many 
 docking and virtual screening programs , such as dock , employ intermolecular interaction energy functions that contain 
 nonbonded van der waals and electrostatic terms to rank - order ( i.e. , 
 score ) small molecule binding geometries ( poses ) generated in the 
 context of a defined protein binding site . other physically reasonable 
 scoring terms such as intermolecular hydrogen - bonding , ligand desolvation , 
 numbers of ligand rotatable bonds , and buried surface area , among 
 others , have also been explored . in all 
 cases , the objective is to enrich for ligands with good geometric 
 and chemical compatibility with the target so that promising druglike 
 leads can be identified . reported a new dock scoring method termed footprint similarity score 
 which can be used to identify compounds that match a specific molecular 
 interaction energy pattern ( i.e. , footprint ) based on a known reference 
 ligand . encouraged by the recent successes from 
 our laboratory , in which  footprints  were used to identify 
 promising lead compounds , we have developed an analogous similarity - based 
 scoring method for dock that employs  pharmacophores  . both methods yield enhanced docking outcomes but do so in an orthogonal 
 sense ( energy vs geometry ) . historically , the concept of a pharmacophore 
 is generally attributed 
 to ehrilich and has evolved to include the 
 three - dimensional spatial arrangements of key chemical features essential 
 for compound affinity leading to a biological effect . a thorough summary of the development of pharmacophores and early 
 works on modeling can be found in a recent publication by gner 
 et al . also discuss technological advances 
 and challenges of using different pharmacophore methods in modern 
 drug discovery . in practice , pharmacophore features can be derived 
 from known active ligand(s ) , a defined binding site geometry , or a 
 combination of both . importantly , the abundance of atomic - resolution 
 structures publically available in the protein data bank ( pdb ) can be used to derive pharmacophore models for 
 compounds with verified experimental activity to help guide structure - based 
 drug design . a partial list of programs that incorporate pharmacophore 
 modeling includes catalyst , gasp , ligandscout , phase , galahad , phdock , and moe , among others . while such prior 
 efforts are important tools and represent different approaches for 
 modeling , the goal of the present work is to provide a pharmacophore 
 method that can leverage dock s powerful anchor - and - grow sampling 
 strategy while taking advantage of different combinations of scoring 
 functions . the new dock pharmacophore scoring protocol termed 
 pharmacophore 
 matching similarity ( fms ) encodes useful chemical features , including 
 hydrogen bond acceptors / donors , hydrophobic groups , positively / negatively 
 charged groups , and aromatic / nonaromatic rings . initial pharmacophore 
 types are generated based on atom type and chemical environment , defined 
 by neighboring atoms in the same ligand molecule , and are processed 
 to create a pharmacophore feature set ( ph4 model ) with coordinates 
 and directionality as shown in figure 1 for 
 three representative druglike compounds . importantly , the amount of 
 overlap ( termed fms score ) between a user reference ligand pharmacophore 
 and candidate pharmacophores derived from docked compounds can be 
 computed on - the - fly during docking ( or rescoring ) without the need 
 for a separate preprocessing step . this enables large virtual screening 
 libraries to be sorted ( i.e. , rank - ordered ) with the function in an 
 efficient manner . two - dimensional ( 2d ) representations for three approved 
 drugs ( top ) 
 and corresponding dock pharmacophore ( ph4 ) models ( bottom ) . features 
 include : ( i ) hydrogen bond acceptor in red , ( ii ) hydrogen bond donor 
 in blue , ( iii ) hydrophobic atom / group in cyan , ( iv ) aromatic ring 
 center and direction in orange , ( v ) nonaromatic ring center and direction 
 in yellow , ( vi ) negatively charged group center in green , and ( vii ) 
 positively charged group center in magenta . structures of nevirapine , 
 erlotinib , and zanamivir from pdb codes 1vrt , 1m17 , and 1a4 g , respectively . specific validation tests used in this work to evaluate the 
 new 
 scoring protocol include pose reproduction , crossdocking , and enrichment . all fms results are compared relative to using the standard dock single 
 grid energy ( sge ) approach , as well as a combined scoring function 
 ( fms+sge ) consisting of both terms . in pose reproduction , crystallographic 
 ligand positions are used as a reference to test if a given method 
 is capable of reproducing nativelike poses ( within 2  of the 
 x - ray pose ) using the large sb2012 validation database ( update of 
 sb2010 ) developed in our laboratory . 
 in crossdocking , select protein families from sb2012 ( based on high 
 sequence homology ) , are employed to evaluate docking accuracy across 
 an n  n matrix when all ligands 
 from a family are docked to each individual receptor . in enrichment , 
 active ligands and accompanying decoy compounds taken from the dud - e database are docked to 15 different targets 
 to assess the ability of the new scoring schemes to correctly rank - order 
 active ligands earlier than decoys . finally , retrospective analyses 
 of three virtual screens to targets of pharmaceutical interest ( egfr , 
 igf-1r , and hivgp41 ) are shown in which fms - based scoring ( fms and 
 fms+sge ) was used as a data - mining tool to identify compounds with 
 high pharmacophore overlap to small molecules or peptide ligand side 
 chains . overall , the results of this comprehensive study suggest the 
 new method will be a useful addition to the growing number of scoring 
 and sampling methods available in dock . pharmacophore 
 modeling in this study uses a two - step protocol involving : ( 1 ) assignment 
 of a pharmacophore type definition to each ligand atom , followed by 
 ( 2 ) construction of pharmacophore points with pharmacophore labels 
 based on the type definitions . inspired by chemical matching code 
 previously developed for dock , we employ a type definition 
 model based on sybyl atom types and environment 
 ( neighboring atoms ) . the finite list of pharmacophore type definitions 
 is stored in the ph4.defn parameter file ( table 1 ) and can be customized to include other pharmacophore 
 types . for clarity , it is important to emphasize there is a distinction 
 between pharmacophore type definitions ( for the individually typed 
 atoms ) and the pharmacophore label definitions ( for the final constructed 
 pharmacophore points ) derived from the pharmacophore types . in the 
 atom environment definition list in table 1 , parentheses ( ) specify  atoms that must be bonded to the 
 parent atom  , while square brackets [ ] specify the integer in the definition represents the number of 
 atoms associated in the rule . for example , the syntax n.pl3 
 ( 2 * ) [ h ]  specifies a trigonal planar nitrogen connected 
 to at least two other atoms and not bound to any hydrogen atoms . for 
 this work , eight pharmacophore types are assigned to individual atoms 
 as outlined in table 1 : ( 1 ) null or no assignment , 
 ( 2 ) hydrophobic , ( 3 ) hydrogen bond donor , ( 4 ) hydrogen bond acceptor , 
 ( 5 ) aromatic ring member , ( 6 ) hydrogen bond acceptor in an aromatic 
 ring , ( 7 ) negatively charged species , and ( 8) positively charged species . the resulting atom set is postprocessed to generate pharmacophore 
 points with coordinates that specify the position of the pharmacophore 
 point center and vectors indicating the direction of potential interactions . aromatic and nonaromatic rings are identified by checking for closed 
 loops formed by connected atoms . the coordinate of the ring center , 
 averaged over all ring member coordinates , is computed and saved as 
 the pharmacophore point ( figure 2 ) . the average 
 normal vector of the plane defined by adjacent ring center - to - vertex 
 vectors ( figure 2 , dashed blue lines ) is calculated 
 and saved as the direction vector of the pharmacophore point . if the 
 individual normal vectors ( figure 2 , solid 
 blue lines ) of the ring are all within an angle cutoff c to the average normal vector ( figure 2a , solid red lines ) then the pharmacophore point is marked as an 
 aromatic ring ( figure 2a ) . otherwise it is 
 labeled as nonaromatic ( figure 2b ) . in practice , i is measured by directly computing the inner product 
 of two vectors ( xi ) which is converted to degrees by the 
 inverse function of cosine as arccos(xi ) = i . on the basis of examining crystallographic ligand coordinates 
 containing aromatic and nonaromatics rings , we use as a cutoff criteria 
 arccos(0.99 )  8.11 degrees to determine if a ring is planar . pharmacophore 
 feature assignment for rings : ( a ) aromatic ( close 
 to planar ) and ( b ) nonaromatic ( not planar ) . ring center - to - vertex 
 vectors shown as dashed blue lines , individual normal vector shown 
 as solid blue lines , and averaged normal vectors shown in solid red 
 lines . the angle between the blue and the red vectors are compared 
 to a threshold to determine the planarity of the ring . atoms with hydrophobic and positive / negative pharmacophore 
 type 
 definitions are saved individually as pharmacophore points inheriting 
