy metals, such as Lithium, Beryllium, Boron, Fluorine, Sodium, Aluminum, Silicon, Argon, Titanium, Iron, Zinc and Bromine. The filtered SMILES were subject to the same ChemAxon standardization filtering as the active ChEMBL bioactivity data set, i.e. duplicate molecules were removed, including compounds with a MW of below 100 or above 900 Da.
The PubChem inactive data set includes more than 194 million protein–ligand pairs spanning approximately 648,000 distinct compound structures (Additional file 1: Table S3). More than 647,900 of these compounds were annotated for two or more protein targets, producing a well-populated matrix of inactive-compound annotations. The annotation overlap for target-annotated chemistry is due to PubChem being initially established to be the storage site for automated high-throughput biological assays testing standardized libraries of compounds . Secondly, it was also the central intention for the National Institutes of Health (NIH) Molecular Libraries program to have many of the laboratories test the same compounds on a wide range of targets, in an attempt to create a useful repository for mining in the future [59, 60].
Sphere exclusion for putative inactive sampling
The data extracted from PubChem contains a very large variance in the number of inactives extracted for each of the targets, with some encompassing little or no experimental inactives. Mixing experimental inactives with putative inactives would alleviate this issue, at the cost of producing models that are no longer trained solely on confirmed evidence of inactivity. Although this caveat may remove the benefit of predicting on truly inactive data from the current state of the models, refraining from any additional sampling would result in overtly imbalanced training data for 480 targets, which would otherwise require removal.
The procedure was applied to 480 target classes that consist of few or no inactive compounds which produce an active-inactive ratio smaller than 1:100. This process sampled approximately 11 million additional inactives for the required targets. For situations with targets with very high numbers of inactive compounds, undersampling was performed through the randomised removal of instances from the inactive class to accomplish the desired ratio. The complete data set of 206,559,765 ligand-target pairs from both the active and inactive classes is available for use as a benchmark data set (see Additional file 1).
RDKit  was used to generate hashed ECFP_4 circular Morgan fingerprints  with a 2048 bit length. ECFP fingerprints were selected as they have previously been shown to be successful when attempting to capture relevant molecular information for in silico bioactivity prediction [39, 64].
We have implemented a method for the extraction of inactive compounds from the PubChem repository. The application of a sphere exclusion algorithm enabled the oversampling of additional inactive compounds for targets with insufficient number of inactive compounds.
The realised target prediction protocol has been packaged and available for download at https://github.com/lhm30/PIDGIN.
The results from the internal and external validation of the tool show differing performance between the breadth of models. Recall and precision performance is influenced significantly on the underlying intraclass similarity and the number of compounds for a target. During internal validation, sphere exclusion models perform better in comparison to PubChem inactive classes, due to the dissimilarity requirement between the active compounds and SE inactive compounds.
The external performance of the models showed a considerable drop in precision, which may be exaggerated due to the absence of inactivity information held within the WOMBAT data set. Applications of target-specific thresholds exhibited a trade-off between recall and precision and indicate that different metrics for thresholds should be used for different applications of the target prediction tool. The precision threshold gleans the highest F-Score performance, producing a close balance between the precision and recall (0.5 and 0.48) for the classes. In comparison, the recall threshold can be applied to generate thresholds when predictions require high recall without the concern for the cost of sacrificing precision.
A distance-based analysis of the applicability domain (AD) for WOMBAT compounds showed that the reliability of the predictions from the models improved with the increasing similarity between the training and WOMBAT test set. The AD analysis indicated that an average Tc cut-off distance between test and train of 0.3 could be incorporated into the predictions of the classes in the future, to give insight into the degree of confidence for the probabilities generated by the model.
A comparison between the inactivity-inclusive models and the activity-only based approach showed the benefit in including negative bioactivity data when building the target prediction models with statistical significance. The ability to take into account the features that contribute and counteract bioactivity, combined with the ability to create individual target models, results in superior precision, recall, precision-recall AUC and BEDROC values when compared to models trained solely on activity data.
