BACKGROUND
malaria is a major health problem across the world, more so in the tropics and especially in developing nations  <cit> .
according to the recent world malaria report, released by the world health organization  in  <dig>  there were about  <dig> million cases of malaria across the globe and  <dig>  million deaths in 2010; with highest mortality found within children living in africa  <cit> .
malaria is a mosquito-borne disease and is caused by protozoan parasites belonging to the genus plasmodium.
p. falciparum, p. vivax, p. ovale and p. malariae are the four species of the parasite which are routinely implicated as the causative agents in humans, with p. falciparum being the most commonly encountered and deadliest amongst them all and associated with 90% of the fatalities in africa  <cit> .
endemic to the tropical and subtropical regions of africa, asia, south and central america where hot and humid climatic conditions prevail, malaria has been indicated as a major constraint to economic development  <cit> .
one of the major roadblocks in the adequate control of malaria has been the limited therapeutic options available for its treatment.
the current commonly used classes of drugs are limited to aminoquinolines and their derivatives such as arylamino alcohols, methanols, biguanides, diaminopyrimidines and antimalarial endoperoxidases.
chloroquine and primaquine have been extensively used for the treatment and prophylaxis of malaria  <cit> .
however, widespread drug resistance to available therapeutic agents and the emergence of multi-drug resistant strains has resulted in limited treatment options  <cit> .
the current pipeline for drug discovery of anti-malarials is also limited, with just  <dig> products in clinical trials and  <dig> in preclinical stages of development  <cit> .
large scale collaborative initiatives have made it possible to assemble large datasets of chemical structure information online  <cit> .
this has been complemented by the annotation of biological activities of these molecules.
many of the biological activities have been derived by high-throughput bioassays made possible by recent advances in automation of these assays.
the availability of chemical structure and bio-activity information in standardized forms provide immense opportunities for creating predictive computational models to understand the correlation between chemical properties and their activities and also opens up the possibility to create predictive computational models for bio-activities  <cit> .
these predictive models make it possible to computationally screen large molecular datasets thereby offering a possibility to improve the hit-rate and thereby reduce the overall costs of drug discovery.
we have also previously successfully generated such predictive models for anti-tubercular molecules  <cit>  and for small molecule modulators of mirna  <cit> .
in the present study, we applied the machine learning technique to create classification models from high-throughput screens of anti-malarial agents that inhibit the development of the apicoplast in the malaria parasite, p. falciparum.
in addition, we used a maximum common substructure  based approach to identify substructures enriched in the bioactive molecules.
our result suggests that efficient and accurate computational predictive models could be built to screen large datasets in silico and could be potentially used to prioritize molecules for high-throughput screens.
RESULTS
descriptor generation and model construction
initially, a total of  <dig> 2d molecular descriptors were generated for the active and inactive datasets downloaded from pubchem.
after data processing, as explained in methods section, the number of descriptors was reduced to  <dig> , since not many descriptors were removed after data processing, we assumed the compounds to be structurally diverse.
as the dataset used in the study was large, the heap-size in weka was increased to 4 gb to handle out-of-memory exception.
the initial experiments were done using standard base classifiers; however, to reduce the rate of false negatives, cost sensitivity was introduced in classifiers using the meta-learners.
misclassification cost was set for false negatives and was incremented so as to stay around the upper limit of false positives .
as expected, introducing cost for each of the classifier resulted in an increase in the number of true positives and decrease in the number of false negatives thereby increasing the robustness of the model.
the final misclassification cost used for each classifier is presented in table  <dig>  the naive bayes classifier required the smallest misclassification cost setting and was also the fastest in building the model.
