fragment screening has become an increasingly 
 important technique in early stage drug discovery . occupying the chemical space that is commonly described 
 by the  rule - of - three ( ro3 ) criteria of physicochemical 
 properties , fragment - like molecules are 
 smaller in size than lead - like and drug - like molecules used in conventional 
 high - throughput screening ( hts ) .
screening of fragment - sized molecules 
 aims to sample a greater coverage of chemical diversity using a smaller 
 collection of compounds , at the same time 
 providing better hits as starting points for optimization into chemical 
 leads .
typical fragment screening 
 libraries contain compounds that originated either from commercial 
 sources or proprietary synthetic chemical intermediates . while these 
 fragments may mostly conform to the ro3 physicochemical properties 
 criteria , the chemical space they represent may not entirely project 
 to the lead - like or drug - like chemical spaces that are relevant to 
 medicinal chemistry . to ensure appropriate sampling of chemical space 
 in fragment screening , it is essential to understand the current coverage 
 of known medicinal chemistry space by commercial fragments . using 
 chembl as an open - access medicinal chemistry 
 repository , we investigated the overlap between the fragment - like 
 chemical space of exemplified medicinal chemistry substructures generated 
 from chembl and that of fragments available from commercial vendors 
 listed in the emolecules database .
the 
 distribution of biologically active medicinal chemistry compounds 
 was also analyzed , and property trends that differentiate active from 
 inactive compounds were identified . based upon these results ,
we make 
 recommendations on the composition of fragment screening libraries 
 to enhance sampling of the chemical space in fragment screening that 
 is enriched in biologically relevant compounds .
all compounds annotated as small molecules 
 in chembl and with a minimum of one ring 
 ( 1.01 million unique compounds ) were used in this study .
although 
 only 46080 compounds from chembl conformed to the fragment - like ro3 
 criteria ( considering all physicochemical property criteria , apart from topological polar surface area for 
 which no limit was applied in this study ) , all small molecules in the chembl database were fragmented to capture 
 all substructures that are within the sampling space of ro3-compliant 
 commercial fragments . for that purpose ,
each compound was subjected 
 to two independent segmentation algorithms to obtain substructures 
 represented by a level 1 scaffold and 
 multiple ring segments .
level 1 scaffolds 
 generated from the scaffold tree algorithm represent the majority of molecules using a two - ring substructure , 
 either in the form of a fused ring system or two linked monocycles . 

the only exception is for substituted monocyclic compounds for which 
 the level 1 scaffold is identical to the entire molecule and therefore 
 contains only one ring .
the ring segmentation 
 algorithm generates ring systems with directly attached polar functional 
 groups .
the former method retains linkers 
 in the molecular scaffold , whereas the latter method describes functional 
 groups relevant for molecular recognition .
. 
 however , level 1 scaffolds that contained only one substituted monocycle 
 were disregarded because such scaffolds were identical to their parent 
 molecules with all substitutions retained .
for example , all compounds 
 comprising substituted phenyl rings would be independently represented , 
 rendering the data set too granular .
nonetheless , substructures containing 
 appropriate functionalities capable of molecular recognition on substituted 
 monocyclic rings were captured using the ring segmentation algorithm . 

although the filtered out substructures are relevant 
 to medicinal chemistry space , they are beyond the sampling space of 
 ro3-compliant commercial fragments that constitute many typical fragment 
 screening libraries and were hence not included in this study . in 
 total
, 72880 level 1 scaffolds and 46163 ring segments were filtered 
 out due to violation of at least one of the ro3 criteria . in particular
, almost 44000 
 of the filtered level 1 scaffolds were substituted monocycles that 
 contained over three rotatable bonds from various substituents . finally , 
 substructures with unwanted functionalities such as reactive , metabolically 
 labile groups or toxicophores were also excluded .
this led to a collection of 13922 unique level 1 scaffolds 
 and 14738 unique ring segments in the chembl data set ( figure 1 ) .
rule - of - three ( ro3 ) 
 compliance , unwanted functionalities and substituted monocycle filter ; ro3 compliance and unwanted functionalities 
 filter ; unwanted functionalities and 
 substituted monocycle filter ; unwanted 
 functionalities filter .
