BACKGROUND
tuberculosis  caused by mycobacterium tuberculosis, is one of the major causes of morbidity and mortality in the developing world.
tuberculosis manifests in many clinical forms from an active disease state to clinical latency that can extend for decades.
it has been reported that there have been  <dig>  million new cases of tb with an estimated global mortality of  <dig>  million in the year  <dig>  <cit> .
recent reports suggest that a high proportion, averaging about 85% cases have been accounted to occur in asia and africa with india and china alone accounting to 50% of the total burden of disease  <cit> .
evidences also point to an increasing incidence of drug-resistant tb over the past decade  <cit> .
in addition, its catastrophic synergy with acquired immune deficiency syndrome  have made it a major health concern worldwide  <cit> .
the present drugs used in the first line therapy of tuberculosis has been discovered at least half a century ago, and the unabated global rise of tuberculosis calls for the development of novel tools and methods for fast and efficient identification of novel molecules with anti-tubercular activities.
with the lack of comprehensive systems level understanding of the causative organism and its intricate biological pathways and control mechanisms, it has been suggested that whole cell phenotypic screens offer a better proposition in comparison with single gene based biological screens  <cit> .
the availability of methods for high-throughput screening has significantly contributed for many such datasets being made available in public domain.
with the very low hit rate for such high-throughput biological screens, it has become inevitable to prioritize molecules to be taken up for biological screens.
it has been suggested out that virtual screening of large compound libraries using computational methods like machine learning techniques could be efficiently employed as a complementary approach to phenotypic screens in drug discovery  <cit> .
the availability of small molecule bio-assay datasets in public domain provide a valuable means to build predictive computational models that can be potentially used to prioritize molecules for biological assays from large digital databases  <cit> .
we have previously  <cit>  used machine learning approaches to classify inhibitors identified from bioassay screens of mycobacterium tuberculosis in middlebrook 7h <dig> broth  <cit> .
the 7h <dig> media  enriched middlebrook 7h <dig> broth) allows rapid recovery of mycobacteria from clinical specimens, sputum, and respiratory secretions.
while the adc enrichment provides more sensitivity to the microbial culture, albumin acts as a protective agent by binding free fatty acids, which may be toxic to mycobacterium species and catalase destroys toxic peroxides that may be present in the medium and dextrose acts as an energy source.
in our present study we make use of a confirmatory screen that identifies novel anti-tubercular inhibitors of mycobacterium tuberculosis in 7h <dig> broth supplemented with glycerol and tween  <dig> for improved growth; the media is principally used for growth of axenic cultures of mycobacteria.
the library of compounds used in current bioassay excluded known inhibitors from previously pursued compounds and their analogs, on which our earlier study was based.
although classification methods using machine learning approach are valuable tools in rapid virtual screening of compound libraries  <cit> , they have been seldom used in tb drug discovery programmes  <cit> .
our present work marks an effort in this direction to make predictive models for prioritization and/or discovery of novel active molecules that can be taken up further in the drug discovery pipeline for tuberculosis.
RESULTS
the dataset  used in this study is a confirmatory bioassay screen to identify novel compounds that inhibit mycobacterium tuberculosis in 7h <dig> media.
the dataset consists of  <dig> , <dig> tested compounds with  <dig> actives,  <dig> , <dig> inactives and rest are inconclusive compounds.
inconclusive compounds were not considered in this study to avoid uncertainty in the predictive ability of the generated models.
a total of  <dig> descriptors were calculated  and data processing was done as described in the "methods" section.
after removing un-informative bit string descriptors , only  <dig> descriptors remained and were used for further classification and analysis.
the list of descriptors removed after data processing is provided in additional file 1: table s <dig>  the processed file was then split into training and test sets .
the training set file was converted to arff format and loaded in weka.
as the file size was very large, weka was started with a heap size of  <dig> gb to handle out-of-memory exception.
initial classification experiments were done with standard base classifiers only.
all the models obtained with the base classifiers had an fp rate well below our threshold limit i.e.
