a pharmacophore is the spatial arrangement of features that enables a molecule to interact with a target receptor in a specific binding mode .
once identified , a pharmacophore can serve as a powerful model in versatile applications for rational drug design , such as virtual screening , de novo design , lead optimization and adme / tox studies ( 1 ) .
this is a new field for systematically studying the effect of a large number of drug - like molecules on a biological network of macromolecular targets .
pharmacophores of target macromolecules in a network can be used to explain and predict the biological effect of new drug - like ligands .
indeed , a large - scale identification of pharmacophores for many biological macromolecules is becoming more and more feasible due to the expanding number of databases of drug - like ligands with known interacting receptors .
some methods , which are called the direct methods , use both ligand and receptor information
. however , in most cases the structure of the target receptor is unknown and the only available information is a set of ligands that have been observed to interact with the receptor . in such cases ,
only the second type of methods , the indirect methods , are applicable , since they are based on ligand information alone . given a set of drug - like molecules that are known to interact with a specific target receptor ,
the aim of the indirect methods is to find the largest ( or highest scoring ) 3d pattern of features responsible for binding that is shared by all or most of the input ligands .
the task is challenging due to the hardness of the problem with respect to both the number of input molecules and the flexibility of drug - like molecules . specifically , aligning drug - like molecules is np hard even in the case of three rigid molecules ( 5,6 ) and in the case of a pair of molecules , where one is rigid and the other one is flexible ( 7 ) .
existing indirect methods for pharmacophore detection mainly differ in their approach for addressing the flexibility of a drug - like molecule .
a drug - like molecule may possess several rotatable bonds and as a result it may have many potentially feasible conformations . the specific conformations that the input ligands adopt when interacting with the receptor are unknown . therefore
specifically , a discrete set of conformations is generated for each input ligand with the aim of sampling all its feasible conformations .
examples for such methods include rapid ( 8) , mphil ( 9 ) and the commercial programs disco ( 10 ) , phase ( 11 ) of schrodinger and hiphop and hypogen , which are part of the catalyst software package of accelrys ( 1216 ) . the main limitation of this approach
is that covering the whole conformational space might require an extremely large number of conformations , especially for molecules with many rotatable bonds .
an alternative approach is to incorporate the conformational search within the pattern identification process . this way the search space
however , the current methods that adopt this approach are based on random search . among them
are scampi ( 17 ) , gamma ( 1820 ) , gasp ( 21,22 ) and galahad ( 23,24 ) of tripos and the moga - based method described in ref .
another approach for treating the flexibility of the ligands within the pattern detection process is employed by the mtree method of biosolveit , which aligns feature - trees derived from the 2d structures of the ligands ( 26 ) .
herein , we present pharmagist , the first webserver for elucidating 3d pharmacophores from a set of drug - like molecules that are known to bind to a target receptor .
the method efficiently searches for possible pharmacophores and reports the highest - scoring ones ( 7 ) .
the candidate pharmacophores are detected by multiple flexible alignment of the input ligands , where the flexibility of the ligands is treated explicitly and in a deterministic manner in the alignment process .
another key advantage of the method is the ability to detect pharmacophores common to subsets of input ligands , a characteristic that makes pharmagist tolerant to outliers and to several binding modes .
the whole dataset consists of almost 80 crystal structures of receptor  ligand complexes taken mainly from the flexs benchmark dataset ( 27 ) .
the complexes are classified into 12 different cases , where each case includes complexes of the same receptor with different ligands . for validation
this alignment was derived from a superposition of the receptor in the different complexes . in all cases ,
the runtime ranges from seconds to a few minutes on a typical set of ligands .
below , we briefly describe the method followed by a comprehensive description of the webserver . the interface of the server is user friendly with only two mandatory input fields .
the output provides a convenient browsing over the suggested pharmacophores and an interactive visualization using jmol ( 28 ) .
the output is a list of candidate pharmacophores . these are 3d patterns of physico - chemical features that are shared by all or some input ligands .
in addition , the output provides for each candidate pharmacophore a 3d superposition of conformations of input ligands that share it .
generally , the algorithm suggests solving the task by multiple flexible alignment of drug - like molecules .
however , due to the hardness of the problem , a heuristic solution is provided in practice .
in addition , the algorithm assumes that one of the input ligands is given in a conformation that is not very different from the bound state .
this ligand serves as a pivot on which the other target ligands are aligned . by default ,
alternatively , the pivot can be defined by the user as the ligand with the highest affinity to the receptor or the one with the lowest number of rotatable bonds .
also , in case of a highly flexible pivot , it is possible to use a precomputed set of conformations .
the method consists of four major stages : ( i ) ligand representation , ( ii ) pairwise alignment , ( iii ) multiple alignment and ( iv ) solution clustering and output ( figure 1 ) . in the first stage , each input ligand is processed separately .
the method detects the rotatable bonds of the ligand and divides it into rigid groups accordingly .
in addition , the ligand is assigned with a set of physico - chemical features ( hydrogen bond donor / acceptor , anion / cation , aromatic ring , hydrophobic group or , optionally , other features defined by the user ) . in the second stage ,
given a pivot ( treated as rigid ) and one target ligand ( treated as flexible ) , pairwise alignments are computed as follows .