the commercial fragments data set was compiled by first applying 
 the ro3 filter criteria to the emolecules database containing 5.2 
 million commercially available compounds to include only compounds that represent the chemical space commonly 
 sampled in typical fragment screening libraries .
substructures of 
 224575 ro3-compliant commercial fragments were generated using the 
 aforementioned segmentation algorithms .
the resultant level 1 scaffold 
 substructures were subjected to both the unwanted functionalities 
 and substituted monocycles filters , whereas only the unwanted functionalities 
 filters were applied to the ring segment substructures .
the final 
 commercial fragments data set contained 11862 unique level 1 scaffolds 
 and 11257 unique ring segments ( figure 1 ) . to understand 
 the sampling of exemplified medicinal chemistry space using commercial 
 fragments , the overlap between the substructures generated from both 
 data sets
, there are 27077 unique substructures 
 derived from chembl and 21228 unique substructures derived from ro3-compliant 
 commercial fragments ( figure 1 ) .
only 10067 
 ( 26% of all the 38238 substructures , the same number of total substructures applies to all subsequent percentage comparisons in this paragraph ) 
 substructures are common in both data sets ( region a , figure 2a ) .
the overlap increased when nonidentical but 
 chemically similar substructures were included . using molecular fingerprints 
 analogous to the daylight fingerprints ( epfp_7 ) , 4346 substructures 
 ( 11% , region c ) derived from chembl compounds had one of their nearest 
 neighbors in the commercial fragments data set above a 0.85 tanimoto 
 similarity cutoff , a threshold at which any molecular pairs above 
 this similarity can be considered chemically similar .
the remaining 12664 substructures from chembl ( 33% , region 
 b ) had low similarity to any commercial fragment substructures .
in 
 an analogous comparison , 4073 substructures ( 11% , region d ) derived 
 from ro3-compliant commercial fragments had a chemically similar substructure 
 in chembl as one of their nearest neighbors .
the remaining 7088 substructures 
 ( 19% , region e ) had low similarity to any chembl substructures ( figure 2a ) .
venn diagrams illustrating the overlap between substructures 
 derived from chembl small molecules ( blue ) and commercial fragments 
 ( yellow ) .
the subsets of substructures that have one of the nearest 
 neighbors above a 0.85 tanimoto similarity cutoff ( epfp_7 ) in the 
 counterpart data set are represented by purple and orange circles . 

( a ) all chembl substructures versus all commercial fragment substructures ; 
 ( b ) only substructures derived from bioactive parent chembl compounds 
 versus all commercial fragment substructures .
the subset of commercial 
 fragment substructures below the 0.85 tanimoto cutoff that only appeared 
 in nonactive parent chembl compounds is represented by the red circle .
next , the same analysis was applied 
 to evaluate a subset of the substructures of the chembl data set . 

this time , only substructures that are derived from biologically active 
 chembl compounds ( 358240 unique compounds , of which 12018 are ro3-compliant ) 
 were considered .
these were defined as the parent compounds annotated 
 with ki , kd , ic50 , or ec50  10 m .
this subset , 
 containing 16518 substructures , was compared to those derived from 
 ro3-compliant commercial fragments ( figure 2b ) .
only 6965 substructures ( 23% of all the 30781 substructures , 
 the same number of total substructures applies to all subsequent percentage 
 comparisons in this paragraph ) are common to both data sets ( region 
 a ) . when extending the overlap by including chemically similar substructures , 
 2435 substructures ( 8% , region c ) from bioactive chembl compounds 
 had similar nearest neighbors in commercial fragments .
over 7000 bioactive 
 chembl substructures ( 23% , region b ) had no similar substructures 
 in commercial fragments .
vice versa , 14263 substructures of the commercial 
 fragment set did not overlap with the chembl bioactive set ( regions 
 d , e and f ) .
4053 of these substructures ( 13% , region d ) had a chemically 
 similar substructure in chembl as one of their nearest neighbors . 