20% however the resulting high accuracies were not a good representation of our dataset because it is highly imbalanced, so cost sensitivity was introduced using cost matrix to produce a more reliable predictive ability of the classifier in use.
misclassification cost for false negatives was raised incrementally so as to stay in the upper limit of false positives.
thus a number of models were trained based on differential cost settings.
the fn cost that resulted in the best predictive models for each of the individual classifiers is depicted in table  <dig>
the performance statistics of best classification models obtained with each classifier are represented in table  <dig>  all the results reported here are based on independent testing and not on the training.
since a number of models were trained on each dataset using different cost settings, best models of each dataset in each classifier category were selected based on various binary classification measurements.
all generated models had a controlled fp rate.
roc curve analysis is considered as one of the best and reliable approach for performance characterization of virtual screening protocols therefore, the roc curve and auc values are widely employed for evaluating the discriminatory power of virtual screens.
the roc curve analysis from figure  <dig> revealed that out of the four classifiers used in this study, smo covers the maximum area under the curve  followed by random forest, naïve bayes and j <dig>  an auc value close to  <dig> is considered significant in data analytics.
in order to make out the classifier's ability to efficiently identify actual positive and negative labels, a measure of sensitivity  and specificity for each dataset was used respectively .
an optimal prediction aims to achieve 100% sensitivity and specificity.
all classifiers were highly specific in their predictions with specificity more than 80% and in terms of sensitivity smo appeared to be the most sensitive among all.
*csc denotes costsensitiveclassifier, # bcr denotes balanced classification rate
though all the models built using the four state-of-the-art classifiers had accuracies above 80% but due to the class imbalance problem in the data, bcr was used to assess the robustness of the models.
a consistent bcr gave a precise estimation of overall model efficiency as it equally weights the errors within each class.
though all models are observed to have equivalent predictive ability, smo turns out to be the best among all with high sensitivity, maximum roc value and highest bcr rate.
the general aim of any virtual screening method is to retrieve a significant portion of true positives from a database being screened over random compound selection.
in order to quantify the enrichment produced by in-silico screening, we calculated enrichment factor  on variable dataset sizes.
popular efs for early enrichment rates are usually calculated at 1%, 2%, 5% and 10% of the screening database.
the ef values obtained with our best model i.e.
smo were  <dig>  ,  <dig>  ,  <dig>   and  <dig>  .
these values suggest that our model is able to achieve 3- <dig> fold enrichment over random screening.
thus for the given dataset under study, smo is proposed to be the best classifier for identifying inhibitors from axenic culture of mycobacterium tuberculosis.
CONCLUSIONS
in the present analysis of publicly available bio-assay datasets for anti-tubercular activity in vitro, we show that machine learning approaches can be efficiently used to build predictive classifiers for anti-tubercular activities.
high auc values and reasonable bcr rates suggest that these predictive models can serve as an effective filter to screen large chemical libraries.
the major caveat of this approach is that the prioritization of the molecules are target-agnostic and may at times would not have any biological correlate given the present understanding of the biological processes and needs to be used in conjunction with other molecular biology techniques to decipher the targets and mechanisms of action.
the amenability of a wide variety of bio-assay datasets now in public domain makes it possible to create classifier models based on them.
this offers the potential to apply multiple models based on additional properties like toxicity, bio-availability, metabolic processes etc., in conjunction to filter large molecular libraries in-silico before being taken up for biological screens.

