disposing the 3d structure of small drug - like molecules can be critical for several computational approaches such as in silico screening ( 1,2 ) , either ligand - based ( 35 ) or receptor structure - based ( 69 ) employed prior to or to complement experimental screening for hit identification , lead optimization or chemical biology purposes .
in addition , for some of the methods , like rigid ligand docking or 3d ligand - based screening , a multiple conformer ensemble is required .
chemical compounds are often distributed by chemical vendors in 1d smiles ( simplified molecular input line entry system ) , 1d cansmiles ( canonical smiles ) ( 10 ) or in 2d sdf ( 11 ) ( structure data file ) formats .
generating an accurate 3d structure for a small chemical compound is a complex task ( 12 ) .
different techniques using rule - based or data - based methods , building linker regions on pre - generated fragments or stochastic procedures up to quantum mechanical methods ( 12,13 ) have been developed .
numerous studies have been carried out to compare the existing approaches and to analyze the small molecule conformations experimentally observed ( 14,15 ) .
they revealed that for a satisfactory sampling of the conformational space , the most important parameters to be optimized are the energy window with respect to the global minimum and the root mean square deviation ( rsmd ) value .
several well established commercial packages such as corina ( corina molecular networks , gmbh computerchemie langemarckplatz 1 , erlangen , germany , 2000 ) , omega , catalyst ( 14 ) or med-3dmc ( 16 ) generate multiple ensemble conformations of small molecules .
in addition , several utilities like zinc ( 17 ) , faf - drugs ( 18 ) or pubchem ( 18 ) take advantage of commercial software to propose pregenerated collections of compounds in 3d . yet ,
very few free tools are available for a single or multiple conformation generation . for instance , balloon ( 13 ) using a multi - objective genetic algorithm approach and multiconf - dock http://dock.compbio.ucsf.edu/contributed_code/index.htm ( 20 ) using a systematic search are freely available . the open source program dg - ammos ( 21 ) based on a distance geometry approach and molecular mechanics optimization has been recently reported .
a practical alternative of the standalone packages , in particular for non - advanced users , are the web services which can provide direct 1d/2d to 3d facilities , such as openeye 's omega , molsoft , corina and from some academic sites such as at cbs .
three years ago we developed and reported the web - service frog ( 22 ) ( http://bioserv.rpbs.univ-paris-diderot.fr/frog.html ) providing an on - line generation of a single or ensembles of 3d conformations for drug - like compounds .
frog is a mixed rule - based data - based approach based on frowns ( a chemoinformatics toolkit available at http://frowns.sourceforge.net/ ) to which several functionalities have been added to allow the generation of 3d structures starting from smiles or sdf data input . here , we describe a new version of frog , frog2 , which is able to ( i ) generate single or ensembles of low to medium energy 3d conformations starting from 1d/2d or 3d input structure , to ( ii ) fully or partially disambiguate compound stereochemistry including chiral sites with a user - defined maximum number of generated conformers , and to ( iii ) minimize the energy of the generated conformers using ammos ( 18 ) if the user requires .
the following important improvements are achieved in frog2 compared to frog1 : ( i ) generation of compound rings not available in the fragment database of frog1 using dg - ammos ( 17 ) ; ( ii ) significantly improved diversity of the generated conformer ensembles , and ( iii ) considerable increase of the computation speed .
in addition , several ancillary tools are provided , such as the format interconversion using openbabel ( 23 ) or energy minimization of 3d conformations via ammos ( 24 ) .
frog2 still does not allow ring flexibility during the multiple confirmation generation which is under development . in this study ,
in addition to describing the web - service frog2 , we validate frog2 on compounds from the astex dataset ( 25 ) .
the comparison of frog2 with frog1 and the commercial package omega ( http://www.eyesopen.com ) shows persuasive performance of frog2 .
the new and the optimized modules of frog2 are shown in the gray white boxes .
