in the past few years , 
 polypharmacology has been recognized as a new avenue for drug discovery 
 and development . numerous drugs such as aspirin , topiramate , and especially kinase inhibitors are known for 
 their multitarget - directed activities . along the same lines , drug 
 repurposing / repositioning , which aims to discover new indications 
 for existing approved drugs , has emerged as a critical cost - effective 
 and time - efficient strategy for drug development . more importantly , the enormous amount of molecular data generated 
 in the postgenomic era will significantly accelerate such polypharmacological 
 research . rational design of multitargeting drugs can be challenging 
 with the current drug discovery strategies . there have been several promising attempts , and various 
 methods were developed for associating drugs with their possible 
 unknown off - targets . for instance , campillos et al . mapped drugs - targets 
 based on their phenotypic side - effect similarities . the shoichet group developed similarity ensemble approach 
 ( sea ) to relate targets based on the 
 set - wise chemical similarity with their ligands , and it was also applied 
 to a large - scale prediction of drug activity on side - effect targets . several other groups used knowledge - based approaches to identify 
 associations among various biomolecules stored in their databases . recently text mining techniques were also employed to extract ligand - target - disease 
 mapping information from the literature and public databases . of course , as the most straightforward methodology in structure - based 
 design , inverse docking has long been used to identify potential targets 
 for a given ligand . additionally , systems biology / pharmacology 
 approaches have gained more attention recently by integrating experimental 
 and computational approaches to understand drug mechanisms of actions 
 at the systems - level . during the past decade , numerous databases have been developed such as drugbank , stitch , supertarget , iuphar - db , wombat , pubchem s bioassay database , chembl , and so 
 on , which integrate diverse information on molecular pathways , drug 
 targets , crystal structures , etc . there are also a number of small 
 molecule - centric databases including zinc , pubchem , ligand expo , etc . these databases are comprised of enormous information 
 about their disease relevance , chemical properties , and biological 
 activities . therefore , they could be potentially used for off - target 
 identifications . however , deriving accurate multitargeting information 
 from these databases is not trivial , and , to date , a dedicated , focused 
 polypharmacological database is yet to be developed . herein 
 we showcase our implementation of a novel , dedicated database for 
 a unique set of polypharmacological ligands with high - quality , experimentally 
 validated structural and biological activity data . the data was integrated 
 from multiple resources including the following : ligandexpo ( formerly known as liganddepot ) , protein data 
 bank ( pdb ) , universal protein resource 
 ( uniprot ) , and drugbank . a variety of ligand - protein binding databases such as pdbbind , bindingdb , and 
 binding moad are also taken into consideration 
 in order to extract the available ligand binding affinities . additionally , 
 literature reports were also mined to obtain as much biological data 
 as possible . by integrating these resources , we have built a novel 
 database , termed polypharma , specifically for multitargeting 
 ligands , along with their modulated targets and quantitative bioactivities 
 ( e.g. , binding affinity ) , in particular for polypharmacology modeling . 
 to date , polypharma includes 953 ligands that are 
 complexed with two or more protein structures belonging to distinct 
 target families . we also provide other information such as molecular 
 properties of ligands and their targets . a set of query functions 
 has been implemented to search our database , and molecular networks 
 can be constructed to depict ligand - target interactions . the polypharma database consists 
 of a unique set of multitarget - directed ligands with their high resolution 
 crystal structures and available binding affinities . these data will 
 provide new insights for off - target identification and polypharmacological 
 agent design . the curation was started by obtaining 
 ligand data from ligand expo , and their 
 interactions with targets were analyzed based on their crystal structures 
 in the pdb . as of march 10 , 2013 , the ligand expo contained 15,952 
 small molecules which were included in 88,714 unique pdb structures . 