 the same type as their pharmacophore labels . for these cases , default 
 direction vectors ( which do not affect the score ) for the hydrogen bond acceptor , 
 the coordinate of the polar atom is saved as the pharmacophore point . the average of vectors pointing from all neighbor atoms to the acceptor 
 atom is saved as the direction vector , indicating the potential position 
 of the coupling hydrogen bond donor , as indicated by red arrows in 
 table 2 , which shows example pharmacophores 
 derived for several small organic molecules . the hydrogen bond donor 
 uses the coordinate of the hydrogen atom as that of the pharmacophore 
 point . similarly , the vector pointing from the donor hydrogen to the 
 connecting polar atom is saved as a normalized direction vector , indicated 
 as blue arrows in table 2 . the combined set 
 of all pharmacophore points is called the molecular pharmacophore 
 ( ph4 ) model , which may include hydrophobic ( pho ) , hydrogen bond donor 
 ( hbd ) , hydrogen bond acceptor ( hba ) , aromatic ring ( aro ) , nonaromatic 
 ring ( rng ) , positively charged ( pos ) , and negatively charged ( neg ) 
 features ( see table 2 ) . pho in cyan , hba 
 ( vertex and vector ) 
 in red , hbd vector in blue , hydrogen vertex in gray , aro ( vertex and 
 vector ) in orange , rng ( vertex and vector ) in yellow , pos in magenta , 
 and neg in green . three - dimensional 
 ( 3d ) molecules 
 and pharmacophore visualization generated with chimera to gauge how many pharmacophore 
 features are present in typically 
 sized compounds , figure 3 plots histograms 
 derived from 1043 molecules in their x - ray pose taken from the sb2012 
 testset used in this work to gauge pose reproduction and crossdocking 
 accuracy . as a reduced representation , the pharmacophore model derived 
 for each molecule contains ( on average ) a much smaller number of pharmacophore 
 points ( 16.0 ) relative to the total number of atoms ( 49.2 ) as shown 
 in figure 3 ( panel b vs a ) . in terms of specific 
 features , molecules in sb2012 contain on average of 1.9 aromatic rings , 
 0.9 nonaromatic rings , 2.6 hydrophobic groups , 3.6 hydrogen bond donors , 
 and 4.6 hydrogen bond acceptors . values for the two latter features 
 are indicative of the druglike characteristics of many of the compounds 
 in sb2012 for which 80% have less than 5 hydrogen bond donors 
 and 58% have less than 10 hydrogen bond acceptors in rough 
 agreement with lipinski - like rules . about 
 1/4 of the testset contains molecules with positively ( 199 ) or negatively 
 ( 287 ) charged functionality . in principle , given the smaller 
 feature space , use of pharmacophore 
 models should yield faster run times than an all - atom based scoring 
 function . in terms of rescoring poses without sampling , timing tests 
 indicate that under the current conditions , computing the pharmacophore 
 matching similarity ( fms ) score between two molecules is faster than 
 computing the standard energy score by about 3.5 fold . comparing production 
 times when using the fms method to drive ligand sampling is less straightforward , 
 due to the much larger numbers of poses generated when using fms compared 
 to sge ( discussed further below ) . however , when normalized by the 
 size of the final pose ensemble retained using fms or sge methods , 
 time per pose with fms is faster by about 1.5 fold . number of pharmacophore 
 features computed by dock fms scoring function 
 using the sb2012 docking testset ( n = 1043 molecules ) . after computing the pharmacophore ( ph4 ) model using 
 the protocol described above for both the reference and candidate 
 poses , molecular similarity between the two poses is evaluated by 
 the degree of pharmacophore overlap , termed here pharmacophore matching 
 similarity score ( fms score ) . for each pharmacophore point a with pharmacophore label a , cartesian 
 coordinate x = [ x1 , x2 , x3 ] 
 and direction vector v = [ v1 , v2 , v3 ] in the reference pharmacophore , is compared to every pharmacophore 
 point bi in the candidate 
 pharmacophore in three steps : ( i ) label check , ( ii ) distance check , 
 and ( iii ) direction check . the pharmacophore label a is used to eliminate pharmacophore points in the candidate pharmacophore 
 that have different labels . the distance between a and the candidate pharmacophore point bi , computed as di = x   = [ j  =  13(xj  yji ) ] where   = [ y1i , y2i , y3i ] is the cartesian 
 coordinate of bi , is 
 compared to a distance cutoff r. only when di  r will the corresponding pharmacophore point bi be further investigated . a constant r value is assigned to all reference pharmacophore points 
 as a default parameter , but for a ring ( aromatic or nonaromatic ) the 
 radius of the ring is assigned as r. the scalar projection 
 of the normalized direction vector v onto 
 that of bi ,   = [ w1i , w2i , w3i ] is calculated . the condition 
 that the vector projection v = j  =  13vj  wji   implies the angle between the two 
 direction vectors v and   is within arccos  , 
 which ensures 
 that the two vectors are pointing in approximately the same direction . 
 a perfect vector overlap ( when v = ) between two normalized direction vectors 
 will be v    = v 
 = 1 . by default , a scalar projection cutoff of  = cos(45 ) 0.7071 is used . note that for hydrophobic and charged feature 
 labeled points , v      is always true , as the 
 same default value of ( 1,0,0 ) is assigned to both v and . for a ring , the absolute value of the 
 scalar projection |v  | is used to account for its orientation 
 ( i.e. , vectors above and below the plane of the ring ) . if all of the 
 above criteria are met then the two pharmacophore points a and bi are deemed a 
 match . in figure 4 , three aro pharmacophore 
 point pairs are shown to illustrate how the three criteria ( label , 
 distance , and direction ) are used to identify matches in rings . the 
 first criterion ( same label ) is met by all three pairs as the pharmacophore 
 points shown are all labeled as aromatic rings ( aro ) . the first pair 
 ( figure 4a ) has both a small distance ( d  r ) and good directional agreement 
 ( |v  | >  ) and thus 
 represents a well - matched 
 case . the second pair ( figure 4b ) , although 
 the ring vectors are well - aligned , is not matched due to the large 
 distance between the pharmacophore centers . for the third pair ( figure 4c ) , although the distance between ring centers is 
 small , this case is also not considered a match due to the large difference 
 in ring vector orientation ( |v  | <  ) . example pharmacophore 
 matches for aromatic rings showing : ( a ) well - matched 
 case with the same labels , small distance , and similar vector directions , 
 ( b ) not matched case with the same labels , large distance , and similar 
 vector directions , and ( c ) not matched case with the same labels , 
 small distance , and different vector directions . all matched point pairs between the reference and candidate 
 pharmacophore 
 models are investigated by their geometric relationships to obtain 
 a quantitative measurement of matching . the residual between two matched 
 points is defined as ai = [ ( di)/|v  | ] which takes into account 
 both the distance and overlap in direction . after comparing pharmacophore 
 point a with all candidate pharmacophore points bi , the best matched point b+ with the lowest matching residual a+ will be retained for the pharmacophore matching similarity ( fms ) 
 score calculation . if no match was found for a then 
 it will not contribute to the residual term of fms score . the residual 
 term in combination with a match rate term defines the numerical value 
 of the fms score via eq 1.1here , k is a constant parameter ; n stands for the total number of matches ( note that for 
 each reference pose pharmacophore point , one match is counted at most ) ; n is the number of pharmacophore points in the reference 
 pharmacophore ; and aj+ represents 
 the best matching residual of a matched reference pharmacophore point aj . on the basis of similarity 
 measurements in graph theory , the fms score uses 
 the match rate term k(1 n / n ) to prioritize poses with higher numbers of pharmacophore 
 matches to the reference pose . poses with similar numbers of matches 
 will be differentiated by their root - mean - square matching residuals 
 [ j  =  1i(aj+)/n ] . note that the total number of matches n needs to be larger than zero for eq 1 to give 
 a reasonable value . when no match is identified ( n = 0 ) , an arbitrary large score x is assigned ( x is set to be larger than the upper bound of fms score 
 value when n > 0 ) . for any reference and candidate 
 pair of molecules , fms score ranges between 0 ( perfect match ) and x , which depend on choices for k , distance 
 cutoff r , and scalar projection cutoff . for 
 pharmacophore - based docking , lower fms scores are more desirable . flowchart schematic outlining pharmacophore - based virtual 
 screening 
 in dock . to determine a default set of 
 values for k , r , and  in 
 eq 1 , we performed 
 a series of rescoring tests using ligand geometries generated with 
 the standard dock protocol , for comparison with crystallographic references , 
 and pose reproduction success ( defined in the next section ) was determined . four values for k ( 1 , 2 , 5 , and 10 ) , three values 
 for r ( 0.5  , 1.0  , and 1.5  ) , and 
 three values for  ( 30 , 45 , and 60 ) were examined . 
