malaria is a life - threatening disease , 
 which is responsible for roughly 1 million deaths each year .
approximately 40% of the world 's population is exposed to the risk of malaria , particularly 
 those in tropical and subtropical countries .
malaria also poses a huge economic burden in countries where the 
 disease is endemic , cutting economic growth rates by as much as 1.3% 
 in countries with high disease rates .
previous 
 successes in attempting to eradicate the disease were only relatively 
 short - lived due to increasing resistance of the mosquito to insecticides and of the parasite to established drugs . in many parts of the world ,
emerging 
 resistance is responsible for a recent increase in malaria mortality , 
 particularly in countries that had previously eliminated its presence . 

the disease has worldwide implications due to the increase in air 
 travel , with travelers from malaria - free areas of the world especially 
 vulnerable ; therefore , the development 
 of new and more effective antimalarial chemotherapy has never been 
 more important .
the plasmodium falciparum parasite , 
 which is the most deadly form of the malaria parasite , has developed resistance to chloroquine in many parts of 
 the world .
there are strenuous and continued efforts to identify novel 
 small molecules that either circumvent chloroquine resistance or act 
 on alternative stages of the malaria parasite lifecycle .
one target that has received attention is the 
 mitochondrial respiratory chain of p. falciparum . 

atovaquone ( part of combination therapy malarone ) targets the cytochrome 
 bc1 complex ( complex iii ) in the mitochondrial electron 
 transport chain of p. falciparum .
the electron transport chain is an attractive chemotherapeutic 
 target in that it differs from the human host in that it lacks a canonical 
 protonmotive nadh : ubiquinone oxidoreductase ( complex i ) ; rather , it 
 has a single subunit , nonprotonmotive ndh2 .
using an escherichia coli nadh dehydrogenase knockout 
 strain ( ann0222 , ndh::tet nuob::npti - sacrb ) , we have developed a heterologous 
 expression system for pfndh2 facilitating its physiochemical and enzymological 
 characterization .
pfndh2 is a metabolic 
 choke point in the respiratory chain of the parasite 's mitochondria 
 and is the focus of the discovery program toward the development of 
 novel therapy for uncomplicated malaria .
we have previously described 
 a miniaturized spectrophotometric assay for recombinant pfndh2 ( steady 
 state nadh oxidation and ubiquinone reduction monitored at 340 and 
 283 nm , respectively ) with robust assay performance measures . this assay forms the basis of the high - throughput 
 screen ( hts ) sequential screening program .
the objective of 
 this program was to identify novel chemotypes that act as selective 
 inhibitors of pfndh2 . upon commencement of the program , there was 
 only one molecule that was known to exhibit pfndh2 activity , 1-hydroxy-2-dodecyl-4-(1h)quinolone ( hdq ) ( figure 1 ) , which 
 has an ic50 value of 70 nm .
our discovery program used virtual screening to select 
 a 16050 compound subset of the 750000 compound biofocus library 
 for a high - throughput in vitro screening campaign .
the authors describe 
 here the use of chemoinformatics , virtual screening , and computational 
 methods to identify the 
best  subset of compounds from 
 the biofocus database and the screening of these compounds using the 
 pfndh2 hts assay to identify novel chemical hit series , with diverse 
 chemotypes , for subsequent medicinal chemistry development .
structure of 
 hdq and the identity of the proposed key moiety in the structure .
virtual screening emerged in the 1990s as a way 
 of predicting bioactive compounds using computational methods .
there are many examples of successful use of 
 such approaches in the literature for both hit finding and hit - to - lead 
 optimization stages of the drug discovery process .
the methods of virtual screening are usually 
 defined as either structure - based or ligand - based .
structure - based 
 approaches use knowledge of the 3d structure of the biological target , 
 whereas ligand - based approaches rely on the knowledge of the structure 
 of compounds exhibiting the desired activity .
this current work adopts a ligand - based approach as there is no 
 crystal structure of the pfndh2 target and it displays poor homology 
 with any published structure in the pdb .
ligand - based virtual screening approaches rely on the complementary 
 area of chemoinformatics , which has been defined as the  the 
 application of informatics methods to solve chemical problems  .
there have been an increasing number of publications 
 that have made use of chemoinformatics in recent years , partly driven by the increasing pressures on 
 the pharmaceutical industry to increase productivity , while decreasing 
 costs .
using computational approaches to expedite the identification 
 of candidate molecules that are predicted to possess the desired properties 
 is an efficient approach to discovery . with the incorporation of computational 
 filters for properties synonymous with unfavorable pk / pd profiles , 

