in structure - based virtual screening ( sb - vs ) , 
 each screened compound 
 is submitted to a two - step process . in the first step , a compound 
 is docked to the putative binding pocket of the protein in a number 
 of energetically acceptable binding modes called poses . in the second step , the free energy of binding 
 is assessed for each pose by a scoring function . while there is now a general consensus that most of the 
 popular docking algorithms perform fairly well in generating sound 
 poses , scoring functions most often fail to adequately evaluate the 
 binding affinity . as a result , even the optimiztic success rates that are generally 
 reported in sb - vs benchmark studies might often 
 be insufficient for true ligand discovery when screening large chemical 
 libraries against a novel target with an objective to experimentally 
 test 50100 virtual hits . therefore , all possible means must 
 be employed to improve the odds of obtaining a sizable number of confirmed 
 actives from a small set of designated virtual hits . scoring approaches 
 that can take advantage of known ligand - bound protein structures ( e.g. , 
 enzyme - bound substrates ) are of special interest . in 2003 , deng et 
 al . introduced structural interaction fingerprints ( sift ) , with an objective to represent and analyze three - dimensional 
 protein  ligand binding interactions by encoding them into a 
 one - dimensional binary string . construction of sift is a two - step 
 process consisting of ( i ) identification of residues interacting with 
 the ligand and ( ii ) classification of ligand  residue interactions 
 into any of seven predetermined types ( e.g. , whether the protein backbone 
 or side - chain is involved , residue acts as an h - bond donor or acceptor , 
 etc . ) . , prez - nueno et al . , and marcou and rognan and 
 implemented in the moe software suite . although the sift - like approaches proved to be useful postdocking 
 analysis techniques , they also have a number of intrinsic limitations . for instance , inferring bond types from quite imperfect binding poses , 
 may lead to frequent bond - type detection mistakes . moreover , the bond - type 
 categories ( e.g. , hydrogen bond , polar , nonpolar , and contact ) used in interaction fingerprints do not account 
 for multiple interaction types , such as cation- , which would 
 be labeled as merely a contact . here ligand interaction fingerprints ( splif ) that also exploits the general idea 
 of quantifying 
 and comparing ligand  protein interactions but does it in a 
 very different way . particularly , in splif , three - dimensional structures 
 of interacting ligand and protein fragments are explicitly encoded 
 in the fingerprint . consequently , all possible interaction types that 
 may occur between the fragments ( e.g. ,  , ch , 
 etc . ) the reported fingerprints 
 are wrapped into a normalized quantitative score that expresses the 
 similarity between the interaction profile of a docking pose and that 
 of a reference protein  ligand complex . in order to quantitatively assess the performance of this new approach , 
 we submitted it to a comparative test using it as a postdocking score 
 against a panel of 10 diverse protein targets . the targets along with 
 the sets of respective actives and decoys were selected from the database 
 of useful decoys : enhanced ( dud - e ) . the 
 purpose of this evaluation was to ascertain if splif can outperform 
 and/or bring complementary actives compared to standard or analogous 
 approaches . the 
 database of useful decoys : enhanced ( dud - e ) , a standard test set for 
 virtual screening , was used to validate our fingerprint approach . ten diverse targets covering six protein classes were randomly selected 
 for this benchmark study ( table 1 ) . the protein 
 structures selected were used as targets for docking of actives and 
 decoys ; the resulting poses were processed for subsequent splif generation . all available cocrystallized ligands were retrieved from the protein 
 data bank and used to generate reference splif . in order to 
 assess the diversity of the actives and decoys , we calculated pairwise 
 tanimoto similarities for all ligands used in this study . the histograms 
 in figure s1 ( see the supporting information ) show similarity probability distributions for each combination 
 of target / activity - category ( active or decoy ) . in all histograms , 
 mostly low similarity values are well - populated , meaning that , similar 
 to typical virtual screening libraries , the sets are overall diverse 
 but have clusters of closely related compounds . the sd files 
 for all data sets including gscore , splif , 2d similarity 
 and plif scores can be obtained on request from the authors . initial numbers 
 of actives and decoys 
 from dud - e with the numbers after the gscore filter included in parentheses . the gscore cutoffs are set 
 to allow 
 all reference ligands to be retained ( in hope to retain the most of 
 actives in the test set as well ) . ligands were docked into the active site of 
 the target protein using the glide program in standard docking precision ( glide sp ) . the binding region was 
 defined by a 20   20   20  box centered 
 on the reference ligand selected from dud - e . splif - based rescoring 
 consists of calculating splif for each docking pose and comparing 
 it to that of a reference ( e.g. , experimentally solved ) ligand - protein 
 complex . the essential steps of the algorithm for building a reference 
 splif are depicted in figure 1 . in the first 
 step , two atoms are considered being in a contact if the distance 
 between them is within a specified threshold ( 4.5  in this study ) . 