overall , the frog internal 3d generation is based on a graph decomposition of the compound ( 22 ) coupled with an identification of the stereo centers for which the chirality is unspecified .
once these sites have been identified , the combinatorial of the unambiguous isomers  for which the chirality of all the identified stereo centers is completely specified  is generated , but randomly truncated to a maximum of eight chiral centers for 3d generation .
for each of these , a starting 3d conformation is generated . during this generation , frog2 takes advantage of dg - ammos to generate on the fly missing rings and adds them to the ring library , thus escaping a major limitation of frog1 .
instead , it relies on openbabel ( 23 ) to generate hydrogen coordinates for a standard protonation state . 

another frog2 major improvement comes from the optimization of the conformation ensemble generation engine . as in frog1
the first stage explores the conformational flexibility based on a limited number of representative dihedral angular values depending on atom types .
this monte - carlo step includes a procedure to avoid conformation redundancy : new conformations that correspond to previously visited combinations of dihedral values are forbidden .
also , the possibility of biasing the exploration of dihedral values depending on their position relative to the center of the compound has been introduced . for this , the probability that a step affects a particular dihedral is not uniform , but depends on the number of flanking atoms at each side , using the following weight : , where   ( resp . )
right ) of the bond undergoing the rotation ,   is the total number of atoms , so that dihedrals located at terminal positions have smaller weights compared to dihedrals having a balanced number of atoms each side .
the second - stage monte carlo uniformly considers small rotations within the stage one conformations , so as to refine them . in order to prune the combinatorial exploration ,
this described conformation ensemble generation engine is supplemented by a standard divisive hierarchical clustering using an approach similar in essence to that used for fingerprints ( 26 ) .
this approach guarantees that all conformers in a class are below a fixed rmsd threshold and that the rmsd between selected conformers are always above that threshold .
finally , in order to speed the computations , the rank of the conformers is based on an internal score of frog2 taking into account only van der waals interactions , which might be insufficient to prevent some geometry distortions during the assembly process . to overcome this limitation ,
it is possible to minimize the conformers using the ammp force field as implemented in ammos ( 24 ) .
frog2 accepts as input three formats widely used by the community : smiles , sdf and mol2 formats .
since the sdf and mol2 formats can correspond to both 2d or 3d descriptions of a compound , the user must be precise among the two possible types of input : 1d/2d or 3d . specifying the 1d/2d type ,
all 3d information of the input will be discarded and the 3d generation will be performed from scratch . specifying the 3d type , frog2 will simply call the conformation ensemble generation engine , not considering stereoisomery and taking the input coordinates as a starting point for the conformation ensemble generation .
parameters related to the generation of ensembles correspond to a maximal number of conformations , and energy thresholds to define the allowed energy window referred to the lowest energy conformation generated .
it is also possible to specify a minimal rmsd value for geometric clustering of the conformers , and to invoke the minimization of the compounds .
the energy minimization takes into account the valence , angle and van der waals components , not considering electrostatics terms .
it is , however , possible to choose between mol2 , sdf or pdb formats , thanks to openbabel .
the new and the optimized modules of frog2 are shown in the gray white boxes .
overall , the frog internal 3d generation is based on a graph decomposition of the compound ( 22 ) coupled with an identification of the stereo centers for which the chirality is unspecified .
once these sites have been identified , the combinatorial of the unambiguous isomers  for which the chirality of all the identified stereo centers is completely specified  is generated , but randomly truncated to a maximum of eight chiral centers for 3d generation .
for each of these , a starting 3d conformation is generated . during this generation , frog2 takes advantage of dg - ammos to generate on the fly missing rings and adds them to the ring library , thus escaping a major limitation of frog1 .
instead , it relies on openbabel ( 23 ) to generate hydrogen coordinates for a standard protonation state . 