 to obtain information on the ligands such as their names , chemical 
 structures , and so on , the mmcif format dictionary was downloaded 
 from ligand expo and analyzed with an in - house program . to make it 
 more applicable for rational drug design , the  filter  
 module of the openeye scientific software was used to keep only the 
 drug - like ligands . to this end , the typical lipinski s rule 
 of five along with other filtering parameters 
 were applied ( table s1 ) . finally , several programs were implemented to automatically 
 identify those ligands complexed with more than one protein . this 
 led to 1,674 ligands corresponding to a total of 9,382 unique protein 
 structures ( pdb ids ) . scheme of polypharma database curation . during the curation we frequently 
 observed that a ligand can be included in multiple pdb entries which 
 are actually of the same protein . for instance , the drug alitretinoin 
 is complexed with 1fm6 , 1fm9 , and 1k74 , but all belong 
 to the ppar- protein ( in a heterodimer with rxr- ) , and 
 hence alitretinoin should not be included in polypharma . to remove cases like alitretinoin , we consider only those ligands 
 complexed with multiple proteins belonging to different families . 
 to this end , we first referred to uniprot identities attempting to 
 obtain unique proteins with an assumption that a protein structure 
 with a unique uniprot identity would represent a unique target . using 
 our in - house tools , the pdb ids were mapped to the uniprot identities 
 as annotated in the uniprot database ( accessed on march 10 , 2013 ) . first , not 
 all pdb ids are associated with uniprot ids . out of 4,167 pdb ids second , in some cases , 
 the same proteins have different uniprot ids . for example , hiv-1 protease 
 complexed with the drug darunavir has crystal structures of 3ttp and 3s53 , but they have different 
 uniprot ids as p03367 and q7ssi0 , respectively . the reason is that 
 p03367 corresponds to the gag - pol gene , whereas q7ssi0 
 corresponds to the pol gene . third , sometimes one 
 pdb i d can correspond to multiple uniprot ids such as 3o3a which is 
 for human class i mhc hla - a2 in complex with the peptidomimetic ela-1 
 protein with two uniprot ids p01892 and p61769 . the simple lesson 
 learned from this unsuccessful attempt demonstrated how complicated 
 and difficult it is to perform such data curation ( also indicating 
 the urgent need of consistent and clean data integration across different 
 resources ) . we also tried other protein classification methods 
 such as cath / scop / ec numbers . various issues were found , and we conclude 
 that they are not appropriate for our problem here . therefore , we 
 ventured back to the very basic concept of sequence similarity for 
 identification of unique protein families . all of the proteins bound 
 to the same ligand were compared for their sequence similarity , and 
 the ones with less than 80% similarity were retained . the threshold 
 was determined through a systematic analysis after experimenting with 
 various cutoff values ranging from 70% to 90% . however , we found that 
 we could maintain nonredundant proteins ( e.g. , some hiv protease mutants 
 have only 80% sequence similarity with the wild type ) only when using 
 this 80% sequence similarity cutoff for our data set . the filtering 
 was achieved with the uclust program which is a clustering algorithm 
 that employs usearch as a subroutine to assign sequences to clusters . since this problem has a significant complexity 
 due to the fact that some pdbs have multiple chains and multiple ligands , 
 the program actually considers each chain separately . so for all proteins binding the same ligand , the sequences 
 of their individual chains are compared with each other . the sequences 
 with similarity above a given threshold ( 80% ) will be grouped into 
 one cluster . in each cluster , the chains are sorted ( i.e. , ranked ) 
 according to the following criteria and the order : ( a ) a quality factor , 
 calculated as ( ( 1/resolution )  r - value ) ; ( b ) deposition date 
 ( newer structures have higher ranks ) ; ( c ) alphabetical order . from 
 each cluster , only the highest ranked chain will be picked as the 
 representative sequence , and this will lead to a set of nonredundant 
 chains for a given ligand . upon the 
 above filtering with the aid of sequence similarity clustering , we 
 obtained 953 multitargeting ligands associated with 4,167 distinct 
 pdbs belonging to various nonredundant proteins . this represents 4,298 
 positive binding data points as some pdbs have multiple chains with 
 multiple bound ligands . among the 953 ligands , 550 are crystallized 
 with two unique proteins ( 1,100 ligand - protein combinations ) , whereas 