 tuberculosis caused by mycobacterium tuberculosis, is one of the major causes of morbidity and mortality in the developing world. tuberculosis manifests in many clinical forms from an active disease state to clinical latency that can extend for decades. it has been reported that there have been million new cases of tb with an estimated global mortality of million in the year. recent reports suggest that a high proportion, averaging about 85% cases have been accounted to occur in asia and africa with india and china alone accounting to 50% of the total burden of disease. evidences also point to an increasing incidence of drug-resistant tb over the past decade. in addition, its catastrophic synergy with acquired immune deficiency syndrome have made it a major health concern worldwide. the present drugs used in the first line therapy of tuberculosis has been discovered at least half a century ago, and the unabated global rise of tuberculosis calls for the development of novel tools and methods for fast and efficient identification of novel molecules with anti-tubercular activities. with the lack of comprehensive systems level understanding of the causative organism and its intricate biological pathways and control mechanisms, it has been suggested that whole cell phenotypic screens offer a better proposition in comparison with single gene based biological screens. the availability of methods for high-throughput screening has significantly contributed for many such datasets being made available in public domain. with the very low hit rate for such high-throughput biological screens, it has become inevitable to prioritize molecules to be taken up for biological screens. it has been suggested out that virtual screening of large compound libraries using computational methods like machine learning techniques could be efficiently employed as a complementary approach to phenotypic screens in drug discovery. the availability of small molecule bio-assay datasets in public domain provide a valuable means to build predictive computational models that can be potentially used to prioritize molecules for biological assays from large digital databases. we have previously used machine learning approaches to classify inhibitors identified from bioassay screens of mycobacterium tuberculosis in middlebrook 7h broth. the 7h media enriched middlebrook 7h broth) allows rapid recovery of mycobacteria from clinical specimens, sputum, and respiratory secretions. while the adc enrichment provides more sensitivity to the microbial culture, albumin acts as a protective agent by binding free fatty acids, which may be toxic to mycobacterium species and catalase destroys toxic peroxides that may be present in the medium and dextrose acts as an energy source. in our present study we make use of a confirmatory screen that identifies novel anti-tubercular inhibitors of mycobacterium tuberculosis in 7h broth supplemented with glycerol and tween for improved growth; the media is principally used for growth of axenic cultures of mycobacteria. the library of compounds used in current bioassay excluded known inhibitors from previously pursued compounds and their analogs, on which our earlier study was based. although classification methods using machine learning approach are valuable tools in rapid virtual screening of compound libraries, they have been seldom used in tb drug discovery programmes. our present work marks an effort in this direction to make predictive models for prioritization and/or discovery of novel active molecules that can be taken up further in the drug discovery pipeline for tuberculosis.  the dataset used in this study is a confirmatory bioassay screen to identify novel compounds that inhibit mycobacterium tuberculosis in 7h media. the dataset consists of, tested compounds with actives,, inactives and rest are inconclusive compounds. inconclusive compounds were not considered in this study to avoid uncertainty in the predictive ability of the generated models. a total of descriptors were calculated and data processing was done as described in the "methods" section. after removing un-informative bit string descriptors, only descriptors remained and were used for further classification and analysis. the list of descriptors removed after data processing is provided in additional file 1: table s the processed file was then split into training and test sets. the training set file was converted to arff format and loaded in weka. as the file size was very large, weka was started with a heap size of gb to handle out-of-memory exception. initial classification experiments were done with standard base classifiers only. all the models obtained with the base classifiers had an fp rate well below our threshold limit i.e. 20% however the resulting high accuracies were not a good representation of our dataset because it is highly imbalanced, so cost sensitivity was introduced using cost matrix to produce a more reliable predictive ability of the classifier in use. misclassification cost for false negatives was raised incrementally so as to stay in the upper limit of false positives. thus a number of models were trained based on differential cost settings. the fn cost that resulted in the best predictive models for each of the individual classifiers is depicted in table the performance statistics of best classification models obtained with each classifier are represented in table all the results reported here are based on independent testing and not on the training. since a number of models were trained on each dataset using different cost settings, best models of each dataset in each classifier category were selected based on various binary classification measurements. all generated models had a controlled fp rate. roc curve analysis is considered as one of the best and reliable approach for performance characterization of virtual screening protocols therefore, the roc curve and auc values are widely employed for evaluating the discriminatory power of virtual screens. the roc curve analysis from figure revealed that out of the four classifiers used in this study, smo covers the maximum area under the curve followed by random forest, naïve