the aim of chemoinformatics is to reduce compound attrition rates at 
 all stages in the discovery process . in this work ,
our definition 
 of the  best  set of compounds is a multidimensional 
 challenge .
set of compounds is whether a compound is predicted by virtual screening 
 and chemoinformatic methods to possess the desired activity against 
 pfndh2 in the hts assay .
the second dimension of this quality metric 
 is that the compounds assayed possess desirable lead / druglike characteristics . 
 over the past few years
, there have been many publications concerning 
 the identification of molecular properties of compounds that are common 
 among drugs and drug candidates .
this 
 has led to many rules of thumb to guide discovery of molecules that 
 are likely to possess the appropriate absorption , distribution , metabolism , 
 excretion , and toxicity ( admet ) characteristics and be amenable for 
 further development along the drug discovery pipeline .
the third dimension of the metric 
 for  best  set of compounds is that of optimal sampling 
 of chemical space . in the project
, we had the resources to assay approximately 
 17000 compounds in total ; therefore , we had to explore chemical space 
 in the most efficient way possible to yield the most information - rich 
 set of results that would be the most informative for both the sequential 
 screening stage and the final compound selection phase . in summary ,
best  set of 
 compounds are those that are predicted to be active against the target , 
 possess desirable drug / lead like characteristics , and sample chemical 
 space the most effectively . in this paper , we described the 
 use of chemoinformatics and virtual screening methods to select an 
 optimal set of compounds for hts screening leading to the identification 
 of new chemotypes that display activity against the pfndh2 malarial 
 target .
overall , our approach consisted of four fundamental stages : 
 ( i ) transfer and optimization of assay from in - house to biofocus , 
 ( ii ) substructure search of quinolin-4-one in biofocus library followed 
 by preliminary hts screening , ( iii ) chemoinformatic compound selection 
 from biofocus library based on results of preliminary screening , and 
 ( iv ) hts screening of selected compounds followed by hit confirmation .
initial substructure searching 
 of the biofocus library for compounds , which contained the  key 
 moiety  , the core of hdq ( figure 1 ) , 
 revealed 1175 compounds .
these compounds underwent screening using 
 the spectrophotometric assay previously developed and validated by 
 us , which had subsequently been transferred to biofocus .
duplicate five point dose  response curves 
 ranging from 20 m to 78 nm revealed the presence of 54 new 
 active compounds ( ic50 < 20 m ) that had confirmed 
 purity by lcms of > 70% .
the identity of these active compounds 
 and hdq along with the identity of the inactive compounds provided 
 the basis of the virtual screening and compound selection .
as the primary 
 goal of this work was to identify novel chemotypes , we used chemoinformatic 
 methods to achieve a  scaffold hop  , that is , identification 
 of structurally diverse compounds that exhibit the desired biological 
 activity .
we applied seven different 
 virtual screening methods in parallel to identify compounds from the 
 biofocus library .
indeed , 2d fingerprint searching 
 has been shown to outperform 3d methods in some situations .
recently , it has been reported that fingerprints 
 can be used successfully to achieve a  scaffold hop  .
the use of fingerprints is particularly attractive 
 for large scale screening as the calculations are quick ( as compared 
 with other methods such as protein : ligand docking ) , and 3d structures 
 of the molecules do not need to be generated or stored .
we applied 
 a range of different fingerprinting methods to navigate chemical space 
 in a variety of different ways .
we employed mdl molecular access system 
 ( maccs ) keys , ecfp2 , and fcfp2 to identify compounds similar to any 
 of the 55 actives ( 54 from substructure screening plus hdq ) previously 
 identified .
the mdl keys are a set of predefined substructural fragments 
 that have been used in similarity searching due their speed of calculation 
 and comparison and broad experience in application .
the publicly available 166 bit key set has found a variety 
 of uses in the drug discovery workflow . extended connectivity fingerprints ( ecfps )
have been shown to have 
 a number of strengths that make them useful for similarity searching . 

ecfps are a fingerprint methodology explicitly designed to capture 
 molecular features relevant to molecular activity
. they can be quickly 
 calculated , as they are not defined a priori ( in contrast with mdl 
 maccs keys ) and they can represent novel structural classes .
functional class fingerprints ( fcfps ) are a 
 related fingerprint to ecfps but instead of using a specific atom 
 identifier for the initial atom in the algorithm to generate the fingerprint , 
 fcfps use a more abstract pharmacophoric set of initial atom identifiers 
 based on properties such as hydrogen bond acceptor and donor , negatively 
 and positively ionizable , aromatic , and halogen .
a study of a wide variety of similarity coefficients 
 ( 22 in total ) that may be employed in similarity searching supports 
 the use of the popular tanimoto coefficient as compared with the alternatives .
there is little literature precedent for the 
 cutoff value that should be used for a given fingerprint and similarity 
 coefficient to identify a compound displaying the desired activity . 

there is , however , a study that used the daylight fingerprint methodology 
 and taminoto coefficient and suggested that a coefficient of greater 
 than or equal to 0.85 would result in a 30% chance of the compound 
 exhibiting similar biological activity to the query molecule .
however , the fingerprints employed in the current 
 study are different to the daylight methodology in the aforementioned 
 study so these analyses may not apply .
consequently , we have chosen 
 similarity thresholds to seek to obtain a  balance  
 between the numbers of compounds
using a threshold of 0.8 for the mdl maccs keys found 8784 
 compounds , whereas for ecfp2 , a tanimoto threshold of 0.6 
 gave 333 compounds , and fcfp2 with a tanimoto coefficient 0.75 
 highlighted 435 compounds .
interestingly , these threshold values match 
 up extremely well with a paper ( published after this work was performed ) , 
 which indicates that the probability assignment of a particular similarity 
 measure of finding 50% of all possible actives using a given measure 
 for maccs keys should use a value of 0.82 , whereas 0.52 should be 
 used for ecfp2 and 0.75 for fcfp2values very similar to the 
 ones we had selected .
thus , the combination 
 of these three fingerprint approaches would hopefully maximize the 
 chances of identifying compounds that were sufficiently similar to 
 one of the query compounds to display inhibition of pfndh2 .
the fourth chemoinformatic method that was used to identify compounds 
 with the desired biological profile was turbo similarity searching .
turbo similarity searching has been developed 
 to increase the effectiveness of virtual screening when there is little 
 information available .
the approach uses the multiple databases searches 
 using the nearest neighbors resulting from an initial similarity search .
the results from these searches are then combined 
 together using group fusion and the molecules ranked by the fusion 
 score .
this approach has been shown in simulated virtual screening 
 to show impressive performance in retrieving active molecules . for 
 our work
, turbo similarity searching was performed using the 55 active 
 compounds as initial query molecules and identifying the 50 nearest 
 neighbors to each compound using ecfp4 fingerprints and the tanimoto 
 coefficient .
these molecules were then used as the queries searching 
 the database utilizing the ecfp4 fingerprints .
the top 250 compounds 
 from each nearest neighbor search were summed using the tanimoto coefficient 
 and identified 4891 unique compounds .
the concept for these searches 
 was to identify bioisosteres of the key moiety headgroup of hdq [ 4-(1h)quinolone ] ( figure 1 ) , thus employing 
 the principle of isosterism , in that similar molecules usually possess 
 similar properties .
thirty - eight scaffold 
 isosteres of the 4-(1h)quinolone headgroup ( hdq ) 
 were identified using a variety of 2d topological pharmacophores .
the pharmacophores used were based on the cahart 
 strategy where a feature refers to two chemical groups separated 
 by a certain 2d path length . atom - based , fragment - based , and pharmacophore - based 
 binary descriptors were investigated using the tanimoto coefficient . 