a further 2146 of these substructures ( 7% , region f ) did not resemble 
 ro3-compliant chembl substructures derived from bioactive compounds , but were present in nonactive parent compounds in chembl ( previously 
 in region a of figure 2a ) . after establishing 
 the overlap between the data sets ,
the chemical space occupied by 
 the generated substructures was further evaluated . using 16 descriptors 
 to characterize the physicochemical properties and molecular complexity 
 of the generated substructures ( table 1 ) , principal 
 component analysis ( pca )
according to the 2d pca 
 plot ( figure 3a ) , the substructures derived 
 from chembl compounds and those derived from ro3-compliant commercial 
 fragments occupy the same areas of chemical space .
subsequently , the 
 distribution of substructures derived from commercially available 
 fragments within the chemical space was analyzed ( figure 3c ) .
the density plot indicated that the highest 
 representation of these substructures is in quadrants one and four , 
 corresponding to chemical space with increasing molecular size and 
 aromaticity respectively , whereas quadrants two and three , corresponding 
 to chemical space with increasing three - dimensionality and polarity 
 respectively , have lower representation .
this distribution remains 
 similar when only the substructures that are identical to , or chemically 
 similar to , bioactive chembl substructures ( 11018 substructures , regions 
 a and d in figure 2b ) were considered ( figure 3d ) .
categorical descriptors with discrete unit values 
 were normalized relative to the number of heavy atoms , unless stated 
 otherwise .
finally , the 
 overlap between the chembl substructures derived from bioactive compounds 
 and those that only appeared in inactive compounds was compared ( figures 3e to 3j ) .
the chemical space 
 covered by both subsets is largely similar ( figure 3e ) .
however , the distribution of these substructures is uneven 
 in chemical space ( figures 3f , 3 g , and 3h ) .
the density plots indicate 
 that quadrants one and four of the 2d pca plot , corresponding to chemical 
 space with increasing molecular size and aromaticity , respectively , 
 have the highest representation , whereas quadrants two and three , 
 corresponding to chemical space with increasing three - dimensionality 
 and polarity respectively , have lower occupancy .
this distribution 
 remains similar when the substructures that are derived from bioactive 
 compounds and those only from inactive compounds were separately analyzed 
 ( figures 3 g and 3h , 
 respectively ) . to further elucidate the differences between 
 the biologically active and inactive substructures ,
the difference 
 and enrichment plots of the 2d pca plot in figure 3e were generated by dividing the scoring plot into cells of 
 size 0.25  0.25 .
the difference plot ( figure 3i ) shows the difference between the normalized occupancy of 
 biologically active and inactive substructures within each cell .
the 
 region of chemical space having the highest positive difference is 
 in quadrant four that corresponds to increasing aromaticity , whereas 
 there are more cells with negative difference observed in quadrant 
 one that corresponds to increasing substructure size .
the enrichment 
 plot ( figure 3j ) shows the ratio of the normalized 
 occupancy of bioactive substructures to all substructures within each 
 cell , with the average ratio at 0.52 .
quadrants two and three , characterizing 
 substructures with increasing three - dimensionality and polarity respectively , 
 appear to have more cells with ratios higher than average compared 
 to quadrants one and four .
quadrants 
 are counted clockwise with the first quadrant in the top right corner . 

( a ) scoring plot with ellipses showing the 95% confidence intervals 
 of the distributions of substructures derived from chembl compounds 
 ( red ) , ro3-compliant commercial fragments ( blue ) , and those common 
 to both data sets ( region a in figure 2a ) ( cyan ) . 

( c ) density plot of the distribution 
 of all substructures from commercial fragments .
the ellipse ( same 
 for figures ( d ) , ( f ) , ( g ) , and ( h ) ) corresponds to a confidence level 
 of 95% of hotelling s t distribution 
 of the entire chemical space analyzed .
( d ) density plot of the distribution 
 of commercial fragments that are identical to , or considered chemically 
 similar to , bioactive chembl substructures ( regions a and d in figure 2b ) .
( e ) scoring plot with ellipses showing the 95% 
 confidence intervals of the distributions of substructures derived 
 from biologically active chembl compounds ( red ) and those only from 
 inactive compounds ( blue ) .