 the other 403 are bound to more than two distinct proteins ( 3,198 
 ligand - protein combinations ) . it is very critical to understand that there could be various other 
 possible interactions that were not reported yet , as they do not have 
 solved crystal structures of these interactions . this database only 
 outlines the data extracted from the currently available crystal structures 
 and will be updated along the time . some statistics of our database , 
 including protein families , protein size , structure resolution , etc . , 
 are shown in figures s1s3 . for 
 instance about 10% proteins have below 200 residues , 40% of proteins 
 have 201500 residues , whereas 31% have residues between 501 
 and 1000 , and the remaining have more than 1000 residues ( figure s3 ) . for ligands , 30% of them have molecular 
 weights between 130 and 200 , 27% between 200 and 300 , 37% between 
 300 and 500 , and only 6% are above 500 ( figure 
 s4 ) . similarly , 64% of the ligands have logp values between 
 0.0 and 4.0 , and 14% with logp higher than 4.0 ( figure s5 ) , suggesting a high percentage of drug - like ligands . other characterizations of these multitargeting ligands , as compared 
 to single - targeting agents , are described next . as a dedicated database 
 for multitargeting ligands , it is of great interest to explore whether 
 they are significantly different from single - targeting agents . to 
 this end , typical chemical / physical properties such as molecular weight 
 and hydrophobicity were compared between the two groups of ligands . it is striking to note that , as demonstrated by our 2d plots in figure 2 and figure s7b , the 
 953 multitargeting ligands are on average smaller than the single - targeting 
 ligands . the multitargeting agents mostly have molecular weight below 
 200 da , while for single targeting ligands the molecular weight is 
 around 300420 da . to some extent , this is not unexpected as 
 lead optimization can improve the selectivity but usually companioned 
 with the increase of molecular size . accordingly , the hydrophobicity 
 as represented by slogp here is slightly lower for multitargeting 
 ( 02 ) than single - targeting ligands ( 04 ) . the similar 
 trend was also observed for the number of rings ( figure s7b ) and topological polar surface area ( tpsa ) ( figure 2 ) . we also performed comparison of other properties 
 such as hydrogen bonding patterns and molecular refractivity , as illustrated 
 in figure s7 . while several properties 
 such as aromatic atoms and molecular refractivity were observed to 
 have more broad range for single targeting ligands ( figures s7b  s7d ) since they generally have larger size , 
 it is surprising to see that the hydrogen bonding patterns are similar 
 for multitargeting and single - targeting agents , both have 34 
 hydrogen bond acceptors and 24 hydrogen bond donors ( figure s7a ) . comparison of slogp , tpsa , and molecular 
 weight in multiple - targeting ligands ( the upper panel ) vs single - targeting 
 ligands ( the lower panel ) . the plots represent the distribution density 
 of the ligands in the 2d space in terms of the respective chemical / physical 
 properties . the color code and scale is the same in each comparison for multitargeting 
 and single - targeting ligands . although we the present study is focused on small molecule 
 ligands , we also conducted characterization analysis of protein binding 
 pockets using our in - house programs and a web server vadar . of note , in the binding sites , no significant 
 differences were observed between the two groups of proteins , in terms 
 of residue composition , hydrophobicity , and polar surface area ( figure 3 ) . further case studies with molecular visualization 
 did not identify any unique features of  multitargeting  
 proteins ( figures s9a , b ) . this is not uncommon 
 because , as is known , small molecules can be optimized to improve 
 their selectivity toward a specific target . in other words , some ligands 
 can be rather specific , and they are different from other multitargeting 
 molecules , which is the primary point of this manuscript . however , 
 on the other hand , protein targets are a bit different : all proteins 
 are flexible , and each single of them can accommodate quite different 
 small molecules in terms of size , flexibility , and even chemotypes , 
 i.e. , they are all always  multitargeting  . therefore , 
 we do not expect any common features among them or unique features 
 compared to the single - targeting proteins . comparison of residue composition ( the upper panel ) and 
 chemical / physical properties of protein binding pockets of multitargeting 
 vs single targeting ligands . for our curated polypharmacological 
