the effects of small molecules on organisms have long been the focus of biochemistry and pharmacology . over the last years
there has been a considerable increase in the number of high - throughput screens that have been performed using chemical libraries ( 13 ) . at the same time , the molecular targets of individual chemicals are being studied in ever greater detail ( 4,5 ) . there also is a great interest in chemical biology approaches , using small molecules to perturb cellular functions ( 6 ) . for the design and interpretation of these studies , the context of the chemicals and proteins needs to be considered .
for example , in the case of high - content screening for specific cellular effects , it is important to know whether the active chemicals already have known activities that can explain the observed effects , or whether novel mechanisms of actions might be present .
therefore , we have developed a  search tool for interactions of chemicals ( stitch ) both as a large - scale , downloadable database of interaction data and as an interactive web tool for the exploration of interaction networks ( figure 1 ) . since its first release ( 7 )
, stitch is being accessed by over one hundred scientists each week and has been used as a source of protein  chemical associations e.g. by prathipati et al .
( 8) , who used the stitch network to automatically extract the targets of anti - tuberculosis compounds in mycobacterium tuberculosis . 

human proteins predicted to interact with aspirin according to different sources of evidence are shown .
edges are colored according to the source of evidence ( magenta : experimental information , cyan : manually curated databases , yellow : text - mining ) .
human proteins predicted to interact with aspirin according to different sources of evidence are shown .
edges are colored according to the source of evidence ( magenta : experimental information , cyan : manually curated databases , yellow : text - mining ) .
aspirin will display a pop - up showing the structure and description . here , we present the second version of stitch .
in addition to the sources of protein  chemical interactions included in the previous version 
pdsp ki database ( 9 ) , protein data bank ( pdb ) ( 10 ) , kegg ( 11 ) , reactome ( 12 ) , nci - nature pathway interaction database ( http://pid.nci.nih.gov ) , drugbank ( 13 ) and matador ( 14)we now further include interactions imported from glida ( 15 ) , pharmgkb ( 16,17 ) , comparative toxicogenomics database ( ctd ) ( 18 ) and bindingdb ( 19 )
. these added databases mainly provide information on interactions between human proteins and drugs or drug - like molecules .
the imported sources of information are scored separately and then combined with information from text - mining ( 7 ) .
databases which contain manually annotated interactions receive high scores , while interactions based on experimental information are scored by the confidence or relevance of the reported interaction . the number of high - confidence ( score  0.7 ) human chemical 
for these high - confidence interactions , the number of interacting human proteins increases from 5300 to 7400 ( as stitch is locus - based , only one gene product is counted per gene ) .
the stitch network is created by mapping interactions from the sources mentioned above and from text - mining onto a consolidated set of chemicals that has been derived from pubchem , assigning a confidence score for each interaction ( 7 ) . the newly - derived protein  chemical and chemical  chemical associations
are then complemented with protein  protein interactions from the string database ( 20 ) . in the previous version of stitch ( 7 )
these actions specify the nature of the interaction independent of the source of interaction information .
we have greatly extended the set of available actions by further importing action types from glida ( 15 ) , pharmgkb ( 16,17 ) , ctd ( 18 ) , bindingdb ( 19 ) and a manually annotated set of interactions .
this set of interactions has been curated from drugbank ( 13 ) records , results from nlp analysis of pubmed abstracts , medical subject headings ( mesh ) pharmacological actions , anatomical therapeutic chemical classification ( atc ) entries and a review paper on drugs and their targets ( 21 ) .
the number of available edges with a high - confidence action annotation increased from 44 000 to 65 000 human chemical  protein interactions .
as described previously ( 7 ) , stitch creates a consolidated set of chemicals from pubchem ( 22 ) by merging stereo isomers and salt forms of the same molecule into one compound .
this is done to ensure that all information about the same biologically active entity is merged . while this works very well for drugs that can be supplied in different formulations ( e.g. different salt forms )
it is our long - term goal to associate interactions both with the individual isomer and the merged structure . for now , we have taken the step to explicitly display all the different compounds that have been deemed biologically equivalent ( figure 2 ) . 

for the drug aspirin , a link to pubchem and a short description is shown .
different salts of aspirin that will have the same bioactivity have been consolidated and merged with the main , uncharged form . below each chemical structure , the first part of the inchikey is shown , corresponding to an encoded ( hashed ) description of the structure .
this short string can be used to search for more information about the compound on the internet .
different structural scaffolds corresponding to aspirin . for the drug aspirin , a link to pubchem and a short description is shown .
different salts of aspirin that will have the same bioactivity have been consolidated and merged with the main , uncharged form . below each chemical structure , the first part of the inchikey is shown , corresponding to an encoded ( hashed ) description of the structure .
this short string can be used to search for more information about the compound on the internet .
recently , the international union of pure and applied chemistry ( iupac ) has standardized an open format for chemical structures , namely the iupac international chemical identifier ( inchi ) .
in addition to the existing capability to search chemical structures using smiles string , we have now also implemented a search for inchis .
we use the tool open babel to convert inchis to smiles strings , which are in turn searched against our chemical database by using hashed fingerprints as implemented in the open - source chemical development kit ( 23 ) .
furthermore , we have implemented a search for inchikeys , which are short strings that represent an encoded ( hashed ) form of the chemical structure .
inchikeys consist of two parts , the first of which is based on the chemical connectivity , whereas the second part contains information about stereochemistry , tautomers and other structural variations .
as stitch currently considers structures with the same connectivity to be equivalent ( thus merging stereo isomers ) , only the first part of the inchikey is queried against our chemical database .
we also use this part of the inchikey to provide links to google and chemspider .
many proteins , especially drug targets , have a large number of high - scoring interactions with small molecules in the stitch network . in this case
, a network centered on such a protein will only show chemicals unless very many interaction partners are requested to be shown ( figure 3a ) . in order to allow the user to see more of the context of the query protein
, we now offer the option to show a network in which proteins and chemicals each make up more than a third of the nodes ( figure 3b ) .
when this option is selected , only a limited number of the highest - scoring chemicals are displayed .
further chemicals are omitted in favor of proteins ( or vice versa ) and their number is shown to the user . if the network consists of only chemicals , but no proteins are available at the current settings ( e.g. due to a minimum score limit ) , then the option to show more context is not shown . 