( g ) density plot of the distribution of biologically active chembl 
 substructures in ( e ) .
( h ) density plot of the distribution of biologically 
 inactive chembl substructures in ( e ) .
( i ) difference plot of ( e ) between 
 the normalized occupancy of biologically active and inactive substructures 
 within each cell .
( j ) enrichment plot of ( e ) showing the ratio of 
 the normalized occupancy of biologically active substructures to all 
 substructures within each cell .
the application of fragment screening in 
 hit discovery aims to efficiently sample the lead - like and drug - like 
 chemical space using a small collection of compounds .
while many typical 
 fragment libraries contain commercial fragments that mostly conform 
 to the ro3 physicochemical properties criteria , the chemical space 
 these fragments represents may not entirely project to the lead - like 
 and drug - like chemical space that are relevant to medicinal chemistry . 
 by generating substructures from chembl compounds and ro3-compliant 
 commercial fragments and analyzing the overlap between the two data 
 sets , the chemical space coverage in fragment screening collections 
 derived from commercial fragments
the overlap 
 analyses ( figure 2b ) indicate that the number 
 of identical substructures derived from bioactive chembl compounds 
 and commercial fragments ( region a ) only accounts for less than 25% 
 of all the 30781 substructures present .
even when chembl substructures 
 similar to commercial fragments ( region c ) are included , more than 
 7000 substructures ( over 40% of the 16518 substructures derived from 
 bioactive chembl compounds , region
almost half of the substructures derived from bioactive 
 medicinal chemistry compounds are not covered using currently available 
 commercial fragments .
chemical syntheses of the substructures that 
 are unique to chembl ( region b ) would be the most exhaustive way to 
 improve the sampling of known bioactive medicinal chemistry space 
 in fragment screening libraries , even though such compounds may be 
 synthetically challenging ( see figures 4 and s1 for exemplars of substructures in region b 
 of figure 2b that frequently appear in bioactive 
 parent chembl compounds ) .
alternatively , we recommend the design of 
 fragment screening libraries should initially and pragmatically focus 
 on exemplifying the substructures that occur in both the chembl set 
 and the commercially available fragment set ( region a ) , or those that 
 occur in commercially available fragments and are similar to substructures 
 derived from bioactive molecules in chembl ( region d ) .
these substructures 
 represent compounds that have reported bioactivity in the primary 
 literature or resemble closely bioactive substructures derived from 
 chembl .
hence , including these compounds in fragment screening libraries 
 will ensure appropriate coverage of chemical space relevant to known 
 bioactive medicinal chemistry series in fragment screens .
this is 
 useful in particular when financial constraints may limit the number 
 of compounds purchased for a fragment screening library , since these 
 compounds can offer a higher confidence of discovering hit matter 
 given the literature precedence of the represented substructures . 

however , these substructures may have already been extensively studied 
 and gaining intellectual property rights from these substructures 
 may prove challenging .
exemplars of frequently occurring substructures derived 
 from bioactive chembl compounds that have low similarity to commercial 
 fragments ( region b of the venn diagram in figure 2b ) .
when considering expanding 
 fragment screening libraries , compounds represented by substructures 
 in commercial fragments that have low similarity to those derived 
 from bioactive chembl compounds ( regions e and f , figure 2b ) may become useful to expand the chemical space 
 sampling .
represented by almost 50% of the 21228 substructures derived 
 from commercial fragments , these compounds either have not been subjected 
 to any biochemical or cell assay , or their assay results were not 
 reported in the primary literature ( region e ) , whereas compounds represented 
 by substructures in region f have thus far only resulted in inactive 
 compounds in the primary literature . although these substructures 
 have not been demonstrated to display any biological activity , they 
 could offer less explored scaffolds that have a better potential in 
 gaining intellectual property rights should hit matter be discovered 
 ( see figure 5 for exemplars of substructures 
 in region e of figure 2b ) . in addition , when 
 screening against emerging target classes , the relevant chemical space 
 may not be well - defined and could be different from that which already 
 has extensive coverage in medicinal chemistry literature . therefore
, 
 these compounds with low similarity to currently known medicinal chemistry 
 space can be useful in offering more diverse fragment screening libraries .