the atom - based approach is likely to retrieve very close analogues 
 to the query compound , while fragment and pharmacophore searches were 
 more likely to find more diverse analogues .
continuous descriptors 
 were also employed to identify isosteres of the 4-(1h)quinolone headgroup using a modified burden number using the euclidean distance .
both binary and continuous 
 approaches were used to search a range of databases ( e.g. , acl and 
 nci diversity database ) using 4-(1h)quinolone as the query .
compounds identified by these 
 methods were examined by eye by medicinal chemists before inclusion 
 in the list of scaffold isosteres .
the thirty - eight substructures 
 were used to search the biofocus database , and 137693 compounds were 
 found .
this number of compounds was too large for this current 
 work , and a simple selection procedure was employed . for those isosteres 
 that retrieved less than 1000 compounds ,
all were kept , but for searches 
 that highlighted more than 1000 compounds , a maximally diverse selection 
 procedure was used to sample the compounds identified , while retaining 
 the maximum coverage of chemical space possible . to achieve this
, 
 we used fcfp4 fingerprints together with diversity selection to achieve 
 a maximum similarity selection .
bayesian methods rely on the estimation of probability 
 distributions of numerical representations of compounds based on molecular 
 properties of fingerprints .
each descriptor 
 value of a molecule is considered in turn , and the probability of 
 activity is considered to be proportional to the ratio of actives 
 to inactives that share that descriptor value . the overall probability 
 of activity is the product of all of the probabilities .
this nave 
 approach assumes statistical independence of descriptors ; however , 
 theoretical results suggest that large deviations from independence 
 can be tolerated .
a bayesian model was 
 built using the 1175 molecules screened initially ( 55  actives  
 the rest  inactive  ) using a variety of physicochemical 
 properties ( alogp , mw , number of hydrogen bond donors , number hydrogen 
 bond acceptors , number of rotable bonds , polar surface area ) and fingerprints 
 ( ecfp2 ) as the molecular descriptors .
the descriptors were binned , 
 and low - information content bins were removed due to a poor normalized 
 estimate .
a leave - one - out cross - validation process was employed to 
 build the model in which each sample was left out one at a time and 
 a model built using the results of the samples and that model used 
 to predict the left - out sample .
the area under the receiver operator 
 curve for the cross - validation set was 0.881 .
thus , given an active 
 molecule and an inactive molecule and one used the model to guess 
 which one was the hit , one would be right 88% of the time . using this 

split a contingency table revealed the number of true positives , true negatives , false positives , and 
 false negatives .
the figure of accuracy of 0.924 indicates that the 
 model generated was very good .
the performance of this model using 
 the whole data set was assessed through examination of its enrichment 
 performance . in the first 25% of the molecules tested , over 80% of 
 the active molecules were retrieved .
application of the bayesian model 
 to the biofocus collection identified 11702 compounds that were predicted 
 to be active .
principle component analysis ( pca ) was used as 
 the seventh method for compound selection .
pca is a simple , nonparametric 
 method of extracting relevant information from complex data sets that 
 are often confusing , clouded , or even redundant .
pca transforms a 
 number of possibly interdependent or correlated variables into a smaller 
 number of significant , independent , and uncorrelated orthogonal components . a principal component model of the initial 55 
 active compounds
was constructed using 20 physicochemical descriptors 
 ( see the experimental section details for 
 a full list ) .
the model constructed explained 88.5% of the overall 
 variance of the active compounds in three principal components .
the 
 euclidean distance was used to identify from the biofocus library 
 the 5000 closest compounds in the three - dimensional principle component 
 space to any of the active molecules .
thus , we were identifying the 
 nearest neighbors to the active compounds in pca space .
the 
 results of the seven virtual screening methods were combined and identified 
 34356 unique compounds that were predicted by one or more methods 
 to be similar in some way to one or more of the initial set of 55 
 active compounds .
this number of compounds was not able to be screened 
 in the in vitro assay ; hence , we undertook scoring and diversity 
 sampling protocols to select the best subset of 16000 compounds that 
 achieved the optimal coverage of chemical space while biasing the 
 selection such that compounds that had been selected by more than 
 one chemoinformatics approach were more likely to be included in the 
 final 16000 molecular set .
the concept that we employed was 
 that of consensus scoring , which can lead to tremendous improvements 
 in virtual screening through the improved quality of the results obtained . 