first , for each rigid group of the target ligand , the method generates a set of transformations for superimposing the target rigid group onto the pivot .
the result for each target rigid group is a set of candidate new poses on the pivot .
then , these poses are reassembled into new conformations of the target ligand aligned on the pivot .
the score of a resulting pairwise alignment is a weighted sum of the matched pivot features .
two features , one from the pivot and one from a conformation of the target ligand , can be matched if they are of the same type and the distance between them is below a predefined threshold ( 1  by default ) .
the algorithm uses default weight values for each feature type ( 0.3 for hydrophobicity and 1 for the rest ) , unless other values are supplied by the user .
the output of the stage is a large number of high - scoring pairwise alignments between the pivot and the target ligand .
the goal is to find significant subsets of pivot features that are matched by as many pairwise alignments for different target ligands as possible .
however , maximizing the number of aligned ligands can be contradictory to maximizing the score of the matched features .
thus , the method produces multiple alignments for each subset size of input ligands . due to efficiency considerations ,
this is achieved by enumerating all the possible subsets of pivot features and selecting the ones that can be aligned by as many ligands as possible .
subsets of pivot features with a significant score are candidate pharmacophores and will be reported to the user . by default
, a candidate pharmacophore must consist of at least three spatially distinct features , but this parameter is user - defined . in the last fourth stage ,
candidate pharmacophores derived from different pivot iterations are clustered and the highest scoring non - redundant ones for each number of molecules are reported . 
 figure 1.pharmagist method flow .
its input form has only two mandatory fields : a file with input ligands and an email address . after submitting the form ,
when the run completes , a link to a web page with candidate pharmacophores is sent to the user by an email .
the basic input form is simple with two mandatory fields : a file with drug - like molecules in mol2 format and an email address .
currently , the user may upload up to 32 molecules , either in a single mol2 file or in one zip file compressing multiple mol2 files .
the basic form has an additional field for setting the number of candidate pharmacophores to be reported for each subset size of input molecules ( 10 by default ) .
advanced options part of the form . by default , these parameters are hidden and setting them is optional .
users are encouraged to change these parameters if additional data on the specific - binding site of the target receptor are known .
the advanced options include selecting a key molecule to serve as a pivot and defining the minimal number of spatially distinct features in a pharmacophore .
for example , if the interaction with the target receptor is mostly hydrophobic , the scoring weight assigned to hydrophobic features can be increased .
finally , the server allows users to define their own feature types with assigned scoring weights .
this option is useful when it is known that a specific group is essential for an interaction with the receptor .
an additional explanation on each field is available in the help page , which can be directly accessed by clicking on the name of the field
once the run completes , a link to a web page with an access to the results is sent to the user via an email .
the results are stored on the server for at least a month , so the user can view the results later again using the same link .
the upper table lists the input molecules with their number of atoms and assigned physico - chemical features . at the top of the table , there is link to a web page that displays the molecules with their features in a jmol applet ( 28 ) .
each table presents the highest scoring candidate pharmacophores shared by a specific number of molecules .
note that at the top of the result page there is a link to a page in which all the candidate pharmacophores are sorted by their score , regardless of the number of ligands that share them . at the bottom of the result page
, there is a link to a page that displays the best flexible alignment for each pair of input molecules . in the result tables ,
the following data are presented for each candidate pharmacophore : ( i ) the score , ( ii ) the names of the aligned molecules , ( iii ) the number of common features and their type distribution and ( iv ) a link to a page that displays the candidate pharmacophore . 

the figure displays parts of the main output page obtained for an input with seven elastase inhibitors .
note that the link to the page that displays the best flexible alignment for each pair of input molecules is not shown due to space limitation .
the figure displays parts of the main output page obtained for an input with seven elastase inhibitors .
note that the link to the page that displays the best flexible alignment for each pair of input molecules is not shown due to space limitation .
specifically , the page describes the top scoring candidate pharmacophore shared by all seven elastase inhibitors .
the page supplies a 3d visualization of the pharmacophore and the multiple flexible alignment that it is based on .
the page consists of three main parts : ( i ) a summary of the attributes of the pharmacophore at the top , ( ii ) a jmol display on the right and ( iii ) a display control panel on the left .
the summary part contains the score of the candidate pharmacophore , its number of features and their physico - chemical types . by default
, the jmol display shows the pharmacophore ( spacefill model ) and the multiple flexible alignment that yields it ( wireframe model ) .
the color coding of the features in the display is by their physico - chemical types and it is as in the summary part of the page at the top .
each molecule is depicted in a different color as it appears in the jmol display .
the first checkbox allows the user to show / hide the controlled molecule in the display and the second checkbox controls the display of the features of the molecule .
in addition , the user can show / hide the pharmacophore in the display using the checkbox at the top of the summary part . at the bottom of the page
, there is a link for downloading a mol2 file with the candidate pharmacophore and the corresponding multiple flexible alignment . 