exemplars 
 of substructures derived from ro3-compliant commercial fragments that 
 have low similarity to bioactive chembl compounds ( region e of the 
 venn diagram in figure 2b ) . using 2d pca , the chemical space described by 16 
 descriptors illustrates uneven distributions of the substructures 
 generated , even though the substructures derived from chembl compounds 
 and those derived from ro3-compliant commercial fragments occupy a 
 largely similar chemical space ( figures 3a , 3c , and 3f ) . in agreement 
 with previous results ,
substructures 
 with increasing aromaticity have the highest representation in commercial 
 fragment substructures ( quadrant four , figures 3c and 3d ) .
the lowest representation in commercial 
 fragment substructures corresponds to chemical space with increasing 
 polarity ( quadrant three , figures 3c and 3d ) .
we speculate that one possible reason for the 
 low representation of polar compounds could be attributed to less 
 robust procedures in the chemical synthesis or purification of polar 
 compounds .
this implies that , when designing fragment screening libraries , 
 focusing on the substructures in the commercial fragments set common 
 to , or those have high similarity to , the bioactive chembl substructures 
 ( regions a and d , respectively , figures 2b 
 and 3d ) as we recommend here , the coverage 
 of polar substructures relevant to known bioactive medicinal chemistry 
 space could remain low .
figure 6 shows exemplars 
 of polar substructures with low similarity to commercial fragments 
 ( region b of the venn diagram in figure 2b ) . 
 given that among the polar substructures , those derived from active 
 compounds are enriched over those derived from inactive compounds 
 ( quadrant three , figure 3j ) , it is desirable 
 to increase the sampling of polar chemical space . with limited availability 
 from commercial vendors , we recommend that fragments containing polar 
 substructures should be of a high priority when devoting synthetic 
 resources to create new fragments since this will complement commercial 
 fragment coverage to improve the chemical space sampling of fragment 
 screens .
exemplars of polar substructures derived from bioactive chembl compounds 
 that have low similarity to commercial fragments ( region b of the 
 venn diagram in figure 2b ) .
the distributions of substructures derived from 
 bioactive chembl compounds and those derived from inactive compounds 
 are both uneven .
although these two subsets from chembl cover a largely 
 similar chemical space ( figure 3e ) , the difference 
 and enrichment plots demonstrate the uneven distributions of the two 
 subsets across the chemical space ( figures 3i and 3j ) . according to the enrichment plot 
 ( figure 3j ) , the chemical space corresponding 
 to increasing three - dimensionality , and
also regions with increasing 
 polarity , display higher - than - average enrichments of biologically 
 active substructures ( respectively quadrants two and three , figure 3j ) .
such observation suggests that substructures 
 with increasing three - dimensionality and/or increasing polarity could 
 more likely deliver biologically active compounds , supporting recent 
 literature recommendations for improving fragment sampling in three - dimensional 
 space .
however , it should be noted that the population of 
 substructures within many of these enriched cells is evidently lower 
 than the average population ( see density plot in figure 3f ) .
therefore , the enrichment for bioactive compounds within 
 these cells might be overestimated given the low number of substructures 
 present .
improved sampling of substructures with increasing three - dimensionality 
 and/or increasing polarity will be required to provide a better understanding 
 of the true enrichment of substructures within this chemical space .
the coverage of known medicinal chemistry space by commercial fragments 
 was evaluated to elucidate the sampling of chemical space in fragment 
 screening .
this analysis points to strategies which enrich representation 
 of substructures relevant to medicinal chemistry and to promote appropriate 
 sampling of chemical space .