another frog2 major improvement comes from the optimization of the conformation ensemble generation engine . as in frog1
the first stage explores the conformational flexibility based on a limited number of representative dihedral angular values depending on atom types .
this monte - carlo step includes a procedure to avoid conformation redundancy : new conformations that correspond to previously visited combinations of dihedral values are forbidden .
also , the possibility of biasing the exploration of dihedral values depending on their position relative to the center of the compound has been introduced . for this , the probability that a step affects a particular dihedral is not uniform , but depends on the number of flanking atoms at each side , using the following weight : , where   ( resp . )
right ) of the bond undergoing the rotation ,   is the total number of atoms , so that dihedrals located at terminal positions have smaller weights compared to dihedrals having a balanced number of atoms each side .
the second - stage monte carlo uniformly considers small rotations within the stage one conformations , so as to refine them . in order to prune the combinatorial exploration ,
this described conformation ensemble generation engine is supplemented by a standard divisive hierarchical clustering using an approach similar in essence to that used for fingerprints ( 26 ) .
this approach guarantees that all conformers in a class are below a fixed rmsd threshold and that the rmsd between selected conformers are always above that threshold .
finally , in order to speed the computations , the rank of the conformers is based on an internal score of frog2 taking into account only van der waals interactions , which might be insufficient to prevent some geometry distortions during the assembly process . to overcome this limitation ,
it is possible to minimize the conformers using the ammp force field as implemented in ammos ( 24 ) .
frog2 accepts as input three formats widely used by the community : smiles , sdf and mol2 formats .
since the sdf and mol2 formats can correspond to both 2d or 3d descriptions of a compound , the user must be precise among the two possible types of input : 1d/2d or 3d . specifying the 1d/2d type ,
all 3d information of the input will be discarded and the 3d generation will be performed from scratch . specifying the 3d type , frog2 will simply call the conformation ensemble generation engine , not considering stereoisomery and taking the input coordinates as a starting point for the conformation ensemble generation .
parameters related to the generation of ensembles correspond to a maximal number of conformations , and energy thresholds to define the allowed energy window referred to the lowest energy conformation generated .
it is also possible to specify a minimal rmsd value for geometric clustering of the conformers , and to invoke the minimization of the compounds .
the energy minimization takes into account the valence , angle and van der waals components , not considering electrostatics terms .
it is , however , possible to choose between mol2 , sdf or pdb formats , thanks to openbabel .
we first assessed the frog2 performance for finding conformation similar to bioactive ones among the generated multiconformation ensemble .
figure 2 shows the results for generating multiconformation ensembles of the astex dataset ( 25 ) containing 85 diverse drug - like molecules using frog2 , frog1 and omega 2 [ openeye scientific software ( http://www.eyesopen.com ) ] . the same input parameters were applied to run frog2 , frog1 and omega , namely : up to 50 conformers , rmsd threshold of 0.8. . in order to make possible the comparison with frog1 ,
using these input parameters , applying unambiguation for the stereoisomery and allowing the stage two monte - carlo , frog2 finds conformations closer to the bioactive ones than frog1 for most of the astex molecules . on average ,
the frog2 bioactive ' closest conformation is at  rmsd values whereas frog1 shows an average rmsd of  .
interestingly , frog2 performs better than frog1 even without employing the stage two monte carlo with an average rmsd of , respectively .
similar results in terms of rmsd are obtained using frog2 and omega when a maximum of 50 conformers were generated on the astex dataset with a slight , outperformance of omega with average values being of   ( wilcoxon signed rank test shows no significant difference ) . for a maximum of 50 conformers per molecule ,
omega and frog2 found conformations within an rmsd with the bioactive one of 1.5  for 82 and 79 compounds out of 85 , respectively . that can be considered as an acceptable accuracy keeping in mind that the required rmsd for clustering the similar conformations was set to 0.8  .
finally , figure 2c illustrates the impact of increasing the maximal number of conformers up to 100 .
examples demonstrating the conformational diversity achieved by frog2 and omega when 50 conformers generated for two molecules of the astex dataset can be seen in figure 3 .