 as a general rule , use of stricter matching criteria ( shorter distance 
 cutoff r , smaller angle cutoff  ) led to lower 
 docking success rates . in addition , the success rate increased as 
 the matching rate term weight k was increased from 
 1 to 5 but remained relatively steady from k = 5 
 to 10 . with these results taken into consideration , the set comprising k = 5 , r = 1  ,  = 45 , 
 and x = 20 yielded generally good pose reproduction 
 success and had values which were roughly in - between the different 
 ranges explored . although other combinations might also have been 
 suitable , this set was ultimately employed for all subsequent fms 
 sampling and scoring experiments used in this work . in order 
 to approximate the accuracy of ligand poses predicted by a given protocol 
 for unknown systems , pose reproduction control experiments are performed 
 over a large number of crystallographic complex structures . ideally , 
 the best - scored docked pose should agree with the crystal pose . following 
 our previous work , docking results are 
 categorized as one of three outcomes : docking success ( success ) , scoring 
 failures ( score fail ) , and sampling failures ( sample fail ) . over a 
 large data set , docking success is defined when the rmsd between the best scored 
 pose and native ( crystal ) pose is 2  . a scoring failure 
 is defined when a close - to - native pose is sampled , but the best scored 
 pose is > 2  from the native pose . finally , a sampling failure 
 is defined if none of the sampled poses are within 2  of the 
 native pose . for ligands of druglike 
 size , low rmsd values also typically correspond to good visual overlap 
 between docked and reference ligand poses . all statistics reported 
 in this work make use of  symmetry corrected  rmsds 
 to account for chemically identical functionality ( i.e. , symmetric 
 ring flips , carboxylate flips , etc . ) or completely symmetric molecules , 
 adopting visually indistinguishable conformations as described in 
 detail previously . the updated pose reproduction 
 database termed sb2012 ( an update of the sb2010 database ) was used for all pose reproduction and crossdocking 
 ( defined below ) experiments . the set , derived from complexes in the 
 protein databank ( pdb ) , contains 1043 protein ligand systems 
 in ready to dock format and is freely available online at www.rizzolab.org . ( a ) 
 pose reproduction cases with different outcomes : success ( top , pdb 
 code 3cpa ) , 
 score fail ( middle , pdb code 1v2w ) , and sample fail ( bottom , pdb code 1gkc ) . crystal poses 
 in orange , best scored poses in magenta , best rmsd pose in cyan . ( b ) 
 representative crossdocking heatmap showing docking outcome as a function 
 of docking all ligands ( lig1 , lig2 , ... , lign ) to all receptors ( rec1 , 
 rec2 , ... , recn ) for an aligned group of proteins with nearly identical 
 sequence homology . ( c ) hypothetical database enrichment results showing 
 a partitioning of data based on fms score ranking ( 0 to 6 ) for a group 
 of ligands ( left bottom , magenta curve ) comprised of a known active 
 ligand set ( left middle , blue curve ) and inactive decoy set ( left 
 top , red curve ) . the vertical dashed line represents a hypothetical 
 fms score cutoff dividing the total group into ( x ) predicted positive and ( y ) predicted negative 
 sets which can be partitioned into four quadrants ( i  iv ) defined 
 respectively as true positives ( tp , i ) , false positives ( fp , ii ) , 
 true negatives ( tn , iii ) , and false negatives ( fn , iv ) . also shown 
 is an roc curve , which for this example plots individual points which 
 correspond to various fms score cutoffs in the left panel . the coordinate 
 of each point is determined by the false positive rate and true positive 
 rate at that fms score cutoff . all dock experiments in this work employed well - defined receptor 
 and ligand setup protocols , in conjunction with the flexible ligand 
 sampling protocol termed flx , as previously described . briefly , in terms of receptor setup , several 
 accessory programs are used to compute a molecular surface ( dms ) , generate docking spheres to guide sampling ( sphgen ) , and precompute the potential energy on a grid 
 which speeds up the docking calculations ( grid ) . key setup parameters include the use of 69 lennard - jones 
 and distance dependent dielectric ( -4r ) , a 0.3  resolution , 
 and a grid box size extending 8  in all directions based on 
 the docking spheres ( 75 spheres max ) . key docking parameters include 
 use of the on - the - fly anchor - and - grow algorithm to orient and assemble 
 ligands layer - by - layer , retaining a maximum of 5000 completely grown 
 conformers to be ranked by the primary scoring function and saving 
 a maximum of 100 conformers ( after clustering to remove redundancy , 
 rmsd  2  ) . ligands were energy - minimized at each stage 
 of conformational search ( 500 iterations per cycle per anchor / step 
 max ) , and those exceeding a total score cutoff of 100.0 were removed . the different functions employed in this work include : ( 1 ) single 
 grid energy ( sge ) score , ( 2 ) dock cartesian energy ( dce ) score which 
 is equivalent to sge but in cartesian space , ( 3 ) pharmacophore matching 
 similarity ( fms ) score , and ( 4 ) the combination of the two - termed 
 fms+sge ( or fms+dce ) score . for the combined function , the fms score 
 was weighted by 10-fold so when summed together the fms and sge ( or 
 dce ) terms would be more equally balanced . in addition 
 to pose production experiments , crossdocking was employed in which 
 highly homologous protein complexes , with nearly identical structure 
 and sequence ( termed here a protein receptor family ) , are aligned 
 into a common reference frame and each ligand is docked into each 
 receptor as shown in figure 6b . such families 
 inherently contain variability due to different crystallization conditions , 
 cocrystallization with different ligands , as well as receptor point 
 mutations , among others . nevertheless , the hypothesis in crossdocking 
 is that ligands should adopt similar binding geometries in highly 
 homologous receptors , provided there are no large deformations in 
 the binding site or incompatible mutations . the results are expressed 
 as a n  n heatmap ( n = number of systems ) with docking success plotted in blue , 
 sampling failures plotted in red , and scoring failures plotted in 
 green ( figure 6b ) . off - diagonal elements 
 are  theoretical  protein  ligand pairs and the 
 reference , in some instances , may be incompatible . to identify incompatible 
 elements , we employ a clash matrix check , independent of the actual crossdocking experiment , in which all 
 matrix complexes ( representing cognate and theoretical references ) 
 are subject to a short restrained energy minimization . if the minimized 
 ligand pose moves > 2  from the starting pose , or the pose 
 bears 
 an unfavorable energy score ( > 0 kcal / mol ) , the specific reference 
 pair containing the clash is not included is crossdocking success 
 evaluations ( figure 6b , black squares ) . all 
 crossdocking studies employed the flx docking protocol , and results 
 are reported for both the diagonal and the entire matrix . a third method 
 used to evaluate docking methods is enrichment ( figure 6c ) . databases such as the directory of useful decoys ( dud ) and the newer enhanced version called dud - e contain large sets of known active compounds 
 ( and property - matched decoys ) , which are docked to a specific target 
 and the results are rank - ordered . good enrichment is achieved when 
 greater numbers of actives are ranked earlier in the list compared 
 to the decoys . for more in - depth discussion on using dock to estimate 
 enrichment , interested readers should consult brozell et al . briefly , for this work , ranked results were 
 visualized as receiver operating characteristic ( roc ) curves which 
 plots how the true positive rate ( true positive / total positive ) changes 
 relative to the false positive rate ( false positive / total negative ) . accompanying area under the curve ( auc ) analysis was also performed 
 and used to estimate fold enrichment values ( fe = auc / aucrandom ) , relative to random , at 0.1% , 1% , 10% , and 100% of the database 
 examined . for virtual screening , early enrichment is of particular 
 importance , as typical applications will only focus on ( i.e. , purchase ) 
 small subsets of molecules ranked very early ( i.e. , 0.11% ) 
 in the database . in the theoretical example shown in figure 6c , which employed fms score to rank active and decoy 
 ligands shown in the left panels , the roc curve on the right represents 
 a good enrichment case relative to random ( figure 6c , magenta vs dashed line ) . by specifying a specific score 
 cutoff ( figure 6c left bottom panel , dashed 
 line ) the data can also be partitioned into two groups for which molecules 
 with smaller scores ( better overlap ) are defined as predicted positives 
 ( x ) and molecules with higher scores ( worse overlap ) 
 defined as predicted negatives ( y ) . if , as in the 
 present example , the results are in fact known , this allows ligands 
 in the active group to be classified as true positive ( i ) or false 
 negative ( iv ) , and ligands in the inactive ( decoy ) group classified 
 as false positive ( ii ) and true negative ( iii ) . by varying the cutoff , the number of molecules in the four subsets i  iv will change 
 accordingly . the receptor pdb files were already available in sb2012 ( same pdb 
 code as dud - e ) , and the active and decoy ligands were downloaded from 
 the dud - e web site and used as is . it is important to note that some 
 ligands ( active and decoys ) for these systems contain multiple entries 
 representing , for example , different tautomers or protonation states . 