*csc denotes costsensitiveclassifier,#balanced classification rate.
model evaluation
a number of models were trained using 5-fold cross validation on the training dataset using different misclassification cost settings for false negatives until cost optimized models were obtained.
the best model for each classifier nb, rf and j <dig>  was chosen based on their performance evaluated using different statistical measures .
all statistical results reported in table  <dig> are based on independent test set and not on the training set.
the overall efficiency of a classifier in generating the models was judged from the accuracy.
the accuracy for all the models came out to be around 75% .
sensitivity and specificity plots were used for identifying the best models for each dataset for evaluating the effectiveness of the classifier in correctly identifying positive and negative labelled instances .
the specificity for all the models was approximately 80% and the sensitivity ranged from 40-50% with rf being the most sensitive classifier for the dataset and nb the least sensitive.
since our dataset was highly imbalanced, accuracy alone cannot be used as a reliable statistical measure for assessment of the classifiers performance.
in addition to this, other performance measures were employed to check the robustness of the model which included the bcr rate and roc curve analysis.
the balanced accuracy values turned out to be satisfactory for all the models with best for random forest , being more accurate than naive bayes and j <dig>  roc curve analysis has been widely accepted as one of the most reliable approach for quick performance assessment of virtual screening approaches therefore, it has been widely deployed in evaluating the discriminatory power of virtual screens.
all the models had significant area under curve  obtained from roc plot of the three classifiers depicted in the figure  <dig>  random forest on the whole establishes to be the best classifier followed by nb and j <dig> producing a significant auc of 70% as compared to nb  and j <dig> .
evaluation of substructures
for identification of potentially enriched substructures in the bioactive molecules, the active dataset containing  <dig>  compounds were clustered using libmcs algorithm.
all the ~22 k compounds were clustered into  <dig>  scaffolds spread over  <dig> hierarchical levels.
only top level clusters were selected for further analysis.
there were a total of  <dig> clusters at level  <dig> which included  <dig> singletons.
as our aim was to identify potentially enriched substructures, all singletons were removed and only  <dig> scaffolds were taken up for further analysis.
the number of occurrences of each of the  <dig> scaffolds in the active and the inactive datasets was determined.
chi-square test and p-value were used to determine the significance of enrichment .
<dig> scaffolds had p-value less than  <dig>  and an enrichment factor >  <dig>  in order to assess the structural similarity of the scaffolds with the active molecules, the final  <dig> scaffolds were aligned against the active molecule dataset.
figure  <dig> represents an alignment generated with the top  <dig> compounds of the active set as determined from tanimoto similarity and overlap between query scaffold and active molecules.
CONCLUSIONS
malaria is a neglected tropical disease.
widespread drug-resistance to commonly used anti-malarials which has limited the therapeutic options available has warranted the need to search for novel molecules with anti-malarial activity.
the availability of high-throughput chemical screens in the public domain provides an excellent opportunity to create predictive computational models to prioritize molecules using a virtual screening approach.
such an approach therefore will, not only serve to aid the rapid screening of compounds but also subsequently enhance the identification of true hits and thereby would lead to reduced cost of carrying out biological screens.
our analysis shows that a systematically designed computational model for activity based on chemical descriptors could be potentially used for virtual-screening.
the work encompasses a machine learning based framework to build in silico predictive models based on datasets from high-throughput screens for apicoplast inhibitors of the malaria parasite.
comparative analysis of various classifiers revealed that random forest performed better than both naive bayes and j <dig>  the study was extended further to explore potentially enriched substructures in bioactive molecules, which resulted in the identification of  <dig> significantly enriched scaffolds.
predictive models in conjunction with the enriched scaffold information can be potentially used as a molecular filtering criterion for prioritizing molecules for biological screens for anti-malarial activity.