according to the computed rmsd values and visual analysis , one can conclude that both frog2 and omega explore quite well the conformational space and are able to generate conformations that are close to the bioactive one .
in addition , the user has the possibility to energy - minimize the generated conformers via frog2 or to minimize a own compound library in 3d .
our assessment of the impact of the minimization on the frog2-generated astex conformers does not show a significant improvement by means of finding the bioactive conformations .
on average , the rmsd from the bioactive conformations was of  , i.e. similar results to the ones obtained by frog2 without a final minimization stage .
in addition , we validated the frog2 performance on a larger collection of compounds taken from the pdbbind database ( 27 ) .
the compounds contatining up to 15 rotatable bonds and not including large bridged rings ( more than 30 atoms , see the discussion about frog2 limitations above ) were retained .
the distribution of the resulted test - set compounds depending on the number of rotatable bonds is given in the supplementary data .
the results for rmsd between the best generated and experimental structures are also given in the supplementary data .
the conformer ensembles were generated for a maximum of 50 conformers , enabling stage two monte carlo , and without applying minimization .
as can be expected , the performance ( in terms of rmsd ) decreases with increasing molecular flexibility .
the median rmsd to the experimental conformation is below 1 up to 7 rotatable bonds , and frog2 results remain however acceptable up to 15 rotatable bonds . 
 figure 2.rmsd between the best - fitted conformers and the x - ray structures for conformers generated by : ( a ) frog1 versus frog2 for series of up to 50 conformers ; ( b ) frog2 versus omega for series of up to 50 conformers .
pdb codes denote ( i ) the compounds with poorest relative performance , i.e. deviating the most from the diagonal ( 1y6b , 1xoz ) and ( ii ) best performing for both frog2 and omega ( 1gpk , 1u4d , 1sqn ) ; and ( c ) frog2 for series of up to 50 conformers versus frog2 for series of up to 100 conformers . 
 figure 3.conformational ensembles for two small molecules of the astex dataset generated by frog2 ( all atom colors , carbons in yellow ) and omega ( all atom colors , carbons in light pink : ( a ) pdb code 1jla ; and ( b ) pdb code 1meh . the experimental structure of the co - crystallized ligands are also shown ( all atom colors , carbons in magenta ) .
rmsd between the best - fitted conformers and the x - ray structures for conformers generated by : ( a ) frog1 versus frog2 for series of up to 50 conformers ;
pdb codes denote ( i ) the compounds with poorest relative performance , i.e. deviating the most from the diagonal ( 1y6b , 1xoz ) and ( ii ) best performing for both frog2 and omega ( 1gpk , 1u4d , 1sqn ) ; and ( c ) frog2 for series of up to 50 conformers versus frog2 for series of up to 100 conformers .
conformational ensembles for two small molecules of the astex dataset generated by frog2 ( all atom colors , carbons in yellow ) and omega ( all atom colors , carbons in light pink : ( a ) pdb code 1jla ; and ( b ) pdb code 1meh .
the experimental structure of the co - crystallized ligands are also shown ( all atom colors , carbons in magenta ) . finally , we assessed the gain in speed of frog2 compared to frog1 . on a computer with intel xeon processors at 2 ghz , omega - generated conformers for all the astex set in 6 min .
in addition , the computational time can be reduced to 5 min if frog2 is run without the two monte carlo stage . in summary ,
frog2 generates better conformational quality and diversity than frog1 and is nine times faster than frog1 using conditions strictly comparable .
frog2 still outperforms frog1 when employing the faster approach without two - stage monte carlo in terms of conformational diversity and is 20 times faster . this dramatic decrease in execution time without affecting the quality of the generated conformations demonstrates the impact of the optimizations implemented into frog2 .
we should note that activating the minimization option leads to significant increase of the computational time . over the astex set , using the disambiguation option and stage two monte carlo , the calculation times increased from 11 min up to 207 min , i.e. close to 20 times slower .