ligand - based virtual screening consensus scoring can show improved 
 performance over a single scoring protocol due to the fact that the mean of repeated samplings is closer to 
 the true value than one single measurement .
also , different methods 
 agree more on the ranking of actives than inactives , which arises 
 from the fact that different ligand - based virtual screening protocols 
 focus on different aspects of the ligand binding process and thus 
 lead to different false positives . also , it has been suggested that 
 actives are clustered more tightly than inactives ; thus , multiple 
 samplings will recover more actives than inactives .
a scoring function 
 was applied to each compound accounting for how often any of the seven 
 virtual screening methods had identified a compound and also took 
 into account key druglike properties ( vide infra ) .
the druglike 
 properties that were calculated for each compound were solubility , 
 octanol / water partition coefficient , and molecular weight .
scoring 
 functions were applied for these molecular characteristics displayed 
 in table 1 and figure 2 .
, the lower bound was chosen due 
 to the fact that during hit to lead development and onward through 
 the preclinical discovery pipeline , there is , in general , an increase 
 in molecular weight of a potential candidate .
the second influence 
 on our choice of these boundaries was the lipinski guidelines for 
 passive absorption of drugs .
solubility 
 is a key factor in any drug discovery program , and as such , compound 
 predicted solubility was assessed .
these 
 values were selected as previous work has suggested that for a compound 
 showing high permeability and a potency of 0.1 mg / kg , the aqueous 
 solubility needs to be 1 g / ml to be completely absorbed .
for example , for a compound with a molecular 
 weight of 400 , 5 g / ml corresponds to a log s value of 5.6 . the octanol / water partition coefficient is 
 one of the key molecular characteristics for any compound as it plays 
 a key determinant in preclinical admet and the increasing body of 
 evidence that suggests that molecules with optimal lipophilicity might 
 have increased chances of success in development .
for example , it has been shown that the promiscuity of 
 a given compound increases dramatically if log p is 
 greater than 3 , and other work has suggested 
 that compounds with a log p value of less than 4 
 ( and molecular weight less than 400 ) have a greatly increased chance 
 of success against a comprehensive set of admet tests . taking these into account , a compound scoring function 
 was derived as displayed in figure 2 and table 1 .
thus , each compound was assigned a score according 
 to its druglikeness considering its solubility , lipophilicity , and 
 aqueous solubility .
each compound was scored using the seven 
 virtual screening methods described above using range - scaled scores . 

the compounds selected by the turbo 
 similarity search were scored using the tanimoto coefficient of the 
 nearest neighbor identified in the turbo search .
molecules predicted 
 to be active via the bayesian model ( bayesian score cutoff > 5 ) 
 were scaled between 0 and 1 .
the pca distances of the 5000 compounds 
 selected were scaled between 0.5 and 1 with the closest compound scoring 
 1 and most distant 0.5 .
these range - scaled virtual screening scores 
 were combined with the physicochemical molecular properties to give 
 a final  virtual screening score  as defined in eq 1 . as we sought to favor selection of compounds that 
 were predicted to be active according to our virtual screening models , 

the score 
 for molecular weight was multiplied by two to enhance the likelihood 
 of selecting smaller compounds , while the solubility and lipophilicity 
 function were applied unchanged . upon collation of all of the data , 

there were in total 34356 unique compounds selected , each of which 
 had a virtual screening score calculated .
the virtual screening score 
 is our numerical assessment of how  good  a molecule 
 is in terms of likelihood of possessing the desired profile in terms 
 of activity and admet properties .
the form of the virtual screening 
 score assigned to each compound is as follows:1these 34356 compounds were then subjected 
 to a series of filters with hard cut - offs to eliminate compounds that 
 did not possess desirable druglike properties .
compounds were discarded if they failed 
 more than one of the following properties : molecular weight > 600 , 
 alog p > 6 , number of hydrogen bond donors > 
 6 , number of hydrogen bond acceptors > 11 , number of rotable bonds 
 > 14 , and polar surface area > 150  .
the reason 
 that we slightly relaxed the published guidelines was that the original 
 publications were based on properties of the majority of compounds 
 that passed certain criteria .
thus , by relaxing these guidelines slightly , 
 we hope to maximize our chances of finding novel chemotypes so we 
 did not want to preclude molecules 
too early  . when 
 these cut - offs were applied , there were 32727 compounds that were 
 taken forward .
to select 16000 compounds for hts , a procedure 
 was employed to choose the most diverse 16000 compound set from the 
 32727 compounds .
this approach was chosen as we desired to identify 
 new scaffolds active against pfndh2 ; thus , selecting the most diverse 
 subset was considered advantageous over selecting the top 16000 highest 
 scoring compounds . to achieve the diversity selection ,
bcut descriptors 
 were used , as these have previously been used in diversity selection .
all but two compounds were successfully calculated ; 
 these two compounds contained a positively charged sulfur atom and 
 a positively charged phosphorus , both types of atoms unlikely to produce 
 a suitable druglike compound .
histograms of the final virtual 
 screening score were examined for the set of 32727 compounds and the 
 16000 compound set .
it is noteworthy that upon compound selection 
 the distribution of virtual screening scores altered to favor compounds 
 with higher virtual screening scores ( figure 3 ) .
thus , any additional biasing of high - scoring compounds was not 
 considered necessary .
compounds in the lower scoring bins were included , 
 as recent work has highlighted the possibility of achieving a successful 
 scaffold hop may come from compounds that have low similarity scores 
 as assessed using molecular fingerprints .
thus , low - scoring compounds overall may well still be able to achieve 
 a scaffold hop .
comparison of the virtual screening scores : ( left ) 32727 
 compounds and ( right ) 16050 compounds .
the effect on this compound selection upon the 
 coverage of chemical space available in the biofocus library was assessed . 