 malaria is a major health problem across the world, more so in the tropics and especially in developing nations. according to the recent world malaria report, released by the world health organization in there were about million cases of malaria across the globe and million deaths in 2010; with highest mortality found within children living in africa. malaria is a mosquito-borne disease and is caused by protozoan parasites belonging to the genus plasmodium. p. falciparum, p. vivax, p. ovale and p. malariae are the four species of the parasite which are routinely implicated as the causative agents in humans, with p. falciparum being the most commonly encountered and deadliest amongst them all and associated with 90% of the fatalities in africa. endemic to the tropical and subtropical regions of africa, asia, south and central america where hot and humid climatic conditions prevail, malaria has been indicated as a major constraint to economic development. one of the major roadblocks in the adequate control of malaria has been the limited therapeutic options available for its treatment. the current commonly used classes of drugs are limited to aminoquinolines and their derivatives such as arylamino alcohols, methanols, biguanides, diaminopyrimidines and antimalarial endoperoxidases. chloroquine and primaquine have been extensively used for the treatment and prophylaxis of malaria. however, widespread drug resistance to available therapeutic agents and the emergence of multi-drug resistant strains has resulted in limited treatment options. the current pipeline for drug discovery of anti-malarials is also limited, with just products in clinical trials and in preclinical stages of development. large scale collaborative initiatives have made it possible to assemble large datasets of chemical structure information online. this has been complemented by the annotation of biological activities of these molecules. many of the biological activities have been derived by high-throughput bioassays made possible by recent advances in automation of these assays. the availability of chemical structure and bio-activity information in standardized forms provide immense opportunities for creating predictive computational models to understand the correlation between chemical properties and their activities and also opens up the possibility to create predictive computational models for bio-activities. these predictive models make it possible to computationally screen large molecular datasets thereby offering a possibility to improve the hit-rate and thereby reduce the overall costs of drug discovery. we have also previously successfully generated such predictive models for anti-tubercular molecules and for small molecule modulators of mirna. in the present study, we applied the machine learning technique to create classification models from high-throughput screens of anti-malarial agents that inhibit the development of the apicoplast in the malaria parasite, p. falciparum. in addition, we used a maximum common substructure based approach to identify substructures enriched in the bioactive molecules. our result suggests that efficient and accurate computational predictive models could be built to screen large datasets in silico and could be potentially used to prioritize molecules for high-throughput screens.  descriptor generation and model construction initially, a total of 2d molecular descriptors were generated for the active and inactive datasets downloaded from pubchem. after data processing, as explained in methods section, the number of descriptors was reduced to, since not many descriptors were removed after data processing, we assumed the compounds to be structurally diverse. as the dataset used in the study was large, the heap-size in weka was increased to 4 gb to handle out-of-memory exception. the initial experiments were done using standard base classifiers; however, to reduce the rate of false negatives, cost sensitivity was introduced in classifiers using the meta-learners. misclassification cost was set for false negatives and was incremented so as to stay around the upper limit of false positives. as expected, introducing cost for each of the classifier resulted in an increase in the number of true positives and decrease in the number of false negatives thereby increasing the robustness of the model. the final misclassification cost used for each classifier is presented in table  the naive bayes classifier required the smallest misclassification cost setting and was also the fastest in building the model. *csc denotes costsensitiveclassifier,#balanced classification rate. model evaluation a number of models were trained using 5-fold cross validation on the training dataset using different misclassification cost settings for false negatives until cost optimized models were obtained. the best model for each classifier nb, rf and j was chosen based on their performance evaluated using different statistical measures. all statistical results reported in table  are based on independent test set and not on the training set. the overall efficiency of a classifier in generating the models was judged from the accuracy. the accuracy for all the models came out to be around 75%. sensitivity and specificity plots were used for identifying the best models for each dataset for evaluating the effectiveness of the classifier in correctly identifying positive and negative labelled instances. the specificity for all the models was approximately 80% and the sensitivity ranged from 40-50% with rf being the most sensitive classifier for the dataset and nb the least sensitive. since our dataset was highly imbalanced, accuracy alone cannot be used as a reliable statistical measure for assessment of the classifiers performance. in addition to this, other performance measures were employed to check the robustness of the model which included the bcr rate and roc curve analysis. the balanced