 for all enrichment analyses , in the case of duplicate i d codes , only 
 the best - scored molecule was retained . for each system , the native 
 cognate ligand in the original pdb file is used as the pharmacophore 
 reference for fms scoring . as in the pose reproduction and crossdocking 
 studies , the enrichment tests also employed the flx docking protocol . 
 with this protocol , predicted ligand poses with accompanying scores 
 were obtained for approximately all but 2% of the actives and decoys 
 listed in table 3 . table 4 shows pose reproduction outcomes computed for the 
 three dock protocols tested ( sge , fms , and fms+sge ) , in which a given 
 function was used for both sampling and scoring ( diagonal blocks in 
 gray box ) or when rescored using the other two scoring functions ( off - diagonal 
 blocks ) . all experiments were performed under the same conditions 
 except for the sampling and/or scoring method employed . it is important 
 to emphasize that use of an alternative function to rerank an ensemble 
 of poses generated by any given method ( table 4 , off - diagonal blocks ) will , in most cases , lead to a different group 
 of top - scored results , but the number of sampling failures remains 
 unchanged . sge sampling size 
 = 89083 poses , 
 fms sampling size = 337674 poses , fms+sge sampling size = 59237 poses . in general , the diagonal results 
 ( table 4 , gray boxes ) using the three different 
 methods yield high percentages 
 of success across the 1043 systems in sb2012 with the flx ligand protocol . importantly , the sge success rate ( 72.5% ) is consistent with earlier 
 work from our laboratory , using a smaller 
 data set ( 68.5% , n = 780 ) , indicating good reproducibility 
 of dock . overall , the diagonal results in table 4 reveal a clear trend in terms of outcome with success following 
 sge < fms < fms+sge and sampling and scoring failures following 
 sge > fms > fms+sge . the very high success rates when using 
 fms ( 93.5% ) 
 or the combination fms+sge ( 98.3% ) is significant and represents a 
 2025% improvement over the standard dock method employing 
 sge ( 72.5% ) . on one hand , such high success rates are expected given 
 that for any system the x - ray reference ligand and docked ligand are 
 the same molecule in terms of topology and thus have the exact same 
 number of pharmacophore features . in actual practice , for virtual 
 screening , the number of features between a reference and candidate 
 would change as each new ligand was docked . nevertheless , the good 
 correspondence in these validation tests provides strong evidence 
 the newly implemented dock pharmacophore labeling , modeling , and overlap 
 routines are behaving as expected and yield robust results over a 
 large pose reproduction testset . importantly , the fms method is straightforward 
 to use and only requires that the user input a reference molecule 
 consisting of a single 3d conformation . the processing of the candidate 
 pose(s ) to determine fms scores is done automatically and on - the - fly . ongoing work to allow a text - based pharmacophore reference to be used 
 as a query will further simplify the procedure of customizing inputs 
 for fms score calculation . of the three 
 methods tested , 
 the fms+sge protocol yields the lowest sampling ( 0.7% ) and scoring 
 ( 1.1% ) failure rates on the diagonal . in an attempt to understand 
 what led to the small subset of failures ( n = 18 ) , 
 docked poses for the group were examined . out of the seven sampling 
 failures , one system did not complete growth , which , although infrequent , 
 can happen using dock under some circumstances . and for the remaining 
 6 sampling failures , 4 are relatively large molecules with up to 35 
 rotatable bonds and thus extremely challenging for any docking protocol . 
 in terms of the 11 scoring failures , a noteworthy result ( figure 7 ) is that 7 out of the 11 systems ( pdb codes : 1xlz , 6tmn , 2itx , 1o86 , 1v2w , 1v2q , and 5tmn ) actually show good 
 correspondence both in terms of visual overlap as well as rmsd ( 2.072.34 
  ) . thus , these 7 can be classified as  near misses  
 for which only a part of the ligand geometry adopts a conformation 
 different than the x - ray pose . consistent with expectation , in all 
 but two cases ( 7cpa , 6cpa ) , geometries corresponding to the best rmsd 
 also have a lower fms score . the fact that the fms+sge protocol correctly 
 identifies a nativelike pose in nearly all 1043 cases is notable . eleven 
 scoring failures derived from fms+sge guided docking showing 
 overlaid poses , pdb code identifier . rmsd in angstroms and fms scores 
 in parentheses for the best fms+sge scored pose ( first row , magenta ) 
 and the best fms+sge rmsd pose ( second row , cyan ) relative to the 
 crystal pose in orange . in terms of the off - diagonal blocks ( table 4 ) , rescoring the standard sge results ( 72.5% ) with 
 fms ( 82.5% ) or fms+sge ( 84.0% ) reveals a similar trend with sge < 
 fms < incorrectly sampled geometries , the 
 maximum success rate attainable is a function of the poses originally 
 sampled , which for sge is 90.2% ( e.g. , 100% - 9.8% sampling failures ) . this specific experiment is important as the improvement in success 
 when rescoring sge - derived results with fms or fms+sge ( 1011% ) 
 suggests the current implementation is a viable way to postprocess 
 docked poses and identify those compounds with good pharmacophore 
 overlap to a reference . this procedure would be a particularly useful 
 tool to aid virtual screening as discussed further below . rescoring 
 results for the group derived from fms+sge sampling shows similar 
 results , with fms ( 99.2% ) yielding a significantly higher success 
 rate than sge ( 81.9% ) . the most dramatic changes in terms of 
 pose reproduction involve using sge ( 58.5% ) or fms+sge ( 68.9% ) to 
 rescore the pose ensembles derived from fms - only sampling ( 93.5% ) . these reduced success rates likely stem from the fact that the fms 
 score accounts only for overlap between pharmacophore features derived 
 from the reference ligand structure and the receptor is  invisible  
 during sampling . the end result is that poses generated using fms 
 alone may clash with the target protein when rescored in  energy 
 space  despite high pharmacophore overlap . however , as the 
 pairing of energy and pharmacophore overlap ( fms+sge ) leads to relatively 
 high success rates when rescored in sge - space , as noted above , the 
 combined function is likely to be preferred when a receptor structure 
 is available . nonetheless , the 58% success rate obtained with sge 
 rescoring can be considered encouraging considering that ligand sampling 
 with the anchor - and - grow algorithm was done in the absence of a receptor . thus , for ligand - only based design , the fms protocol appears to be 
 capable of enriching for energetically favorable poses by matching 
 only to a reference pharmacophore . the caveat of course is identifying 
 suitable pharmacophores in the absence of crystallographic information . a protocol designed to enrich for 
 ligands with poses close to a native structure should , in theory , 
 yield favorable scores using any reasonable scoring function . to examine 
 in more detail how properties of molecules generated with one protocol 
 may differ when rescored with another , histograms of the resultant 
 sge and fms scores were plotted using each of the three different 
 pose ensembles obtained with sge , fms , or fms+sge methods . as expected , 
 and consistent with the rescoring results in table 4 , use of the fms function alone to derive poses does lead 
 to overall less favorable dock energies ( figure 8 top , red ) when rescored in sge - space compared to fms+sge ( figure 8 top , green ) or sge ( figure 8 top , blue ) . the large positive peak at 200 kcal / mol ( figure 8 top , red ) represents those systems for which large 
 positive energies were obtained due to geometric clashes occurring 
 between ligand and protein . however , an encouraging number of the 
 poses derived from fms sampling do yield favorable energies . at first 
 glance , the fact that the sge and fms+sge energy histograms ( figure 8 top , blue and green ) are nearly superimposable 
 is somewhat surprising , especially considering the two ensembles yield 
 substantially different success rates ( sge = 72.5% vs fms+sge = 98.3% ) . however , given the underlying complexity of binding energy landscapes , 
 ligand poses with distinctly different binding geometries may in fact 