 agents , although their binary activities are apparent based on their 
 pdb complexes , the quantitative data of their binding affinities , 
 if available , would be more useful to develop accurate qsar models 
 or docking / scoring functions for multitargeting predictions . the curation 
 of ligand - protein binding affinities has been conducted by many groups 
 during the past decade , and a variety of databases have been constructed . to obtain the binding data for our specific multitargeting agents we found that 587 out of 953 ligands ( 61.5% ) have available 
 binding data from databases such as pdbbind , bindingdb , and moad . as these databases are implemented without a standardized 
 format , it was not trivial to retrieve the activity data automatically 
 from them . to this end , individual programs have been developed to 
 access these databases and extract the activity data in an automated 
 way . since the data is obtained from multiple databases , the redundancy 
 in the data was eliminated using in - house scripts . similarly when 
 conflicting data was obtained for the same ligand - protein binding , 
 we double - checked their initial reports to ensure the accuracy of 
 data collections . it is worthy of note that , although 
 the data about the ligands , targets , and their activities are also 
 available elsewhere ( e.g. , pdb or the databases cited here ) , polypharma is a specialized database dedicated to polypharmacological 
 ligands and is uniquely built to perform analysis , visualization , 
 and prediction of multitargeting properties . polypharma has been designed in a three - tier architecture ( figure 4 ) . the web user interface ( figure 5 ) was implemented with html / css / php ( version 5.3.27 ) , and 
 the database is managed by mysql ( version 5.5.32-cll ) . 2.2.24 ) with a http content accelerator are 
 deployed on a linux operating system ( kernel 2.6.32458 ) . peter ertl , is integrated to draw ligand molecules for chemical similarity and 
 substructure search . additionally , moldb5r , a collection of fully functional php scripts and perl scripts , is 
 embedded for search options based on ( a ) substructure and ( b ) functional 
 groups . moreover jmol is used for visualizing 
 ligand - protein interactions directly within an html page , and it provides 
 controls of different visualization schemes of the structures in the 
 jmol applet . finally we also integrated our molecular network analysis 
 technologies for visualization of multitargeting ligand - target interactions . some of the screenshots of the graphical user interface 
 of the polypharma database . for chemical similarity / substructure search , as a first step 
 all the ligands are encoded into their molecular fingerprints using 
 the checkmol program , and they are stored in our mysql database . to create a query , users 
 can draw a chemical structure using the jme applet embedded into the 
 search page , and the query structure will be converted to a mdl mol 
 file . based on this file , our backend programs will generate the fingerprints 
 which will be used to search the database for molecules with the similar 
 chemical features ( figure 5 ) . another useful 
 feature is that the fingerprints of chemical functional groups of 
 all the polypharmacological ligands are stored and used for searching 
 with moldb5r . additionally , the user is provided with the option to 
 select multiple functional groups to identify polypharmacological 
 ligands of their interest ( figure s6 ) . polypharma is dedicated to multitargeting agents 
 along with their specific targets and biological activities . option , 
 the queries can be keywords of ligands ( e.g. , aspirin ) or ligand expo 
 ids ( e.g. , sti ) . the results page displays all entries matching the 
 queries , including their ligand expo ids , ligand generic names , etc . the data is linked to a page of the available activity data ( ki , kd , ec50 , ic50 , ka , etc . ) of ligands , 
 the target information ( e.g. , pdb ids ) , and so on . as mentioned above , 
 the polypharmacological ligands can also be searched using either 
 substructures or using functional groups . the results are listed along 
 with the 2d structure of each resulted polypharmacological ligand 
 and linked to ( a ) ligand physicochemical properties , ( b ) original 
 ligandexpo entry page , and ( c ) available activity data . with the  target option , the queries can be pdb ids ( e.g. , 1mlw ) , disease names ( e.g. , 
 cancer ) , target description ( e.g. , hiv-1 ) , etc . the results page shows 
 the matched protein identities along with their ligands as well as 
 descriptions of the complex structures . furthermore , the activity 
 data of ligands for different targets is linked to the ligand expo 