 in the second step , for each ligand protein atom pair , the 
 respective ligand and protein atoms are expanded to circular fragments , 
 i.e. , fragments that include the atoms in question and their successive 
 neighborhoods up to a certain distance . in figure 1 , the contacting ligand and protein atoms are enclosed in 
 small dotted circles and the respective circular fragments are enclosed 
 in larger concentric circles . each type of circular fragment is assigned 
 an identifier . here , we made use of extended connectivity fingerprints 
 up to the first closest neighbor ( ecfp2 ) as defined in the pipeline 
 pilot software . ecfp retains information 
 about all atom / bond types and uses one unique integer identifier to 
 represent one substructure ( i.e. , circular fragment ) . in the third 
 step , fingerprints 
 is that in splif the interactions are encoded implicitly , as a result 
 of explicitly encoding ligand and protein fragments , whereas in sift - like 
 methods the interaction types need to be encoded explicitly , by means 
 of empirical rules . consequently , most of current sift - like implementations 
 handle only a small number of interaction types . for example , 
 two parallel aromatic fragments would imply a  
 interaction ; a cation fragment positioned on the axis perpendicular 
 to the aromatic plane of another fragment would imply a cation- 
 interaction and so forth . for sift - like fingerprints , if these two 
 types of interaction are not encoded , they will certainly not be identified . 
 while splif records any contacting fragments from the ligand and the 
 protein , meaning the two aromatic fragments and the cation and the 
 aromatic plane here . if the test ligand presents the same fragments , 
 a match to the reference would be assigned by splif , but not by sift - like 
 fingerprints . the interaction fingerprints for the docking 
 poses of a test compound 
 ( an active or a decoy from dud - e ) are computed in a similar fashion 
 to that of the reference fingerprints . the calculation of a splif - based 
 similarity score is depicted in figure 2 . in 
 the first step , the ecfp identifiers of a test splif ( i.e. , the splif 
 of a docking pose to be scored ) are compared to ecfp identifiers of 
 the reference fingerprint , which is done to find matching circular 
 fragments between the test ligand and the reference ligand and results 
 in a list of 2d - matching splif - bits . in the second step , 3d coordinates 
 of the matching circular fragments ( 2d - matching bits ) are retrieved 
 and root - mean - square deviations ( rmsds ) are calculated in order to 
 assess the 3d overlay . the bits for which rmsds are within a specific 
 threshold ( set to 1  in this study ) are considered as ( fully ) 
 matching . next , all atom lists of all matching bits are fused together 
 and deduplicated to form two consolidated lists : ( i ) unique matching 
 ligand atoms ( umla ) and ( ii ) unique matching protein atoms ( umpa ) . finally , a splif - based similarity score is calculated as follows:1where numla is 
 the number of unique matching ligand atoms , 
 i.e. , atoms constituting the matching circular fragments of the docking 
 pose compared to the reference ( on the ligand side ) ; nula is the number of unique ligand atoms , i.e. , atoms constituting 
 all interacting circular fragments of the docking pose ( on the ligand 
 side ) ; numpa is the number of unique matching protein atoms , i.e. , atoms constituting the 
 matching circular fragments of the docking pose compared to the reference 
 ( on the protein side ) ; nupa is the number 
 of unique protein atoms , i.e. , atoms constituting all interacting circular 
 fragments of the docking pose ( on the protein side ) . topological fingerprint similarity 
 ( expressed as the tanimoto coefficient ) of test vs reference ligands 
 was used as a benchmark showing how efficient vs might be without 
 any knowledge of the protein structure at all . the  molecular 
 similarity  component in pipeline pilot was used with default 
 settings . functional connectivity fingerprints up to the second closest 
 neighbor ( fcfp4 ) were selected to describe ligand structures . the 
 atomic label in fcfp may be one of the following : ( 1 ) hydrogen - bond 
 acceptor ; ( 2 ) hydrogen - bond donor ; ( 3 ) positively ionized or positively 