accuracy values turned out to be satisfactory for all the models with best for random forest, being more accurate than naive bayes and j roc curve analysis has been widely accepted as one of the most reliable approach for quick performance assessment of virtual screening approaches therefore, it has been widely deployed in evaluating the discriminatory power of virtual screens. all the models had significant area under curve obtained from roc plot of the three classifiers depicted in the figure  random forest on the whole establishes to be the best classifier followed by nb and j producing a significant auc of 70% as compared to nb and j. evaluation of substructures for identification of potentially enriched substructures in the bioactive molecules, the active dataset containing compounds were clustered using libmcs algorithm. all the ~22 k compounds were clustered into scaffolds spread over hierarchical levels. only top level clusters were selected for further analysis. there were a total of clusters at level which included singletons. as our aim was to identify potentially enriched substructures, all singletons were removed and only scaffolds were taken up for further analysis. the number of occurrences of each of the scaffolds in the active and the inactive datasets was determined. chi-square test and p-value were used to determine the significance of enrichment. scaffolds had p-value less than and an enrichment factor >  in order to assess the structural similarity of the scaffolds with the active molecules, the final scaffolds were aligned against the active molecule dataset. figure  represents an alignment generated with the top compounds of the active set as determined from tanimoto similarity and overlap between query scaffold and active molecules.  malaria is a neglected tropical disease. widespread drug-resistance to commonly used anti-malarials which has limited the therapeutic options available has warranted the need to search for novel molecules with anti-malarial activity. the availability of high-throughput chemical screens in the public domain provides an excellent opportunity to create predictive computational models to prioritize molecules using a virtual screening approach. such an approach therefore will, not only serve to aid the rapid screening of compounds but also subsequently enhance the identification of true hits and thereby would lead to reduced cost of carrying out biological screens. our analysis shows that a systematically designed computational model for activity based on chemical descriptors could be potentially used for virtual-screening. the work encompasses a machine learning based framework to build in silico predictive models based on datasets from high-throughput screens for apicoplast inhibitors of the malaria parasite. comparative analysis of various classifiers revealed that random forest performed better than both naive bayes and j the study was extended further to explore potentially enriched substructures in bioactive molecules, which resulted in the identification of significantly enriched scaffolds. predictive models in conjunction with the enriched scaffold information can be potentially used as a molecular filtering criterion for prioritizing molecules for biological screens for anti-malarial activity. 
in the present study, we have used high-throughput screen datasets for the discovery of apicoplast inhibitors of the malarial pathogen as assayed from the delayed death response. we further evaluated the active molecules using a substructure based approach to identify common substructures enriched in the active set. the recent availability of high-throughput phenotypic screen datasets for antimalarial activity offers a possibility to create computational models for bioactivity based on chemical descriptors of molecules with potential to accelerate drug discovery for malaria. we found random forest based approach provides for better accuracy as assessed from roc curve analysis. the molecules were further evaluated for common substructures using a maximum common substructure based approach. we argue that the computational models generated could be effectively used to screen large molecular datasets to prioritize them for phenotypic screens, drastically reducing cost while improving the hit rate.
 malaria is a major healthcare problem worldwide resulting in an estimated million deaths every year. it is caused by the members of the parasite genus plasmodium. the current therapeutic options for malaria are limited to a few classes of molecules, and are fast shrinking due to the emergence of widespread resistance to drugs in the pathogen. the recent availability of high-throughput phenotypic screen datasets for antimalarial activity offers a possibility to create computational models for bioactivity based on chemical descriptors of molecules with potential to accelerate drug discovery for malaria.  in the present study, we have used high-throughput screen datasets for the discovery of apicoplast inhibitors of the malarial pathogen as assayed from the delayed death response. we employed machine learning approach and developed computational predictive models to predict the biological activity of new antimalarial compounds. the molecules were further evaluated for common substructures using a maximum common substructure based approach.  we created computational models using state-of-the-art machine learning algorithms. the models were evaluated based on multiple statistical criteria. we found random forest based approach provides for better accuracy as assessed from roc curve analysis. we further evaluated the active molecules using a substructure based approach to identify common substructures enriched in the active set. we argue that the computational models generated could be effectively used to screen large molecular datasets to prioritize them for phenotypic screens, drastically reducing cost while improving the hit rate. 