the diversity of compounds selected was examined using fingerprints 
 with the tanimoto coefficient to compare the coverage of the chemical 
 space within the entire biofocus library .
the whole biofocus library 
 of 750000 compounds displayed 37881 clusters ( ranging from 
 having 1 member to 2927 members ) , whereas the 16000 compounds selected 
 gave 5913 clusters ( having between 1 and 164 members ) .
thus , 15.6% 
 of the overall diversity within the library was covered by 2.1% of 
 the compounds .
three potential false negatives were identified 
 from the initial screen from our previous work .
a similarity search 
 using ecfp2 was performed against the biofocus library , and the 50 
 most similar compounds were added to 16000 compounds to be screened . 

compounds were screened at a concentration 
 of 20 m generating one data point per compound . the mean and 
 median z factors on day one were 0.77 and 0.76 , respectively , and on day two were 
 0.81 and 0.81 .
a total of 395 compounds ( 2.5% of the total screened ) 
 showed > 20% inhibition in the primary screen .
to best resolve compounds 
 with weak activity from the noise , a runwise multiplicative correction 
 factor available within genedata screener software ( assay analyzer 
 module , genedata inc . ,
this correction factor is created by a sophisticated 
 pattern detection algorithm that detects subtle recurring patterns 
 within the data set , and when applied , the median of the distribution 
 of activity is brought in line with zero % inhibition , thereby tightening 
 the data and potentially rescuing false negatives . using the corrected 
 data , 333 compounds showed > 20% inhibition . of these 333 compounds , 

24 had not been identified using noncorrected data . to maximize the 
 chances of identifying weakly active compounds , any compounds that 
 showed > 20% inhibition using either the corrected or the noncorrected 
 data were progressed to retesting .
this resulted in the progression 
 of 419 compounds ( 395 + 24 ) to retest analysis .
a total of 469 compounds 
 were tested in triplicate for the ability to inhibit the pfndh2-catalyzed 
 reaction .
a total of 419 of these were identified from the primary 
 screen , and a further 50 were included as structurally related to 
 three potential false negatives in the 1175 screen . prior to initiating 
 this phase of the hts , the pharmacology of hdq
was reconfirmed to 
 ensure comparable performance between the primary and the hit confirmation 
 screens .
these compounds were progressed 
 to potency analysis to ensure capture of all compounds that showed 
 activity in either the primary or the retest stages .
the hit calling 
 criteria were set such that if any one of the three percentage inhibition 
 measurements for a given compound was > 30% , the compound was progressed 
 for potency testing .
in addition , compounds 
 that showed > 50% inhibition in the primary screen but whose maximum 
 triplicate retest values was < 30% inhibition were included , capturing 
 a further 36 compounds .
a further six compounds were selected that 
 showed a maximum retest value of 2530% and whose screening 
 concentration was < 20 m .
this hit - calling approach was designed 
 to ensure that no compounds of interest or weak hits were missed , 
 and 150 compounds met these criteria .
the ic50 was 
 determined for these 150 compounds with each compound screened at 
 10 concentrations ( with 3-fold dilution steps between points ) as replicated 
 duplicates ; that is , each compound was replicated on a plate , and 
 each plate was screened twice . as for previous phases of the hts , 
 the z factor and signal to background obtained 