Model training using Bernoulli the Naïve Bayes classifier implemented in Scikit-learn
The Naïve Bayes algorithm was selected due to its basic implementation and demonstrated ability to perform in a variety of target prediction settings [28, 39, 40]. The specific classification algorithm of choice for this study is the Bernoulli Naïve Bayes algorithm, due to the ability of the algorithm to interpret the binary bit string features used to describe compound inputs. In comparison to other methods, this algorithm is also capable of maintaining its predictive power for highly imbalanced datasets, which is particularly beneficial when attempting to utilise large numbers of negative instances in training data [65, 66]. For example, it has been shown that enlarged negative training set sizes hinder the recall performance of the SMO, Random Forest, Ibk and J48 algorithms . The preferable ratio of active to inactive compounds for these methods was found to be only around 1:9, a significant decrease from the 1:100 ratio envisaged for this study. Such algorithms would therefore require large scale undersampling of inactives data points to obtain acceptable performance, thus sacrificing the coverage of inactivity space.
This algorithm explicitly penalizes the non-occurrence of a feature i that is indicative of activity class C . For bioactivity prediction, this is recognized as the differential ability for the treatment of negative evidence within the fingerprints, i.e. a 0-bit being interpreted as the absence of an atom environment feature in a molecule . This allows the models to explicitly identify trends including the absence of features within molecules.
Internal model evaluation of the models
Target-specific activity threshold generation
Target-specific activity threshold values were calculated and employed to weight the learner algorithm to avoid the degenerate situation that can arise from class imbalance. Such thresholds allow the models to more precisely generate binary predictions based on a calculated probability of activity, i.e. activity if Pa ≥ threshold.
Finally, the overall performance of each threshold is averaged for each of the folds, and the threshold with the optimal performance over the five-fold yields the optimum decision threshold for that specific target. This process is conducted for each of the performance metrics to produce metric-specific thresholds which will be discussed in more detail in the results section.
External model evaluation
The WOMBAT  database contains compounds that are not annotated in the PubChem and ChEMBL databases. A data set of annotated compounds was extracted from the WOMBAT database (version 2011.1) considering activities for Ki, IC50, Kd or EC50 with values of 10 μM or smaller. Bioactivites also contained in the ChEMBL or PubChem training sets (identified as Tanimoto values between structures of 1.0) were removed, resulting in the removal of 3624 WOMBAT compounds. In total 65,123 active compounds were retained for testing, comprising 418 protein target classes. Performance of the models was measured using precision and recall after application of the various class-specific binary thresholds (previously calculated) for each target.
Evaluation of class-specific thresholds
Applicability domain estimation
A major problem regarding the practical applications of target prediction models is the unassessed reliability of the predictions . The function of the applicability domain (AD) is to indicate when the assumptions made by a model are fulfilled and which input chemicals are reliably appropriate for the models [8, 74, 75]. A distance-based AD approach was employed to analyse the distances between a query compound to the nearest neighbour in the training data . The distance from the test and training set is then cross-referenced with the probability score for activity for active compounds from WOMBAT, giving a measurement for the active prediction performance of the models.
Construction and performance evaluation of a model trained on activity only
A single model was trained based on activity data from ChEMBL exclusively. More specifically, a Bernoulli Naïve Bayes algorithm was trained on the fingerprints from the target-compound associations from the 1080 targets extracted from the ChEMBL dataset. Similarly to the previous models, 2048 bit Morgan fingerprints were imported into Scikit-learn class, giving a model containing almost 300 thousand active compounds. The probability scores generated by this model produce predictions that a compound is active for a given target when considering the probability that the molecule is also active for the other targets.
LHM generated the ChEMBL and PubChem data sets, implemented and evaluated the algorithms presented in this work, and wrote this manuscript. GD helped implement the class-specific activity threshold calculation. AMA contributed to the validation of the models. AB and OE conceived the main theme on which the work was performed and ensured that the scientific aspect of the study was rationally valid. All authors read and approved the final manuscript.
The authors thank Krishna C. Bulusu for proof reading the manuscript. LHM would like to thank BBSRC and AstraZeneca and for their funding. GD thanks EPSRC and Eli Lilly for funding.
The authors declare that they have no competing interests.
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