 ionizable ; ( 4 ) negatively ionized or negatively ionizable ; ( 5 ) aromatic ; 
 and ( 6 ) halogen . the plif ( protein  ligand 
 interaction fingerprints ) descriptors implemented in the moe suite were used as a benchmark with respect to interaction 
 fingerprints . the performance of plif would indicate how efficient 
 the first - generation interaction fingerprints might be . interactions 
 are classified as hydrogen bonds , ionic interactions , and surface 
 contacts according to the residues . we applied it to virtual screening 
 here and compared it to our splif approach . the plif descriptors for 
 all protein - bound ligands were generated with the default parameter 
 set in moe . we made use 
 of enrichment factors 
 ( ef ) and ef plots to quantitatively assess the performance of the 
 vs scores under study . the ef plot represents the enrichment for a 
 specific top - scoring percentile of the database as a function of the 
 corresponding percentile px ( in log10 scale):2where as is the 
 number of active ligands in the selected top scoring percentile of 
 the database ; ds is the number of decoys 
 in the selected top scoring percentile of the database ; at is the total number of active ligands in the whole database ; 
 and dt is the total number of decoys in 
 the whole database . enrichment factor indicates the ability of a virtual 
 screening score to increase the proportion of true positives in a 
 respective percentile relative to an average random selection . the 
 logarithmic scale for the x - axis is used to accentuate 
 the contribution of the lower percentiles to the plot . the generic benchmark workflow for each target 
 included the following 
 steps : ( i ) collecting reference ligands from crystal structures ; ( ii ) 
 glide - based docking and scoring of all reference ligands and test 
 ligands ( actives and decoys from dud - e ) ; ( iii ) selecting test - ligand 
 poses with plausible gscore values ( to leave out the most awkward 
 poses ) ; ( iv ) generating splif for reference and test ligands , and 
 calculate splif - based similarity scores ; ( iv ) calculating alternative 
 scores ( ligand - based similarity and first - generation interaction fingerprints 
 plif ) ; and ( iv ) calculating performance metrics . the targets for the 
 benchmark study represented six protein classes including kinases , 
 proteases , synthases , gpcrs , nuclear receptors , and ion channels ( table 1 ) . in step iii , the gscore cutoffs were set on per - target 
 basis ( see table 1 ) to let all reference ligands 
 be selected ( in hope to retain most of the actives from the test set 
 as well ) . the gscore filter removed substantial numbers of decoys , 
 while sacrificing a relatively small number of actives . however , the 
 enrichment produced by step iii alone was still insufficient , which 
 emphasized the importance of further filtering . the enrichment plots 
 for the 10 targets are shown in figure 3 and 
 can be used for a quick visual assessment of the vs scores employed . ideal ef plots ( rendered as black solid lines ) produced by hypothetic 
 scores that would rank all actives above the decoys are given for 
 reference . as expected , the efs resulting from the scoring methods 
 under study differed most significantly at relatively small percentages 
 of selected top scoring compounds ( < 10% of at+ dt ) . as can be seen from the 
 plots , the enrichment curves are often entangled , meaning that the 
 relative performance of a method may depend on how many top - scoring 
 compound you choose to select as virtual hits . the predominance of 
 a single method over a broad range of percentile cutoffs has been 
 observed for five targets : adrb1 and pgh2 ( where splif ranked first 
 over the full range of percentile cutoffs ) ; fak1 and ace ( where ligand - based 
 similarity ranked first ) ; and tryb1 ( where plif ranked first ) . despite 
 that the performance analysis over a broad percentile range seems 
 to be a frequent practice in vs assessment , we consider that it needs 
 to be complemented by a quantitative analysis of enrichment for low - percentile 
 selections . indeed , in real - world vs implementations , where millions 
 of compounds are docked to a target , it would be an unaffordable luxury 
 to consider hundreds of thousands of compounds as potential hits . 