were within the accepted range , and the correlation between the two 
 runs was strong with minimal numbers of outliers .
the potency screen 
 produced 32 compounds with an ic50 < 10 m . the 
 most potent compound showed an ic50 of 292 nm .
the chemoinformatics methods selected 16050 
 compounds from a library of over 750000 that were subjected to a screening 
 cascade involving primary , retest , and potency screens .
the screen 
 resulted in the identification of 32 compounds with an ic50 < 10 m .
interestingly , analysis of the chemoinformatics 
 approaches that selected the hits identified revealed that only 2 
 of the 32 compounds were selected by more than one virtual screening 
 approach ( table 2 ) , justifying our use of a 
 range of virtual screening approaches .
this result indicates that 
 different screening methods probe different areas of chemical similarity 
 space .
the two compounds that were identified are displayed in figure 4 together with the methods that identified them .
two compounds identified by more than one chemoinformatic method . to examine which selection method out of bioisosteres , 
 bayesian and turbo , gave the most molecular diversity ,
the diversity 
 of the compounds as selected was assessed using ecfp4 fingerprints 
 and the tanimoto coefficient ( table 3 ) . as 
 can be seen
, the bioisosteric method gave the highest average diversity 
 with turbo only slightly behind , with bayesian compound selection 
 displaying the least diversity .
in addition to these 32 compounds identified by the 
 screening cascade , 16 compounds from the initial screening displayed 
 ic50 < 10 m against the pfndh2 enzyme ; thus , 
 in total , 48 hit compounds have been identified .
inspection of the 
 48 hits identified revealed several chemotypes indicating that using 
 many complementary virtual screening methods is a favorable choice 
 when embarking on a hit discovery campaign .
example compounds from 
 some of the chemotypes are displayed in table 4 together with the pfndh2 and 3d7 antimalarial activities .
our approach did provide hits that would not have 
 been identified from simple 2d similarity approaches . to exemplify 
 this , compounds 3 , 4 , and 5 in table 4
would not have been identified 
 if we had only relied on substructure searches based on bioisosteric 
 replacement of the core of hdq .
thus , we believe that our approach 
 of using many chemoinformatics methods is justified .
this stance is 
 further strengthened through a very recent retrospective study in 
 which a multipronged virtual screening approach very similar to the 
 one employed here was one of the best performing  consensus  
 approaches .
the activity range 
 of the example five chemotype compounds against the enzyme is in the 
 tens to hundreds nanomolar .
their whole cell 3d7 growth inhibition 
 activity is in the micromolar range presumably due to the large number 
 of membranes that the compound has to cross to reach its site of action 
 in the mitochondria .
crucially , however , the ligand efficiency ( pic50/number of non hydrogen atoms ) of these classes of compounds 
 compares favorably with current therapies ( e.g. , chloroquine has a 
 value 0.33 and atovaquone 0.30 ) , and our compounds 
 are yet to undergo optimization .
after this work was performed , 
 the results from large scale screening campaigns were released from 
 gsk , st .
judes , and novartis . comparing our hits with 
 those from those published , there are no exact duplicate compounds , 
 and using fcfp4 fingerprints , there are no compounds with a tanimoto 
 value of > 0.9 .
thus , our suite of chemoinformatics methods has 
 enabled the discovery of novel antimalarial chemotypes as compared 
 with the mass screening campaigns , and the results display extremely 
 promising levels of inhibition for both the target enzyme and the 
 whole cell inhibition .
as such , these molecules are very attractive 
 scaffolds on which to base a medicinal chemistry lead development 
 program . consequently
we have employed a wide range of ligand - based 
 chemoinformatics methods in the rational selection of 16050 compounds 
 that were predicted to possess activity against pfndh2 and possess 
 favorable admet properties .
these compounds were subjected to high - throughput 
 screening triage against the p. falciparum ndh2 enzyme 
 target . hit confirmation and potency determination revealed 48 compounds 
 with ic50 values ranging from 100 nm to 10 m .
analysis 
 of these hits revealed several novel distinct chemotypes primed for 
 development as new agents against malaria . to our knowledge , hitherto , 
 hdq and the phenothiazines are the only selective compounds known 
 to inhibit type ii nadh : ubiquinone oxidoreductase homologues from 
 any organism .
the hits discovered in this hts therefore represent 
 a significant advancement and should enable chemical biology research 
 into these enzymes from a number of important organisms including plasmodium , mycobacterium , trypanosomes , and yeast .
all reagents were prepared in 
 glassware prerinsed with deionized water followed by ethanol ( etoh ) 
 and finally deionized water unless otherwise stated . with the exception 
 of assay buffer and 1 m kcn ,
the 
 assay buffer was prepared on the day of the assay and was comprised 
 of 20 mm hepes ( free acid , sigma h3375 ) in h2o , ph 7.4 . 