 yield similar energy scores ( and vice versa ) , thus the observed sge 
 overlap in figure 8 ( top panel ) is not unreasonable . sge ( top ) 
 and fms ( bottom ) score histograms using ensembles derived 
 from sge ( blue ) , fms ( red ) , or fms+sge ( green ) driven sampling methods . as shown in figure 8 ( bottom ) , fms score 
 distributions show much greater separation , indicating greater sensitivity 
 in contrast to the sge score distributions shown in figure 8 ( top ) . here , sge sampled poses yield a much wider 
 almost uniformly distributed range of fms scores ( figure 8 bottom , blue ) compared to fms ( figure 8 bottom , red ) or fms+sge ( figure 8 bottom , green ) sampled poses which have large peaks around 
 0.5 , indicative of high pharmacophore overlap . importantly , the fms+sge 
 combination containing both geometric and energetic components to 
 guide growth yields energy scores on par with standard sge - guided 
 docking poses ( figure 8 top , green vs blue ) 
 and matches the pharmacophore models even better than fms - only docking 
 ( figure 8 bottom , green vs red ) . an additional interesting observation 
 from the results in table 4 is the larger number 
 of final docked poses obtained using fms ( 337674 ) compared to sge 
 ( 89083 ) or fms+sge ( 59237 ) . the much larger ensemble generated with 
 fms corresponds to an increase in total docking time , which could 
 be of concern , although when normalized by the number of poses kept , 
 the fms function is actually faster than sge by about 1.5 fold . current experiments employed a standard dock input file specifying 
 a maximum score cutoff of 100.0 , larger than the upper bound of the 
 fms function [ 0 , 20 ] . thus , poses are not as vigorously pruned during 
 growth compared to protocols that employ energy - based functions ( themselves 
 not bounded ) . the significantly larger ensemble from fms - sampling 
 also likely contributes to the reduction in docking success rate associated 
 with sge rescoring because of the greater number of alternative ( decoy ) 
 poses associated with system . although no score 
 cutoffs were used to define success in the pose 
 reproduction tests in table 4 , if both a rmsd 
 cutoff and score cutoff are defined then the results can be classified 
 in one of four different quadrants ( see figure 9b ) defined as ( i ) true positive ( tp ) , good score and low rmsd ; ( ii ) 
 false positive ( fp ) , good score and high rmsd ; ( iii ) true negative 
 ( tn ) , bad score and high rmsd ; ( iv ) false negative ( fn ) , bad score 
 and low rmsd . to highlight properties of the new dock pharmacophore 
 function , figure 9 focuses on the results derived 
 using only the fms - guided sampling protocol discussed above ( success 
 = 93.5% , sampling failure = 3.7% , scoring failure = 2.8% ) . dashed 
 green lines at rmsd = 2  and fms = 2 delineate the four quadrants . 2d histograms 
 of fms score and rmsd for ( a ) all poses ( n = 239486 ) 
 and ( b ) best scored poses ( n = 1041 ) generated using 
 fms guided sampling of 1043 systems . figure 9a plots the large  all 
 poses  
 set consisting of 239486 ligand conformations with fms < 20 out 
 of the total sampled space obtained with fms sampling ( 337674 poses ) . 
 here , the small separate cluster of points located in the tp region 
 ( lower left quadrant ) , which shows roughly linear correlation with 
 rmsd , corresponds to mostly docking successes compared to the highly 
 populated tn region ( upper right quadrant ) containing many thousands 
 of points for which the correlation between rmsd and fms begins to 
 diverge as fms values increase . unlike the standard sge function , 
 which typically shows little correlation with rmsd , the fms method 
 behaves more like rmsd given the geometric nature of the function . importantly , the results in figure 9a indicate 
 that the fms protocol is not only able to identify close - to - native 
 ligand conformation with favorable scores ( region i ) but also correctly 
 characterizes poses that are geometrically different from the reference 
 by assigning unfavorable scores ( region iii ) . figure 9b plots the  best poses  
 set consisting of 1041 ligand conformations ( 1 system failed to dock , 
 1 system with fms = 20 for the best score pose ) . as in figure 9a , poses in the tp region again show roughly linear 
 correlation with rmsd . in this case , however , as only a single pose 
 for each system is retained , unlike the  all poses  
 case , the tn region is sparse . ideally , a good function should maximize 
 tp and minimize fp . with the present rmsd ( 2.0  ) and fms ( 2 ) 
 cutoffs , 949 points are classified as tp and 26 the remaining points ( 1042  975 = 67 ) are divided into 
 39 tn cases and 28 fp cases . overall , the 97.3% tp rate ( 949/975 ) 
 and 41.8% fp rate ( 28/67 ) indicates good quadrant partitioning . and , 
 as expected , use of a smaller score cutoff will yield a reduction 
 in tp but an improvement in fp . for example , use of an fms cutoff 
 = 1.5 yields a tp rate = 91.4% and a fp rate = 20.9% , and use of an 
 fms cutoff = 1.0 yields a tp rate = 78.1% and fp rate = 9.8% . as a 
 point of comparison , comparable analysis by balius et al . for a similar tp rate = 79.8% yielded a higher 
 fp rate = 46.2% using dock s footprint similarity method with 
 a 0.6 score cutoff ( based on normalized euclidean distance ) and 2 
  rmsd cutoff across 780 protein the optimal choice of a numerical value for score cutoff to employ 
 in a study to yield compounds with the desired properties is system - dependent . for example , in typical virtual screening applications , fms score 
 between candidate compounds and a reference would be expected to be 
 higher ( i.e. , less overlap ) than under the present pose reduction 
 tests which compare compounds with identical topologies but different 
 conformations . while fms in general 
 yields excellent quadrant partitioning , an examination of the results 
 was undertaken to determine the underlying cause of fp and fn classifications . focusing on results from the  best poses  set ( figure 9b ) , figure 10 presents the 
 ten out of twenty - eight fp results ( rmsd > 2  , fms  
 2 ) with the highest rmsd . analogous to that observed with the fms+sge 
 scoring failures ( figure 7 ) , fp poses derived 
 with fms - sampling show , for the most part , remarkably high overlap 
 except for one end of the molecule . and in all ten cases , the poorly 
 overlapped groups contain rings , which are weighted heavier by the 
 rmsd function than fms . 180 
 relative to the reference ( magenta vs orange ) , resulting in overlap 
 between two rings on one end with three rings from the other . although 
 the hungarian algorithm used here in dock to compute symmetry - corrected rmsd effectively accounts for the 
 swap of functionality having identical chemical properties , the resultant 
 value of 3.41  is still classified as a failure , largely as 
 a result of one ring on either end ( 8 atoms total ) not being matched . in contrast , the fms score not only accounts for the symmetry but 
 the good overlap between four out of six ring centers ( and associated 
 vector directions ) , which leads to a relatively low fms score of 1.48 . overall , visual examination of these ten worst fp cases reveals a 
 significant amount of physically reasonable matches and minimal mismatch 
 and the classification of poses to this quadrant is , in most cases , 
 understandable . ten out of twenty - eight fp poses derived from fms - guided 
 docking 
 with the largest rmsd values . rmsd in angstroms and fms scores in parentheses . in terms of the 
 fn examples ( rmsd < 2  , fms > 2 ) , figure 11 presents the ten out of twenty - six poses with the highest 
 fms scores . immediately obvious compared to the fp examples is that 
 the molecules here contain fewer aromatic rings , for the most part 
 are larger and more extended , and have a higher number of more loosely 
 matched hydrogen - bonding functional groups ( most polar atoms in the 
 fp cases are either tightly matched or not matched at all ) . this latter 
 point is particularly important as relatively small changes in position 
 of a hydrogen - bonding functional group can lead to relatively large 
 changes in fms overlap but minor effects on rmsd which is computed 
 using only heavy ( non - hydrogen ) atoms . although our standard preparation 
 protocol for fms scoring employs an energy minimization step to relax 
 any hydrogen atoms added to the system , the positions adopted as a 
 result of ligand sampling during growth may result in the candidate 