 therefore , in addition to the ef plot analysis , we quantitatively 
 compared the performance of the scores at a 1-percentile cutoff , which 
 was considered realistic for a typical virtual screening campaign 
 ( i.e. , 10  000 potential hits for a 1  000  000 
 compound collection ) . splif ranked first for three targets 
 ( grik1 , pgh2 , and hmdh ) , tied for first rank with one other score 
 for three targets ( mcr , fak1 , and akt1 ) and ranked / tied second for 
 three more targets ( adrb1 , prgr , and ace ) . overall , on the basis of 
 the per - target rankings , splif is definitely the best performing approach 
 in this benchmarking test . the 2d similarity to the reference ligand 
 was the closest follower , which ranked first for two targets and tied 
 for first for two more . gscore alone ranked first for none of the 
 targets ( this result underlines the importance of postscoring functions 
 in sb - vs ) . this result is in a sharp contrast with the performance 
 of plif , splif s closest methodological analog , which failed 
 completely on two tested targets : pgh2 , with ef = 0 , and prgr , with 
 ef = 1 . for comparison , splif ranked first for pgh2 ( with ef = 33 ) 
 and second for prgr ( with ef = 36 ) . ef plots for the performance of benchmarked 
 scores in sb - vs against 
 10 dud - e targets : [ color legend ( by method ) ] splif red ; ligand similarity 
 yellow ; glide - score green ; plif blue ; ideal black . the gray background indicates that 
 the respective score ranked or tied first for this target . it is an interesting result that 
 the least informed method , 2d 
 ligand similarity , was the second best performer in this study . here , 
 by least informed  we mean that , unlike other methods 
 on our panel , the ligand similarity does not make use of any protein  structure 
 information . this relative success of splif and ligand similarity , 
 i.e. , the two methods on the panel that make explicit use of the chemical 
 structure of known actives , may merely reflect the fact that predicting 
 affinity without a prior knowledge of actives , e.g. , by means of docking / scoring , 
 is still in need of substantial improvements . because the ligand- 
 and the splif - based similarity metrics in our study involve the same 
 type of structural descriptors , one might expect that these two methods 
 should perform with comparable sensitivities and that splif is expected 
 to be more specific due to the 3d information about the protein ligand 
 interactions embedded in the reference splif ( see below a more detailed 
 discussion on the relative splif performance ) . we have also 
 analyzed the reasons of the varying splif performance 
 and , more specifically , of its poor performance on tryb1 . a plausible 
 explanation comes from the inspection of the absolute splif - score 
 values . while we did not pay much attention to how high / low the score 
 values are when selecting the top - percentile hits , they display a 
 substantial intertarget variation . for instance , figure 4 shows the probability density distributions of splif - scores 
 for the most successful splif - screening ( hmdh ) , the least successful 
 one ( tryb1 ) and the overall distributions for actives and decoys ( distributions 
 for all 10 targets can be found in supporting 
 information figure s2 ) . it can be seen from the overall distributions 
 in figure 4 that a splif - score cutoff of 0.5 
 would allow for a very significant prediction specificity ( i.e. , low 
 false - positives rate ) , while lower splif - score values are highly populated 
 by both actives and decoys . then , it is easy to see that higher splif - score 
 values are significantly populated for hmdh and only marginally for 
 tryb1 . the latter observation simply means that there is a large number 
 of splif - based analogs for hmdh - references among the hmdh - actives 
 and too few analogs for tryb1-references among tryb1-actives . that 
 is , in the case of tryb1 , splif underperformed simply because we lacked 
  right  structural references . unfortunately , like in 
 the case of most vs scores , this explanation does not offer any means 
 of predicting how splif would perform on a given screening collection 
 against a new target . however , one might suggest general principles 
 of the relative splif performance with respect to other screening 
 approaches . first , the sensitivity of splif is expected to be dictated 
 by the reference ligands ( and their respective interactions with the 
 target protein ) . and , hence , it is restricted compared to docking - based 
 approaches , which potentially can perceive any active ligand in any 
 screening library . therefore , as in the case of ligand - based similarity , 
 the splif s sensitivity would grow as the size and diversity 
 of the reference set grow ( while its specificity would remain unchanged ) . our hypothesis about the splif performance relative to ligand similarity 
 was that splif would be even less sensitive , but more specific , which 
 would result in higher enrichment rates . while a quantitative testing 