one molar kcn was prepared in a fume hood using buffer and 10 m hcl 
 to adjust the ph to 7.5 .
ten millimolar coq ( sigma c7956 ) was prepared 
 by dissolving 10 mg in 4 ml of etoh .
this was then further diluted 
 10-fold in 25% dmso to give 1 mm working solution .
one millimolar 
 hdq was prepared fresh every 23 days by diluting a fresh weighing 
 into methanol ( meoh ) ; this was then further diluted 20-fold in buffer 
 to obtain the 50 m used in the assay ( 5 m in 0.5% meoh 
 final assay concentration ) .
a 0.1 m concentration of nadh ( sigma n8129 ) 
 was prepared in buffer contained in a light protective eppendorf tube ; 
 the reagent was not used more than 3 h after preparation . a perkin - elmer 
 envision with a 340/25 nm emission filter
assay plates were black sided and clear bottomed , and the final well 
 volume was 100 l . because absorbance is dependent on the path 
 length ,
the reaction was initiated with a minimal volume of coq ( 2 
 l ) , and a 2% change in volume itself was deemed to have negligible 
 effects on the absorbance and was consistent for all wells .
control 
 wells that received no coq received 2 l of diluent to maintain 
 the path length and concentrations .
the expected absorbance drop corresponding 
 to 100% conversion of coenzyme q from the quinone to quinol form is 
 0.15 abs units .
prior to the reaction , 11.36 mm kcn was prepared 
 in assay buffer . for each 384-well plate to be screened , 83 l 
 of 0.1 m nadh was added to 37.6 ml of 11.36 mm kcn / buffer followed 
 by 108 l of 1:1 membrane ( 8 mg / ml ) .
the glass container was 
 then swirled to ensure thorough mixing of the assay mixture , and 88 
 l was added to each well containing 10 l of compound 
 using a matrix multichannel pipet .
a  pre - read  340 
 nm absorbance value was then obtained prior to the addition of 2 l 
 of 1 mm coq using a velocity11 bravo to initiate the reaction .
the 
 reaction was then mixed ( 20 l  3 cycles fast speed ) , 
 and a 
post - read  340 nm absorbance value was obtained 
 1 min after reaction initiation .
the final assay concentrations were 
 therefore 200 m nadh , 10 mm kcn , 1 g / well membrane , 
 20 m coq , and 5 m hdq .
the assay mixture was only stable 
 for a few minutes ( nadh oxidizes when in contact with the membrane ) 
 and was therefore prepared fresh for each plate where a 1 min reaction 
 time was used or to support a batch of three plates where 3 min of 
 reaction time was used . because the reaction was not stopped , it was 
 important to keep the timings very tightly controlled
a fresh weighing of lyophilized hdq was 
 dissolved in meoh to a concentration of 1 mm .
this stock was stored 
 at 20 c and used for a maximum of 3 days . following 
 data collection
, the results were analyzed , and ic50 was 
 calculated in graphpad prism ( graphpad software , inc .
preliminary assaying 
 of the 1175 set of compounds was performed in replicate five - point 
 concentration curves with a 4-fold dilution step in 100% dmso .
the 
 1 l stock concentration curve was diluted 25-fold by the addition 
 of 24 l of buffer to give a top concentration of 200 m 
 in 4% dmso , that is , 20 m final in the assay .
plates were formatted 
 such that test compounds were present in columns 322 ; columns 
 1 , 2 , 23 , and 24 contained 4% dmso with the exception of wells m - p1 
 and a - d24 , which contained 50 m hdq as a positive control for tracking 
 purposes .
columns 1 ( a  l ) and 24 ( e  p ) contained the 
 no - coq positive control used for data calculations ; instead of 2 l 
 of 1 mm coq , these wells received 2 l of the carrier ( 22.5% 
 dmso/10% etoh ) .
the final well contents for phase 2 screen were 20 
 m top concentration of compound , 20 m coq ( or diluent 
 for the 100% inhibition controls ) , 0.85% dmso , and 0.2% etoh .
assaying of the 16000 compounds was performed at 20 m for 
 both the primary and the hit confirmation screens .
plates were formatted 
 such that test compounds were present in columns 122 , and 
 columns 23 and 24 contained 4% dmso with the exception of wells a - d24 , 
 which contained 50 m hdq as a positive control for tracking 
 purposes .
column 24 ( e - p ) contained the positive control used for 
 data calculations ; these wells received 2 l of the carrier 
 ( 22.5% dmso/10% etoh ) instead of 2 l of 1 mm coq .
the final 
 well contents for phase 4 screen were approximately 20 m compound , 
 20 m coq ( or diluent for the 100% inhibition controls ) , 0.85% 
 dmso , and 0.2% etoh .
plates were formatted such 
 that test compounds were present in columns 322 , and columns 
 1 , 2 , 23 , and 24 contained 4% dmso with the exception of wells m - p1 
 and a - d24 , which contained 50 m hdq as a positive control for 
 quality control purposes .
response 
 curve of hdq , which had a final assay top concentration of 5 m 
 in 0.5% meoh ; the curve was contained in rows o1322 and p1322 . 

columns 1 ( a  l ) and 24 ( e  p ) contained the positive 
 control used for data calculations , and these wells received 2 l 
 of carrier ( 22.5% dmso/10% etoh ) instead of 2 l of 1 mm coq . 
 the final well contents for potency determination were 40 m 
 top concentration of compound , 20 m coq ( or diluent for 100% 
 inhibition controls ) , 1.25% dmso , and 0.2% etoh .
data were recorded 
 as absorbance units prior to initiation of the reaction and after 
 reaction completion .
the pre- and postreaction absorbance values were 
 imported into genedata screener assay analyzer ( genedata inc . ,
switzerland ) , 
 and having defined the control populations , the % inhibition was calculated 
 for each well .
control wells used in the calculations did not receive 
 compound ( but were diluent controlled ) , and % inhibition was calculated 
 using wells that had not received coq as 100% inhibition .
five micromolar 
 hdq wells were monitored to ensure that a high % inhibition was being 
 achieved .
the option of correcting the data for plate effects using 
 assay analyzer 's runwise multiplicative correction factor ( genedata 
 inc . ) was investigated , and this data set was taken into account for 
 screen hit calling .
response data were processed 
 through genedata screener as described , then calculated , and plotted 
 using xlfit 4.2.1 .
curve fitting was carried out using a 4-point parameter logistic equation , 
 model 204 .
all of the methods below were 
 performed in parallel on the biofocus compound collection .
the biofocus compound collection 
 of 750000 compounds was searched for compounds that contained 
 the  key moiety  ( figure 1 ) of 
 hdq that we chose as 1h - quinolin-4-one using pipeline 
 pilot .
three types of molecular fingerprints 
 were calculated using pipeline pilot : mdl maccs keys , fcfp2 , and ecfp2 .
the similarity between the 55 active compounds 
 and the biofocus library compounds was assessed using the tanimoto 
 coefficient .
using the 55 active compounds , ecfp4 were used as descriptors , and for each search , 
 250 of the highest ranking compounds were kept using the biofocus 
 database .
the descriptors calculated were 
 alog p , molecular_weight , num_atoms , num_bonds , num_hydrogens , 
 num_positiveatoms , num_negativeatoms , num_ringbonds , num_rotatablebonds , 
 num_aromaticbonds , num_bridgebonds , num_rings , num_aromaticrings , 
 num_ringassemblies , num_chains , num_chainassemblies , num_fragments , 
 num_h_acceptors , num_h_donors , and molecular_solubility .
the 5000 
 closest compounds to any of the hits as assessed by euclidean distance 
 in pca were selected from the biofocus database using pipeline pilot .
the 
 descriptors calculated were as follows : alog p , molecular_weight , 
 num_h_donors , num_h_acceptors , num_rotatablebonds , ecfp_2 , and molecular_polarsurfacearea . 