( a ) the highest - scoring interaction partners of ptgs1 are non - steroidal anti - inflammatory drugs ( nsaids ) .
as the confidence scores for these interactions are very high , no interacting proteins are shown .
( b ) the user may ask stitch to display more of the interaction context and to let at least one - third of the interaction partners be proteins . in this case , stitch is skipping 19 high - scoring chemicals in order to include four interacting proteins . in both networks
, the color of the edge corresponds to the type of connected nodes ( e.g. green : chemical  protein interaction ) and the width of the edge correlates with the confidence score .
( a ) the highest - scoring interaction partners of ptgs1 are non - steroidal anti - inflammatory drugs ( nsaids ) .
as the confidence scores for these interactions are very high , no interacting proteins are shown .
( b ) the user may ask stitch to display more of the interaction context and to let at least one - third of the interaction partners be proteins . in this case , stitch is skipping 19 high - scoring chemicals in order to include four interacting proteins . in both networks
, the color of the edge corresponds to the type of connected nodes ( e.g. green : chemical  protein interaction ) and the width of the edge correlates with the confidence score .
previously , stitch required the user to select an organism when searching for interactions with a chemical .
now , this is not required anymore . when no organism is selected , the organism with the highest - scoring interaction partners is selected . in case of multiple organisms with equal scores , human and several model organisms
( human is one of the highest - ranking species for 60% of the chemicals with protein  chemical interactions . ) for example , the binding between the antipsychotic agent fluspiperone and the 5hydroxytryptamine ( serotonin ) receptor 7 has only been studied in mouse and rat .
it is also possible to restrict the search to different levels of the ncbi taxonomy ( 24 ) , e.g. bacteria , fungi or rodents . while central repositories of gene annotations exist , no such information is available in a centralized manner for chemicals . to be able to display text annotation for chemicals ,
we have imported information from the following databases : drugbank ( 13 ) , national cancer institute ( nci ) thesaurus ( 25 ) , mesh descriptors and qualifiers .
using stitch s dictionary of chemical synonyms we mapped compounds from these databases to stitch identifiers . in case where descriptions are available for different forms of the same compound ( e.g. different salt forms , which have been merged in stitch ) ,
we have assigned the text annotation from only one source , prioritizing sources as follows : nci ( descriptions ) , drugbank ( descriptions ) , drugbank ( pharmacology ) , drugbank ( drug category ) , mesh ( pharmacological action ) , nci ( tags ) and mesh ( scope note ) .
descriptions are available for 33 352 chemicals , covering 33% of the chemicals with interactions .
the stitch homepage offers several short tutorials to introduce the different query options ( e.g. searching for a single identifier or multiple chemical structures ) .
a search for  aspirin on the homepage will lead to the interaction network shown in figure 1 . here , the main interactors of the drug are shown in human ( which is selected automatically as described above ) .
the known main targets , ptgs1 and ptgs2 , are connected by very high scores .
while most interaction partners are backed up by evidence from manually curated databases and are therefore very reliable , one interaction is derived only from text - mining : cox1 is actually a false positive arising from an ambiguous synonym .
taken together , stitch 2 offers an enlarged set of protein  chemical interactions , extended inter - database operability , increased query options and an improved user interface .
stitch can be accessed at http://stitch.embl.de/. users can explore the interaction network interactively or download the complete set of interactions .
in addition , we provide an application programming interface ( api ) to let scripts resolve identifiers and retrieve interaction networks either as an image or in standard network formats ( 20 ) .
novo nordisk foundation center for protein research ( partial ) . funding for open access charge : european molecular biology laboratory . conflict of interest statement .
 over the last years , the publicly available knowledge on interactions between small molecules and proteins has been steadily increasing . to create a network of interactions , stitch aims to integrate the data dispersed over the literature and various databases of biological pathways , drug  target relationships and binding affinities . in stitch 2 
 , the number of relevant interactions is increased by incorporation of bindingdb , pharmgkb and the comparative toxicogenomics database . 
 the resulting network can be explored interactively or used as the basis for large - scale analyses . to facilitate links to other chemical databases , 
 we adopt inchikeys that allow identification of chemicals with a short , checksum - like string . 
 stitch 2.0 connects proteins from 630 organisms to over 74 000 different chemicals , including 2200 drugs . 
 stitch can be accessed at http://stitch.embl.de/. 
INTRODUCTION
INCREASING THE NUMBER OF SPECIFIED ACTIONS
HANDLING OF CHEMICAL STRUCTURES
USER INTERFACE IMPROVEMENTS
USE CASES
FUNDING