 of this hypothesis may need a more substantial effort than the current 
 study , the data presented here corroborate it qualitatively by showing 
 an overall gain in enrichment rate when using splif . probability density distributions 
 for splif - scores by category 
 ( actives , decoys , hmdh - actives , tryb1-actives ) . another goal in this study was to assess how complementary 
 splif 
 is to the other benchmarked scores . to this end , for each target we 
 calculated the overlap between the true - positives identified by splif 
 and each other benchmarked score ( see table 2 ) . the overlaps range from 0% to 83% with a median of 36% . the highest 
 overlaps ( 83% and 72% ) are with plif , followed by the overlaps with 
 gscore ( 68% ) and ligand - based similarity ( 67% ) . interestingly , and 
 contrary to expectations , the overlaps with plif are low ( < 50% ) 
 for most targets . the latter observation means that the explicit encoding 
 of the chemical structure ( which constitutes the novelty of splif 
 compared to plif and other first - generation interaction fingerprints ) 
 does indeed result in better vs performance and complementary actives . overall , the data show that in terms of true positives identified 
 splif is highly complementary to the other benchmarked scores . when it comes to a general assessment of whether a new scoring 
 method may be recommended for broad use , there are three key questions 
 to be answered : does the new method show any improvement compared 
 to its close analogs and popular standard tools ? does the new method 
 show robust performance across a broad range of targets ? does the 
 new method bring additional actives compared to similarly performing 
 techniques ? while the importance of the first question is evident , 
 the second and third questions are equally important for the following 
 reasons . it has been demonstrated in multiple benchmark studies that 
 the relative performance of any scoring method varies with protein 
 target and that in general it is impossible to predict , which function 
 would perform best on a given target . a solution to address this uncertainty would be to use a panel of 
 scoring functions and merge the respective hit lists using either 
 and or or logic . however , this still requires all methods on the panel 
 to perform reasonably well . otherwise , a single strongly underperforming 
 ( as happened twice to plif and twice to gscore in this study ) would 
 significantly undermine the overall performance . we believe that the 
 presented data on splif performance supports an affirmative response 
 to all three questions . indeed , although it did not rank first for 
 all protein targets , it did rank first more frequently than any other 
 benchmarked method , hence showing a tangible improvement . also , splif 
 did not rank last for any of the targets and showed significant enrichment 
 for all of them . finally , for 9 out of 10 targets splif brought 30% 
 or more additional true positives compared to the method that ranked 
 higher or tied with splif ( or was one rank below when splif ranked 
 first ) . splif is an interaction fingerprint that 
 explicitly encodes structural 
 information and hence implicitly captures all types of ligand protein 
 interactions ( e.g. , stacking , polarization , cation- , ch- ) . 
 here , we demonstrated that splif - based similarity of ligand - protein 
 interaction motifs is a valuable metric that helps to improve the 
 odds of finding true hits via structure - based virtual screening . splif - score 
 showed a robust performance over a broad panel of diverse protein 
 targets . the splif - based hit lists featured true positives , which 
 ( i ) can not be obtained by a docking score alone , ( ii ) are not structurally 
 similar to reference ligands , and ( iii ) are complementary to the hits 
 that might be obtained using analogous interaction fingerprints . we 
 therefore recommend the use of splif either as a single rescoring 
 technique to increase the probability of success or as a complement 
 to other scoring functions in order to increase the chemical diversity 
 of resulting hits .
accurate and affordable assessment 
 of ligand  protein affinity 
 for structure - based virtual screening ( sb - vs ) is a standing challenge . 
 hence , empirical postdocking filters making use of various types of 
 structure  activity information may prove useful . here , we introduce 
 one such filter based upon three - dimensional structural protein  ligand 
 interaction fingerprints ( splif ) . 
 splif permits quantitative assessment 
 of whether a docking pose interacts with the protein target similarly 
 to a known ligand and rescues active compounds penalized by poor initial 
 docking scores . 
 an extensive benchmark study on 10 diverse data sets 
 selected from the dud - e database has been performed in order to evaluate 
 the absolute and relative efficiency of this method . 
 splif demonstrated 
 an overall better performance than relevant standard methods .