the model was developed using leave - one - out cross - validation and applied 
 to the biofocus database with compounds with a score of 5 or above 
 being selected .
bioisosteres were identified using powermv using a variety of descriptors , similarity measures , 
 and databases : atom pair / tanimoto , atom pair ( cahart)/tanimoto , fragment 
 pair / tanimoto , pharmacophore fingerprints / tanimoto , and weighted burden 
 number / euclidean methods were used against the acl , gene logic , and 
 nci databases within powermv .
the bioisosteres identified were used 
 as substructure searches to identify compounds in the biofocus database 
 using pipeline pilot .
the scoring functions were implemented in pipeline 
 pilot using the built in algorithms for molecular solubility and log p. bcut descriptors were calculated using the cdk descriptor calculator .
a total of 16000 diverse compounds were selected 
 using a protocol in pipeline pilot .
ecfp4 fingerprints , using a similarity 
 cut off of 0.7 with the tanimoto coefficient , were used together with 
 clustering to compare the coverage of the chemical space of the 16000 
 selected compounds within in the entire biofocus library using protocols 
 in pipeline pilot .
ic50 values ( 50% inhibitory concentrations ) were calculated by 
 using the four - parameter logistic method ( grafit program ; erithacus 
 software , united kingdom ) .
all of the methods below were 
 performed in parallel on the biofocus compound collection .
the biofocus compound collection 
 of 750000 compounds was searched for compounds that contained 
 the  key moiety  ( figure 1 ) of 
 hdq that we chose as 1h - quinolin-4-one using pipeline 
 pilot .
three types of molecular fingerprints 
 were calculated using pipeline pilot : mdl maccs keys , fcfp2 , and ecfp2 .
the similarity between the 55 active compounds 
 and the biofocus library compounds was assessed using the tanimoto 
 coefficient .
ecfp4 were used as descriptors , and for each search , 
 250 of the highest ranking compounds were kept using the biofocus 
 database .
the descriptors calculated were 
 alog p , molecular_weight , num_atoms , num_bonds , num_hydrogens , 
 num_positiveatoms , num_negativeatoms , num_ringbonds , num_rotatablebonds , 
 num_aromaticbonds , num_bridgebonds , num_rings , num_aromaticrings , 
 num_ringassemblies , num_chains , num_chainassemblies , num_fragments , 
 num_h_acceptors , num_h_donors , and molecular_solubility .
the 5000 
 closest compounds to any of the hits as assessed by euclidean distance 
 in pca were selected from the biofocus database using pipeline pilot .
the 
 descriptors calculated were as follows : alog p , molecular_weight , 
 num_h_donors , num_h_acceptors , num_rotatablebonds , ecfp_2 , and molecular_polarsurfacearea . 

the model was developed using leave - one - out cross - validation and applied 
 to the biofocus database with compounds with a score of 5 or above 
 being selected .
bioisosteres were identified using powermv using a variety of descriptors , similarity measures , 
 and databases : atom pair / tanimoto , atom pair ( cahart)/tanimoto , fragment 
 pair / tanimoto , pharmacophore fingerprints / tanimoto , and weighted burden 
 number / euclidean methods were used against the acl , gene logic , and 
 nci databases within powermv .
the bioisosteres identified were used 
 as substructure searches to identify compounds in the biofocus database 
 using pipeline pilot .
the scoring functions were implemented in pipeline 
 pilot using the built in algorithms for molecular solubility and log p. bcut descriptors were calculated using the cdk descriptor calculator .
a total of 16000 diverse compounds were selected 
 using a protocol in pipeline pilot .
ecfp4 fingerprints , using a similarity 
 cut off of 0.7 with the tanimoto coefficient , were used together with 
 clustering to compare the coverage of the chemical space of the 16000 
 selected compounds within in the entire biofocus library using protocols 
 in pipeline pilot .
ic50 values ( 50% inhibitory concentrations ) were calculated by 
 using the four - parameter logistic method ( grafit program ; erithacus 
 software , united kingdom ) .
 malaria is responsible for approximately 1 million deaths 
 annually ; thus , continued efforts to discover new antimalarials are 
 required . 
 a hts screen was established to identify novel inhibitors 
 of the parasite 's mitochondrial enzyme nadh : quinone oxidoreductase 
 ( pfndh2 ) . on the basis of only one known inhibitor of this enzyme , 
 
 the challenge was to discover novel inhibitors of pfndh2 with diverse 
 chemical scaffolds . 
 to this end , using a range of ligand - based chemoinformatics 
 methods , 17000 compounds were selected from a commercial library 
 of 750000 compounds . 
 forty - eight compounds were identified 
 with pfndh2 enzyme inhibition ic50 values ranging from 
 100 nm to 40 m and also displayed exciting whole cell antimalarial 
 activity . 
 these novel inhibitors were identified through sampling 
 16% of the available chemical space , while only screening 2% of the 
 library . 
 this study confirms the added value of using multiple ligand - based 
 chemoinformatic approaches and has successfully identified novel distinct 
 chemotypes primed for development as new agents against malaria . 
Introduction
Results and Discussion
Screening
Analysis
Conclusions
Experimental Section
Chemoinformatic Methods