however , the minimization may be required to energy - minimize some generated structures with detected structural inaccuracies , or for a small number of compounds .
the main goal of the frog2 development was to overcome several limitations of frog1 and to increase the overall quality and speed performance . over these , rings were a central concern .
indeed , frog2 dependency on a ring library is much less critical since frog2 takes the advantage of dg - ammos to generate on the fly missing rings .
furthermore , frog2 addresses a frog1 issue problem related to rings ' protonation now generated standardly via openbabel . as a result ,
used over 10 000 compounds randomly selected out of 40 0000 cmps from the chembrdge diverset ( http://www.chembridge.com/ ) , frog2 only failed to generate conformers for < 2% of the compounds .
as illustrated from the tests on the astex set , frog2 reaches a good structure quality .
the frog2 development permitted to better control the diversity in the conformation ensemble generation and frog2 reaches extensive conformational diversity .
further improvements are expected . in particular , better consideration of symmetry is under investigation .
indeed , in a context of virtual screening experiments , where millions of compounds can be considered , the time to generate libraries can become a concern .
one can suppose that once generated in 3d , a chemical compound collection can be in silico screened for different projects , however , more and more generation of focused libraries , either target - based or ligand - based , is required to increase the efficiency of discovery programs . in this respect , frog2 brings significant improvement since it is able to generate satisfactory quality conformations 20 times faster than frog1 .
we should note that a balance between the speed and conformational diversity can be achieved depending on the project purposes and the number of compounds to treat . inactivating the stage two monte carlo , operational on the server , will result in a faster generation , but a reduced conformational diversity will be reached . for applications requiring
finally , frog2 takes advantage of ammos to offer the possibility to minimize the conformations that can be very helpful in some particular cases , even its use results in dramatically larger computational cost .
several limitations are still present in frog2 . the first one is related to the ring rigidity that can affect the quality or the diversity of the conformations generated in some cases .
particularly , compounds including large bridged rings for which flexibility impacts the conformational search are presently out of frog scope .
also , a side effect of the ring library strategy is that frog2 is presently not able to treat properly some cases of stereoisomery involving stereo centers in rings .
finally , some errors in the generation of some particular groups can be presently observed since frog2 builds from scratch the linkers , at a single atom level , i.e. not combining pregenerated fragments .
in addition , the implementation of some ring flexibility is under development . despite of the minor current limitations ,
the use of frog2 could help for various in silico studies , from ligand - based or structure - based virtual screening , to lead compounds optimization etc .
thus , the frog2 server offers a very valuable tool which provides efficient features to the community for an extremely competitive computational time .
 frog is a web tool dedicated to small compound 3d generation . here 
 we present the new version , frog2 , which allows the generation of conformation ensembles of small molecules starting from either 1d , 2d or 3d description of the compounds . from a compound description in one of the smiles , sdf or mol2 formats 
 , the server will return an ensemble of diverse conformers generated using a two stage monte carlo approach in the dihedral space . when starting from 1d or 2d description of compounds , frog2 is capable to detect the sites of ambiguous stereoisomery , and thus to sample different stereoisomers . 
 frog2 also embeds new energy minimization and ring generation facilities that solve the problem of some missing cycle structures in the frog1 ring library . finally , the optimized generator of conformation ensembles in frog2 results in a gain of computational time permitting frog2 to be up to 20 times faster that frog1 , while producing satisfactory conformations in terms of structural quality and conformational diversity . the high speed and the good quality of generated conformational ensembles 
 makes it possible the treatment of larger compound collections using frog2 . 
 the server and documentation are freely available at http://bioserv.rpbs.univ-paris-diderot.fr/frog2 . 
INTRODUCTION
METHODS
Concepts
Input/output
RESULTS
DISCUSSION AND FUTURE DIRECTIONS
SUPPLEMENTARY DATA
FUNDING