 ids . users can also explore the link - out pages of pdb , drugbank , and 
 ligand expo databases . more documentation with examples and screen 
 shots are available at our web site http://imdlab.org/polypharma . users can also communicate with us for further suggestions or questions . as a unique and interesting feature , the ligand - target relations 
 are depicted and can be used to visualize the multitargeting molecular 
 interaction networks ( figure s8 ) . this 
 was built upon our technology of molecular network analysis as described 
 previously . this in - house technique , 
 as the first step , generates the list of all the polypharmacological 
 ligands and , for each ligand , obtains the target information from 
 our curated database through sql subroutines . then for each of the 
 targets , all of their complexed ligands information is obtained 
 from rcsb and used to construct the graphical network . an open source 
 visualization software graphviz is employed 
 to generate the ligand - protein networks for visualization . the major limitation 
 is that we only partially understand the pathways / mechanisms of many 
 diseases at the molecular level . it is exceedingly difficult to derive 
 the full polypharmacological networks without the complete data . as 
 a critical step of our attempt in this area , polypharma was built as a dedicated database specifically designed for polypharmacology 
 studies by providing accurate , experimentally validated structural 
 and activity data of multitargeting ligands . we have mined and integrated 
 information from a number of existing databases to extract related 
 data . the database will be updated monthly , and future releases will 
 include new multitargeting ligand molecules together with their targets 
 and biological activities as they become available . additional 
 functional features ( e.g. , searching by properties such as logp , chemaxon 
 fingerprints , etc . ) will also be added . similarly we plan to integrate 
 with more databases such as pubchem database , chembl database , and community structure we expect that our dedicated 
 database will lay a foundation for analysis of multitargeting ligand 
 properties and development of novel polypharmacology approaches . in 
 particular , with our accurate activity data ( both binary and continuous ) 
 along with high resolution structures , investigators can develop various 
 ligand - based ( e.g. , qsar ) and structure - based ( e.g. , docking / inverse 
 docking ) methods / models to predict off - targets or design polypharmacological 
 agents . notably , the database would also accelerate other drug development 
 efforts such as drug - repurposing . therefore , we anticipate that this work will vastly 
 promote polypharmacology studies , and it is significant to propel 
 the field forward . moreover , there are a few cases where two 
 different ligands bind to different sites of the same protein , which 
 may not be true examples of polypharmacology but may affect the target 
 functions , e.g. , binding of one may affect the binding of the other 
 due to allosteric effects . on the other hand , in many cases , binding 
 of one may have nothing to do with binding of the other  especially 
 if they bind to different domains . this type of situation further 
 makes the study more complicated and needs to be addressed in the 
 future . last but not least , as we already stated , it is important 
 to realize that the current data collection is far from complete . absence of a ligand - target data ( structure or binding affinity ) does 
 not mean they are not really interacting with each other . there could 
 be possible interactions that were not just yet reported . with more 
 data becoming available , we anticipate that our database will be more 
 useful for more accurate polypharmacology modeling .
in 
 drug discovery and development , the conventional  single drug , 
 single target  concept has been shifted to  single drug , 
 multiple targets   a concept coined as polypharmacology . 
 for studies in this emerging field , dedicated and high - quality databases 
 of multitargeting ligands would be exceedingly beneficial . to this 
 end , we conducted a comprehensive analysis of the structural and chemical / biological 
 profiles of polypharmacological agents and present a web - based database 
 ( polypharma ) . 
 all of these compounds curated herein 
 have been cocrystallized with more than one unique protein with intensive 
 reports of their multitargeting activities . 
 the present study provides 
 more insight of drug multitargeting and is particularly useful for 
 polypharmacology modeling . 
 this specialized curation has been made 
 publically available at http:/imdlab.org / polypharma/