the analysis of substructures generated 
 from chembl compounds and ro3-compliant commercial fragments suggests 
 that the overlap between the two data sets is low . to ensure initial 
 confidence in discovering hit matter using commercial fragments ,
a 
 subset of the commercial fragment substructures constituting 11000 
 substructures ( regions a and d in figure 2b ) 
 that have close similarity to bioactive compounds in known medicinal 
 chemistry space should be used since these substructures have literature 
 precedence to exhibit biological activity . when expanding screening 
 libraries to include more diverse screening compounds , the remaining 
 commercial fragment substructures ( regions e and f in figure 2b ) that have low similarity to known medicinal chemistry 
 compounds may offer novel hits that have better potential to gain 
 intellectual property rights in addition to those similar to known 
 bioactive substructures . applying such strategies to the design of 
 new fragment screening libraries using commercial fragments
would 
 maximize the opportunity to discover fragment hits . when further 
 analyzing the distribution of the substructures in chemical space 
 using 2d pca , the chemical space characterizing substructures with 
 increasing three - dimensionality and/or increasing polarity apparently 
 display higher - than - average enrichments for bioactive substructures , 
 indicating that compounds with these substructures might be more likely 
 to display biological activity .
however , given the limited coverage 
 of polar substructures by commercial fragments , the most effective 
 way to enhance the sampling of polar chemical space in fragment screening 
 is to synthesize new fragments containing these substructures .
together 
 with the improvement of fragment sampling in three - dimensional space 
 that has already been initiated in various academic organizations , the true enrichment of polar and/or three - dimensional substructures 
 for bioactive compounds can be more thoroughly assessed .
the ring segments for individual compound were generated 
 by defining substructures as a ring system plus any polar functional 
 groups directly attached to a ring system .
polar functional groups were specified as nitrogen , oxygen , or sulfur 
 atoms directly attached to the ring system , carbonyl groups , or double 
 or triple bonded carbon atoms .
polar functional groups linking two 
 ring systems were retained in both ring segment substructures .
a mix 
 of pattern matching and rules , implemented in a python script using 
 the oechem toolkit ( openeye scientific software ) , was used to extract 
 the ring segments .
the 16 descriptors 
 were calculated using pipeline pilot professional client 8.0 ( accelrys , 
 inc . ) applying the definitions in the software , unless stated otherwise . 

all categorical descriptors with discrete unit values were normalized 
 relative to the number of heavy atoms , unless stated otherwise .
a heteroatom was defined as the elements s , o , or n. an unsaturated 
 bond was defined as a bond with a bond order greater than one .
an 
 sp - hybridized carbon atom was defined as any carbon atom 
 which has an atom hybridization of sp according to pipeline 
 pilot calculations .
the fraction of sp - hybridized carbon 
 atoms ( fsp3c ) was normalized relative to the total number of carbon 
 atoms in the same molecule .
all fingerprint 
 density descriptors were defined as the ratio between the number of 
 bits in the corresponding fingerprint generated and the number of 
 heavy atoms . for pmisum and pbf ,
one single 3d conformer was 
 generated for each individual substructure using corina ( molecular 
 networks gmbh ) .
the principal moment of inertia ( pmi ) was then calculated 
 using pipeline pilot and the sum obtained after normalizing the pmi 
 to the longest vector .
the 2d - pca plots were generated 
 using simca - p+ 13.0.0.0 ( umetrics ) .
the descriptor matrix was normalized 
 to unit variance before carrying out pca using the pca - x option under 
 standard settings .
the number of principal components was based on 
 automatic cross - validation within the software .
the difference and 
 enrichment plots were calculated using pipeline pilot 8.0 ( accelrys , 
 inc . ) .
the difference is defined as that between the normalized occupancy 
 of biologically active substructures ( activen ) and the normalized occupancy of inactive 
 substructures ( inactiven ) within each cell .
the enrichment is defined as the ratio of activen to the sum of activen and inactiven .
 analyzing 
 the chemical space coverage in commercial fragment screening collections 
 revealed the overlap between bioactive medicinal chemistry substructures 
 and rule - of - three compliant fragments is only 25% . 
 we recommend 
 including these fragments in fragment screening libraries to maximize 
 confidence in discovering hit matter within known bioactive chemical 
 space , while incorporation of nonoverlapping substructures could offer 
 novel hits in screening libraries . 
 using principal component analysis , 
 polar and three - dimensional substructures display a higher - than - average 
 enrichment of bioactive compounds , indicating increasing representation 
 of these substructures may be beneficial in fragment screening . 
Introduction
Results
Discussion
Conclusions
Materials and
Methods