 and reference poses having different hydrogen directions . this result 
 highlights the need for care when preparing a molecule to be used 
 as a  reference  for scoring candidate compounds . despite 
 being a distinctly different type of function , a similar conclusion 
 was reached by employing the dock footprint function . despite this sensitivity , however , most of the fn cases 
 have scores close to 2 that could easily be rescued by a minor increase 
 in fms cutoff to 2.5 . ten out of twenty - six fn poses derived from fms - guided 
 docking 
 with the largest fms scores . in addition 
 to pose reproduction , crossdocking experiments are a useful way to 
 determine if different protocols can reproduce nativelike poses when 
 ligands are docked to highly homologous protein binding sites from 
 different crystallographic structures ( see figure 6b ) . figure 12 displays outcomes across 
 six protein families : carbonic anhydrase ( ca , n = 
 29 ) , carboxypeptidase a ( cpa , n = 8) , epidermal growth 
 factor receptor ( egfr , n = 15 ) , thermolysin ( therm , n = 26 ) , hiv protease ( hivpr , n = 60 ) , 
 and hiv reverse transcriptase ( hivrt , n = 21 ) . for 
 comparison , both the diagonal ( cognate protein as before , three 
 docking protocols were tested ( sge , fms , and fms+sge ) . as shown in 
 figure 12a , this is a particularly challenging 
 group of proteins with the standard sge protocol yielding low diagonal 
 successes ( 34.560.0% ) for 5 out of 6 families . the exception 
 is hivrt for which the sge success rate = 95.2% . in contrast , use 
 of fms ( 71.7100.0% ) or fms+sge ( 75.0100.0% ) yields 
 significant improvement for cognate receptor  ligand pairs . carbonic anhydrase is a particularly noteworthy example as the sge 
 diagonal success increases from only 34.5% to near 100.0% using the 
 fms or fms+sge functions . comparable enhancements in success for carbonic 
 anhydrase were also reported by balius et al . when using the dock footprint similarity scoring function ( 82.8% ) 
 compared to sge ( 31.0% ) . crossdocking outcomes averaged across the diagonal 
 ( left ) or total 
 matrix ( right ) for six protein families : carbonic anhydrase ( ca ) , 
 carboxy peptidase a ( cpa ) , epidermal growth factor receptor ( egfr ) , 
 thermolysin ( therm ) , hiv protease ( hivpr ) , and hiv reverse transcriptase 
 ( hivrt ) using sge ( top ) , fms ( middle ) , and fms+sge ( bottom ) protocols . success in blue , scoring failure in green , and sampling failure in 
 red . as expected , for more challenging 
 crossdocking experiments , matrix 
 success ( figure 12b ) using any of the scoring 
 functions are in general significantly lower than their diagonal counterparts 
 ( figure 12a ) . as a baseline , use of sge yields 
 an averaged matrix success of 36.0% compared to the diagonal at 54.6% . in contrast to the diagonal results , interestingly , use of fms alone 
 for crossdocking shows improvement over sge in only two cases ( ca 
 and cpa ) . however , in all cases , the combined fms+sge function always 
 yields a better matrix success than does sge . analogous to the diagonal 
 results , the matrix outcomes ( figure 12b ) similarly 
 reveal that carbonic anhydrase has the lowest overall matrix sge success 
 rate ( 17.8% ) which increased the most among all systems tested when 
 using fms ( 48.8% ) or fms+sge ( 52.1% ) . figure 13 compares the heatmaps for carbonic anhydrase , derived from three 
 independent docking sets of size 29  29 = 841 combinations , 
 using sge , fms , and fms+sge methods . the maps visually highlight that 
 sge failures are primarily due to scoring ( green squares ) , pinpoint 
 which specific systems are involved , and indicate that fms and fms+sge 
 protocols significantly improve docking outcomes ( more blue squares ) . additionally , visible in the fms heatmap for carbonic anhydrase 
 ( figure 13 , middle ) it 
 is important to note that the rmsd calculations in both diagonal and 
 off - diagonal experiments always involve compounds of the same topology . however , for pharmacophore overlap calculations involving off - diagonal 
 elements , the pharmacophore reference and the candidate molecule being 
 docked are usually of different topology . in such cases , fms - guided 
 docking may drive sampling in a direction that will not necessarily 
 agree with the rmsd reference . calculation of the pharmacophore overlap 
 between all aligned crystallographic references for carbonic anhydrase 
 indeed shows 1bcd has the poorest reference fms scores ( between the 
 pharmacophore reference and the rmsd reference ) when averaged across 
 all columns ( fms = 5.15 ) or all rows ( fms = 5.51 ) , which is appreciably 
 above the overall average ( fms = 3.38 ) across all reference pairs . 
 inspection further revealed that the ligand from 1bcd has only one 
 rotatable bond and a molecular weight of 148.1 g / mol , which is markedly 
 smaller than the average ligand in this family with 5.1 rotatable 
 bonds and a molecular weight of 339.7 g / mol . thus , crossdocking of 
 ligands to receptor 1bcd , using fms alone , is not expected to be consistent 
 with the 1bcd reference sampling space , which leads to the observed 
 sampling failures . crossdocking heatmaps using sge , fms , and fms+sge protocols 
 for 
 carbonic anhydrase ( 29  29 = 841 combinations ) . additionally , more dramatic examples of this phenomenon 
 manifest 
 themselves in the heatmaps for thermolysin as shown in figure 14 . here , in contrast to carbonic anhydrase , crossdocking 
 with sge yields a higher overall success rate of 38.2% ( figure 14 left , blue ) but with a higher percentage of sampling 
 failures ( 29.1% , red ) . and , while the combined function fms+sge yields 
 the overall best docking success rate ( 51.0% ) for this family , use 
 of fms alone actually increases sampling failures ( 47.5% ) relative 
 to sge ( figure 14 left vs middle , red ) which , 
 as described below , likely involves poor reference pharmacophore overlap . close inspection of the crossdocking heatmaps reveals submatrices 
 of size 4  4 , defined here as group 1 ( 1pe5 , 1pe7 , 1pe8 , 2tmn ) 
 and group 2 ( 1kjo , 1kl6 , 1ks7 , 1kkk ) , for which fms sampling relative 
 to sge : ( i ) maintains docking success and/or ( ii ) rescues previously 
 unsuccessful docking outcomes involving systems within the same group , 
 and ( iii ) introduces docking failures for systems from different groups . 
 to aid the discussion , figure 15 shows a heatmap 
 of fms scores ( as opposed to docking outcomes ) , derived from the x - ray 
 references , with diagonal and off - diagonal submatrix blocks for groups 
 1 and 2 outlined as black boxes . crossdocking heatmaps using sge , fms , 
 and fms+sge protocols for 
 thermolysin ( 26  26 = 676 combinations ) . ( a ) fms heatmap , using all crystallographic reference poses for 
 thermolysin , with perfect overlap in dark blue ( fms = 0 ) and poorest 
 overlap ( fms  8) in dark red . group 1 submatrix defined by 
 systems 1pe5 , 1pe7 , 1pe8 , and 2tmn . group 2 submatrix defined by systems 
 1kjo , 1kl6 , 1ks7 , and 1kkk . ( b ) crystallographic reference overlays 
 showing matched pharmacophore features for group 1 ( left , orange ) , 
 group 2 ( right , magenta ) , and group 1 vs group 2 ( middle ) . the fms scores computed between all reference pairs 
 indicate perfect 
 overlap on the diagonal ( fms = 0 , dark blue ) , but for the most part 
 the majority of pairs have poor overlap ( fms = 38 , green to 
 dark red ) . a striking exception are the cases defined by groups 1 
 and 2 ( figure 15 , black boxes ) which all have 
 relatively good reference fms scores within the same group ( two blue 
 submatrices near the diagonal ) but poor fms scores between different 
 groups ( two green to yellow submatrices on the off - diagonal ) . this 
 observation helps explain why fms - guided docking yields 100% success 
 across the submatrices formed within the same group ( figure 14 , middle ) , but when using group 1 systems as a 
 reference to guide docking of ligands in group 2 , no matrix success 
 is reported and only 1 success is obtained for the opposite case ( other 
 symmetric block ) . structurally , the molecular cluster formed by ligands 
 in group 1 occupies an extended space in the thermolysin binding pocket 
 ( figure 15b , left ) and contain additional hydrophobic 
 groups compared to group 2 ( figure 15b , right ) . group 2 ligands cluster into a more slender volume anchored by an 
 aromatic ring at one end and hydrogen bond acceptor on the other . 