the basic input form is simple with two mandatory fields : a file with drug - like molecules in mol2 format and an email address .
currently , the user may upload up to 32 molecules , either in a single mol2 file or in one zip file compressing multiple mol2 files .
the basic form has an additional field for setting the number of candidate pharmacophores to be reported for each subset size of input molecules ( 10 by default ) .
advanced options part of the form . by default , these parameters are hidden and setting them is optional .
users are encouraged to change these parameters if additional data on the specific - binding site of the target receptor are known .
the advanced options include selecting a key molecule to serve as a pivot and defining the minimal number of spatially distinct features in a pharmacophore .
for example , if the interaction with the target receptor is mostly hydrophobic , the scoring weight assigned to hydrophobic features can be increased .
finally , the server allows users to define their own feature types with assigned scoring weights .
this option is useful when it is known that a specific group is essential for an interaction with the receptor .
an additional explanation on each field is available in the help page , which can be directly accessed by clicking on the name of the field .
once the run completes , a link to a web page with an access to the results is sent to the user via an email .
the results are stored on the server for at least a month , so the user can view the results later again using the same link .
the upper table lists the input molecules with their number of atoms and assigned physico - chemical features . at the top of the table
, there is link to a web page that displays the molecules with their features in a jmol applet ( 28 ) .
each table presents the highest scoring candidate pharmacophores shared by a specific number of molecules .
note that at the top of the result page there is a link to a page in which all the candidate pharmacophores are sorted by their score , regardless of the number of ligands that share them . at the bottom of the result page
, there is a link to a page that displays the best flexible alignment for each pair of input molecules . in the result tables ,
the following data are presented for each candidate pharmacophore : ( i ) the score , ( ii ) the names of the aligned molecules , ( iii ) the number of common features and their type distribution and ( iv ) a link to a page that displays the candidate pharmacophore . 

the figure displays parts of the main output page obtained for an input with seven elastase inhibitors .
note that the link to the page that displays the best flexible alignment for each pair of input molecules is not shown due to space limitation .
the figure displays parts of the main output page obtained for an input with seven elastase inhibitors .
note that the link to the page that displays the best flexible alignment for each pair of input molecules is not shown due to space limitation .
specifically , the page describes the top scoring candidate pharmacophore shared by all seven elastase inhibitors .
the page supplies a 3d visualization of the pharmacophore and the multiple flexible alignment that it is based on .
the page consists of three main parts : ( i ) a summary of the attributes of the pharmacophore at the top , ( ii ) a jmol display on the right and ( iii ) a display control panel on the left .
the summary part contains the score of the candidate pharmacophore , its number of features and their physico - chemical types . by default ,
the jmol display shows the pharmacophore ( spacefill model ) and the multiple flexible alignment that yields it ( wireframe model ) .
the color coding of the features in the display is by their physico - chemical types and it is as in the summary part of the page at the top .
each molecule is depicted in a different color as it appears in the jmol display .
the first checkbox allows the user to show / hide the controlled molecule in the display and the second checkbox controls the display of the features of the molecule .
in addition , the user can show / hide the pharmacophore in the display using the checkbox at the top of the summary part . at the bottom of the page
, there is a link for downloading a mol2 file with the candidate pharmacophore and the corresponding multiple flexible alignment . 

we have presented the first webserver for detecting 3d pharmacophores shared by known active ligands in the absence of structural information on the target receptor .
the deterministic and efficient algorithm behind the server allows a fast and reliable detection of pharmacophores with explicit consideration of the flexibility of the ligands .
a key characteristic of the server is the ability to detect outlier molecules as well as to find pharmacophores common only to subsets of input ligands .
the web interface is simple and easy to use . on the one hand , the algorithm is fully automated and the user is only required to upload the input ligands . on the other hand ,
the server provides advanced customization options , such as defining new features or setting the scoring weight of the features .
the pharmacophores found by the server can be further used for generating new leads or for optimizing existing ones in a drug design process .
 predicting molecular interactions is a major goal in rational drug design . 
 pharmacophore , which is the spatial arrangement of features that is essential for a molecule to interact with a specific target receptor , is an important model for achieving this goal . 
 we present a freely available web server , named pharmagist , for pharmacophore detection . 
 the employed method is ligand based . 
 namely , it does not require the structure of the target receptor . 
 instead , the input is a set of structures of drug - like molecules that are known to bind to the receptor . 
 the output consists of candidate pharmacophores that are computed by multiple flexible alignment of the input ligands . 
 the method handles the flexibility of the input ligands explicitly and in deterministic manner within the alignment process . 
 pharmagist is also highly efficient , where a typical run with up to 32 drug - like molecules takes seconds to a few minutes on a stardard pc . 
 another important characteristic is the capability of detecting pharmacophores shared by different subsets of input molecules . 
 this capability is a key advantage when the ligands belong to different binding modes or when the input contains outliers . 
 the webserver has a user - friendly interface available at http://bioinfo3d.cs.tau.ac.il/pharmagist . 
INTRODUCTION
METHOD OVERVIEW
WEBSERVER
Input
Output
SUMMARY