bayes and j an auc value close to is considered significant in data analytics. in order to make out the classifier's ability to efficiently identify actual positive and negative labels, a measure of sensitivity and specificity for each dataset was used respectively. an optimal prediction aims to achieve 100% sensitivity and specificity. all classifiers were highly specific in their predictions with specificity more than 80% and in terms of sensitivity smo appeared to be the most sensitive among all. *csc denotes costsensitiveclassifier, # bcr denotes balanced classification rate though all the models built using the four state-of-the-art classifiers had accuracies above 80% but due to the class imbalance problem in the data, bcr was used to assess the robustness of the models. a consistent bcr gave a precise estimation of overall model efficiency as it equally weights the errors within each class. though all models are observed to have equivalent predictive ability, smo turns out to be the best among all with high sensitivity, maximum roc value and highest bcr rate. the general aim of any virtual screening method is to retrieve a significant portion of true positives from a database being screened over random compound selection. in order to quantify the enrichment produced by in-silico screening, we calculated enrichment factor on variable dataset sizes. popular efs for early enrichment rates are usually calculated at 1%, 2%, 5% and 10% of the screening database. the ef values obtained with our best model i.e. smo were,, and. these values suggest that our model is able to achieve 3- fold enrichment over random screening. thus for the given dataset under study, smo is proposed to be the best classifier for identifying inhibitors from axenic culture of mycobacterium tuberculosis.  in the present analysis of publicly available bio-assay datasets for anti-tubercular activity in vitro, we show that machine learning approaches can be efficiently used to build predictive classifiers for anti-tubercular activities. high auc values and reasonable bcr rates suggest that these predictive models can serve as an effective filter to screen large chemical libraries. the major caveat of this approach is that the prioritization of the molecules are target-agnostic and may at times would not have any biological correlate given the present understanding of the biological processes and needs to be used in conjunction with other molecular biology techniques to decipher the targets and mechanisms of action. the amenability of a wide variety of bio-assay datasets now in public domain makes it possible to create classifier models based on them. this offers the potential to apply multiple models based on additional properties like toxicity, bio-availability, metabolic processes etc., in conjunction to filter large molecular libraries in-silico before being taken up for biological screens. 
the availability of datasets based on high-throughput biological screens or assays in public domain makes computational methods a plausible proposition for building predictive models. in the present study, we have used the data from in-vitro screens for anti-tubercular activity from a high-throughput screen available in public domain to build highly accurate classifiers based on molecular descriptors of the molecules. we show that by using four supervised state-of-the-art classifiers we are able to generate in-silico predictive models on an extremely imbalanced large dataset of anti-tubercular molecules with reasonable aroc and bcr values. we show that machine learning tools can be used to build highly effective predictive models for virtual high-throughput screens to prioritize molecules from large molecular libraries. computational methods including machine learning have been widely employed to build classifiers for high-throughput virtual screens to prioritize molecules for further analysis. the emergence of multi-drug resistant tuberculosis in pandemic proportions throughout the world and the paucity of novel therapeutics for tuberculosis have re-iterated the need to accelerate the discovery of novel molecules with anti-tubercular activity. though high-throughput screens for anti-tubercular activity are available, they are expensive, tedious and time-consuming to be performed on large scales. thus, there remains an unmet need to prioritize the molecules that are taken up for biological screens to save on cost and time.
 the emergence of multi-drug resistant tuberculosis in pandemic proportions throughout the world and the paucity of novel therapeutics for tuberculosis have re-iterated the need to accelerate the discovery of novel molecules with anti-tubercular activity. though high-throughput screens for anti-tubercular activity are available, they are expensive, tedious and time-consuming to be performed on large scales. thus, there remains an unmet need to prioritize the molecules that are taken up for biological screens to save on cost and time. computational methods including machine learning have been widely employed to build classifiers for high-throughput virtual screens to prioritize molecules for further analysis. the availability of datasets based on high-throughput biological screens or assays in public domain makes computational methods a plausible proposition for building predictive models. in addition, this approach would save significantly on the cost, effort and time required to run high throughput screens.  we show that by using four supervised state-of-the-art classifiers we are able to generate in-silico predictive models on an extremely imbalanced large dataset of anti-tubercular molecules with reasonable aroc and bcr values. moreover, these models are able to provide 3- fold enrichment over random selection.  in the present study, we have used the data from in-vitro screens for anti-tubercular activity from a high-throughput screen available in public domain to build highly accurate classifiers based on molecular descriptors of the molecules. we show that machine learning tools can be used to build highly effective predictive models for virtual high-throughput screens to prioritize molecules from large molecular libraries. 