 as a consequence , groups 1 and 2 share only a few ( 13 ) matched 
 pharmacophore points ( figure 15b , middle ) , 
 which explains the poor fms scores between off - diagonal reference 
 ligands in addition to the poor docking outcomes . interestingly , the 
 addition of the energy term to the pharmacophore overlaps score ( fms+sge 
 score ) , using group 2 as a reference to dock group 1 , yields 100% 
 docking success . in contrast , using group 1 as reference to dock group 
 2 yields 100% sampling failure ( figure 14 , 
 right panel ) . finally , the overall poorest matrix success results 
 using fms ( 7.2% ) 
 or fms+sge although high ligand 
 flexibility is expected to play a role in the large number of sampling 
 failures seen in the fms matrix ( figure 12b , 
 red ) relative to other systems ( 31/60 of ligands have 15 rotatable 
 bonds ) , the most likely cause is poor pharmacophore overlap between 
 all pairwise combinations . consistent with the discussions above , 
 out of the 3600 pairwise combinations in the hivpr crossdocking reference 
 fms matrix derived from crystallographic poses , only 220 pairs yielded 
 reasonable pharmacophore overlap ( fms  3 ) . in contrast , 2493 
 pairs have poor pharmacophore overlap ( fms  4.5 ) which , interestingly 
 in this case , is about the same as the number of sampling failures 
 ( 2816 ) . overall , two key points have emerged from the current 
 crossdocking 
 studies : ( 1 ) fms - guided success rates , particular for off - diagonal 
 elements , are dependent on the similarity between the pharmacophore 
 reference and the rmsd reference . ( 2 ) the fms+sge protocol generally 
 improves crossdocking performance , relative to sge or fms , by integrating 
 known binding profiles into the standard dock energy score . results for enrichment 
 experiments , used to gauge how dock would perform in a virtual screening 
 using sge , fms , or fms+sge protocols are shown in figure 16 and table 5 . receiver operating 
 characteristic ( roc ) curves and area under the curve ( auc ) analyses 
 were used to compute fold enrichment ( fe = auccurve / aucrandom ) values for docking active and decoy ligands taken from 
 the dud - e database . for virtual screening 
 applications , good early enrichment is considered to be critically 
 important , thus fe was also computed at 0.1% , 1% , and 10% of the ranked 
 database . for the current tests , the overall shape of the roc curves 
 vary from essentially perfect enrichment ( 1njs ) to random enrichment 
 ( 1c8k ) with most systems exhibiting good overall enrichment but with 
 a visible dependence on which of the three docking functions was used . 
 for the majority of systems , depending on which roc region is examined , 
 fms ( red curves ) shows higher enrichment than sge ( blue curves ) , with 
 fms+sge ( green curves ) being roughly in between ( figure 16 ) . across different ranges of the database , based 
 on numerical auc values , use of fms or fms+sge for example , at 0.1% of the database , 11/15 fe values using fms and 
 11/15 fe values using fms+sge are enhanced relative to sge ( table 5 , column a ) . similarly , at 1% of the database , 10/15 
 fe values using fms and 13/15 fe values using fms+sge are enhanced 
 relative to sge ( table 5 , column b ) . the fact 
 that use of fms+sge yields generally lower enrichment outcomes than 
 fms is somewhat surprising given that fms+sge yielded higher success 
 rates than fms in pose reproduction experiments . however , it is important 
 to note that the role of the sge term in fms+sge is fundamentally 
 different for pose reproduction given that different molecular conformers , 
 as opposed to the different chemical species for enrichment , are what 
 is rank - ordered . the most likely contributing factor as to why fms 
 scoring yields enhanced enrichment involves the fact that use of a 
 crystallographic reference captures elements of what is important 
 for activity for at least one active ligand . because rank - ordering 
 of  actives  using fms scoring are biased toward the 
 known binder , higher enrichments can be obtained . with the addition 
 of the sge term , sampling and rank - ordering using fms+sge will change 
 as a result of , for example mw bias , which leads to different enrichment 
 results ( less - favorable in most cases for the present tests ) . overall , 
 the enrichment tests validate the ability of fms and fms+sge protocols 
 to enrich for true actives relative to sge alone by prioritizing molecules 
 with similar binding profiles as a known ligand . this strongly suggests 
 use of a pharmacophore reference to help guide virtual screening and 
 is a viable alternative to the standard dock protocol . roc enrichment 
 curves for 15 dud - e systems using sge , fms , and 
 fms+sge protocol . pdb codes used with accompanying 
 dud - e libraries ( actives + decoys ) . fe = auccurve / aucrandom thus baseline 
 random selection always yields a fe = 
 1.00 . as an additional point , 
 in general , good enrichment should depend 
 only on actives being ranked earlier than decoys without regards to 
 there being  similarity  among groups of compounds . however , use of the fms function might be expected to yield higher 
 early similarity , compared to the entire set of actives as a whole , 
 provided the composition of active molecules in a given database does 
 contain subsets with 2d similarity , and a larger than average number 
 of docked compounds yield good 3d overlap with the reference pharmacophore . to explore this issue , among rank - ordered active compounds , we computed 
 all possible pairwise tanimoto coefficients using the dock fingerprinting 
 method motivated by the molprint algorithm and plotted the data as heatmaps ( figure 17 ) . while additional studies should be pursued , especially those 
 employing 
 more than one reference per system as was done in the current study , 
 figure 17 reveals that in a number of cases , 
 active molecules do in fact appear to have higher similarity earlier 
 in rank - ordered list when using fms versus sge scoring ( figure 17 , red / yellow vs blue , top vs bottom rows ) . rank - ordering 
 with fms also shows a tendency to cluster similar molecules together . particularly interesting examples include 1sjo , 1uyg , and 1l2s for 
 which sge shows poor ( random in two cases ) enrichment compared to 
 fms as gauged by the shape of the roc curves in figure 16 . pairwise tanimoto heatmap for 15 dud - e systems using fms ( top ) 
 and sge ( bottom ) protocol . the color scheme in the heatmap represents 
 the magnitude of tanimoto similarity , and the x / y axis represents the rank - ordered list ( fms or sge ) of 
 unique active molecules for each system . to further gauge the utility of using fms methods , we rescored 
 virtual screening results for three systems being targeted in our 
 laboratory : epidermal growth factor ( egfr ) , insulin - like growth factor 1 receptor ( igf-1r ) , and human immunodeficiency 
 virus glycoprotein 41 ( hivgp41 ) and visually examined 
 the number of pharmacophore matches for top - ranked molecules under 
 different conditions ( figure 18 ) . the fms references 
 employed for egfr ( erlotinib ) and igf-1r ( isoquinolinedione analog ) 
 were based on known small molecule inhibitors , while the hivgp41 reference 
 was based on four key amino acid side chains ( wwdi ) from a known peptide 
 inhibitor . the receptors and accompanying references were derived 
 from crystallographic structures ( pdb codes 1m17 , 2zm3 , and 1aik , respectively ) , 
 and the molecules docked to each target were taken from the publically 
 available zinc collection of purchasable 
 organic compounds . for each screen , the top 100000 ranked compounds 
 obtained with the standard docking protocol ( grid score with flx protocol ) 
 were retained and then rescored and reranked using dock cartesian 
 energy ( dce , which is comparable to sge but in cartesian space ) , fms , 
 and fms+dce scoring protocols . references ( orange sticks , gray surface ) 
 used to rescore virtual 
 screening results targeting ( a ) egfr , ( b ) igf-1r , ( c ) hivgp41 , and 
 ( d ) hivgp41 with asp side chain weighted 5 times . matched pharmacophore 
 features include : pho in cyan , hba ( vertex and vector ) in red , hbd 
 vector in blue , hydrogen vertex in gray , aro ( vertex and vector ) in 
 orange , pos in magenta , and neg in green ( see theoretical 
 methods for definitions ) . as shown in figure 18 , the number 
 of pharmacophores 
 matched for the top 25 ranked compounds is relatively small using 
 dce . in sharp contrast , use of fms or fms+dce show , for example , many 
 more matched hbd ( blue arrows ) , hba ( red arrows ) , aro ( orange arrows ) , 
 and pho ( cyan spheres ) features . it is important to note that the 
 plots in figure 18 show how many  matched pharmacophores were obtained , relative to the reference , but candidate 
 compounds can contain unmatched features  that extend 
 beyond the volume defined by the reference compound ; the functional 
 form of eq 1 does not necessarily penalize unmatched 
 features relative to the candidate . this behavior could be changed , 
 for example , by including simply a penalty term based on the number 
 of unmatched groups in the candidate ; however , this was not explored 
 in great detail . the number of matched 
 and unmatched features , including types , for each docked pose , are 
 printed to the dock output , which can be useful to determine whether 
 particular characteristics have been satisfied . as a specific 
 example , an interesting result from the present analysis 
 is a lack of matched pharmacophore features to the asp carboxylate 
 group in the hivgp41 reference ( figure 18 row 
 c ) . an examination of ranked poses higher up the fms and fms+dce lists 
 did indeed reveal compounds with overlap to the reference carboxylate 
 but they were not ranked as well as compounds with multiple matches 
 involving two trp indole rings and a hydrophobic ile ( figure 18 , row c ) . given the biological importance of the 
 asp group in this system , an effective small molecule mimic would 
 reasonably be expected to contain a negatively charged or hydrogen - bonding 
 group at this position . a straightforward way 
 to enforce this requirement was devised by using a modified hivgp41 
 reference that simply included 5 copies of the asp carboxylate which 
 had the effect of weighting this feature more heavily , as shown in 
 figure 18 , row d. for this particular test , 
 weighting the asp more highly had the desired effect but at the expense 
 of losing hydrophobic matches to ile ( figure 18 , fms and fms+dce , row c vs d ) . as a general point , this example 
 demonstrates the ease with which specific pharmacophore features can 
 be emphasized over others using the current dock infrastructure . finally , in terms of additional ligand properties , figure 19 plots results from the hivgp41 screen for different 
 groups of top - ranked molecules ( n = 500 ) each obtained 
 by one of the ranking protocols . consistent with previous studies 
 from our laboratory , use of dce ( or sge ) shows a bias toward larger 
 molecules . in contrast , compounds ranked by fms score are smaller 
 in size as demonstrated by ligands with lower molecular weights ( figure 19d ) and fewer numbers of rotatable bonds ( figure 19e ) . as anticipated , use of fms+dce yields molecular 
 weights and numbers of rotatable bonds roughly in - between dce and 
 fms . for scoring , use of dce results in more favorable dce energies 
 ( figure 19a , blue vs red or green ) , fms results 
 in more favorable fms scores ( figure 19b , red 
 vs blue or green ) , and fms+dce results in more favorable fms+dce scores 
 ( figure 19c , green vs blue or red ) . and , rescoring 
 molecules obtained with one function with another function leads to 
 the expected results . for example , dce score distributions for top - ranked 
 fms+dce molecules are in between that of dce and fms ( figure 19a green ) , fms score distributions for top - ranked 
 fms+dce molecules are in between that of fms and dce ( figure 19b green ) , and fms+dce score distributions for top - ranked 
 fms molecules are in between that of fms+dce and dce ( figure 19c red ) . importantly , use of the combined fms+dce 
 function to rescore virtual screening results yield both favorable 
 fms scores and dock energies . this suggests use of a reference to 
 rescore screening results could also be a viable way to identify compounds 
 that make known interaction patterns , with favorable interaction energies , 
 while reducing molecular weight bias . histograms of rescoring 
 results for the top 500 molecules selected 
 from virtual screening targeting hivgp41 . in conclusion , the primary 
 goal of this study was to develop , implement , 
 and thoroughly test a pharmacophore - based scoring function for the 
 docking program dock . the resulting method , termed pharmacophore matching 
 similarity ( fms ) score , was validated using experiments that help 
 gauge accuracy relative to the standard dock single - energy grid ( sge ) 
 protocol , and the combination score fms+sge . three groups of validation 
 experiments were performed : ( i ) pose reproduction ( figures 711 and table 4 ) , ( ii ) crossdocking ( figures 1215 ) , and ( iii ) enrichment ( figure 16 and table 5 ) . importantly , 
 in terms of pose reproduction , use of fms ( 93.5% ) or fms+sge ( 98.3% ) 
 functions yielded significantly higher success rates than the standard 
 sge ( 72.5% ) method when evaluated using 1043 systems in the sb2012 
 testset . the nearly perfect success rate obtained with the combined 
 fms+sge function , which biases sampling to match a reference while 
 simultaneously including energetic constraints imposed by a binding 
 site , is notable and strongly suggests the method will have applicability 
 for structure - based drug design provided a  suitable tests using fms alone for pose reproduction 
 showed relatively few ligand poses falling into false positive ( fp ) 
 and false negative ( fn ) regions defined by quadrant partition using 
 specific rmsd and fms score cutoff criteria ( figure 9 ) . interestingly , visual examination of the worst fp cases 
 ( figure 10 ) revealed , in most instances , that 
 the candidate and references poses were in fact well - overlaid and 
 that only one part of the molecule was not well - matched . unlike the 
 standard dock energy function , the geometry - based fms scores show 
 reasonable correlation with rmsd . for crossdocking , while use 
 of fms scoring alone showed significant 
 improvement with regards to systems on the diagonal ( cognate protein  ligand 
 pairs ) , the overall matrix success rate in 4 out of 6 cases was significantly 
 lower than sge . examination of the underlying reference structures 
 showed that fms docking success is highly dependent on how well the 
 pharmacophore reference overlays with the rmsd reference ( figures 1215 ) . thus , while 
 use of fms scoring alone to drive sampling of a ligand using a reference 
 without possibilities for good overlap yields poor results , such behavior 
 makes physical sense . more importantly , the results dramatically emphasize 
 that the fms function works best when the goal is identification of 
 molecules that resemble the reference , as was the original intent . as expected , use of the combined fms+sge function provides more of 
 a balance and yields the highest crossdocking matrix success rates 
 ( figure 12 ) . in terms of enrichment , 
 receiver operator characteristic ( roc ) , 
 area under the curve ( auc ) , and fold enrichment ( fe ) analyses , in 
 general , showed that fms and fms+sge functions yield better performance 
 than sge alone ( and random selection ) for both early and total enrichment 
 ( figure 16 and table 5 ) when evaluated over 15 systems taken from the dud - e database . for 
 several systems , fms+sge enrichment appears roughly in between that 
 obtained using fms or sge alone ( figure 16 ) . importantly , fe values computed very early in rank - ordered lists ( 0.1% 
 and 1% ) showed using fms and fms+sge yielded 1013 out of 15 
 fe values enhanced relative to the standard protocol sge ( table 5 , columns a and b ) despite the fact that only a 
  single  reference ( cognate ligand ) was used to guide 
 sampling of compounds . future studies should evaluate enrichment outcomes 
 using multiple fms references . in terms of virtual screening , 
 rescoring results obtained from 
 standard docking to three target of pharmaceutical interest ( egfr , 
 igf-1r , and hivgp41 ) showed that the fms and fms+dce ( equivalent to 
 fms+sge ) methods yielded more compounds with greater numbers of pharmacophore 
 matches when the top 25 compounds from each method were examined ( figure 18 ) . the example also demonstrated how fms scoring 
 could utilize small organic molecules or noncontiguous protein side 
 chains as a reference . for gp41 in particular , examination of top 
 poses revealed that none of the compounds matched an important asp 
 side chain in the initial pharmacophore model . a simple modification 
 of the reference to include multiple copies of the asp weighted this 
 functionality more highly , and when rescored , yielded top - ranked compounds 
 with the desired interaction . importantly , this result further establishes 
 the importance of the fms  reference  in addition to 
 demonstrating how pharmacophores could be customized . finally , 
 the current results suggest several directions for future 
 research , including exploring other functional forms of the main fms 
 equation ( eq 1 ) , testing fms score in combination 
 with other scoring functions ( i.e. , footprint similar scoring ) , development 
 of a receptor - based as opposed to the 
 current ligand - based method , and implementation of routines to address 
 multiple pharmacophore references simultaneously . ongoing work involves incorporation of fms scoring into 
 a de novo design version of dock , currently under 
 development in our laboratory , to allow pharmacophore - guided denovo growth of new ligands from scratch 
 having similar binding profiles as a known reference .
pharmacophore modeling incorporates 
 geometric and chemical features 
 of known inhibitors and/or targeted binding sites to rationally identify 
 and design new drug leads . in this study , we have encoded a three - dimensional 
 pharmacophore matching similarity ( fms ) scoring function into the 
 structure - based design program dock . 
 validation and characterization 
 of the method are presented through pose reproduction , crossdocking , 
 and enrichment studies . when used alone 
 , fms scoring dramatically 
 improves pose reproduction success to 93.5% ( 20% increase ) 
 and reduces sampling failures to 3.7% ( 6% drop ) compared to 
 the standard energy score ( sge ) across 1043 protein 
 ligand 
 complexes . 
 the combined fms+sge function further improves success 
 to 98.3% . crossdocking 
 experiments using fms and fms+sge scoring , 
 for six diverse protein families , similarly showed improvements in 
 success , provided proper pharmacophore references are employed . for 
 enrichment , incorporating pharmacophores during sampling and scoring , 
 in most cases , also yield improved outcomes when docking and rank - ordering 
 libraries of known actives and decoys to 15 systems . 
 retrospective 
 analyses of virtual screenings to three clinical drug targets ( egfr , 
 igf-1r , and hivgp41 ) using x - ray structures of known inhibitors as 
 pharmacophore references are also reported , including a customized 
 fms scoring protocol to bias on selected regions in the reference . 
 overall , the results and fundamental insights gained from this study 
 should benefit the docking community in general , particularly researchers 
 using the new fms method to guide computational drug discovery with 
 dock .