Ligand-based virtual screening under partial shape constraints
Ligand-based virtual screening has proven to be a viable technology during the search for new lead structures in drug discovery. Despite the rapidly increasing number of published methods, meaningful shape matching as well as ligand and target flexibility still remain open challenges. In this work, we analyze the influence of knowledge-based sterical constraints on the performance of the recently published ligand-based virtual screening method mRAISE. We introduce the concept of partial shape matching enabling a more differentiated view on chemical structure. The new method is integrated into the LBVS tool mRAISE providing multiple options for such constraints. The applied constraints can either be derived automatically from a protein–ligand complex structure or by manual selection of ligand atoms. In this way, the descriptor directly encodes the fit of a ligand into the binding site. Furthermore, the conservation of close contacts between the binding site surface and the query ligand can be enforced. We validated our new method on the DUD and DUD-E datasets. Although the statistical performance remains on the same level, detailed analysis reveal that for certain and especially very flexible targets a significant improvement can be achieved. This is further highlighted looking at the quality of calculated molecular alignments using the recently introduced mRAISE dataset. The new partial shape constraints improved the overall quality of molecular alignments especially for difficult targets with highly flexible or different sized molecules. The software tool mRAISE is freely available on Linux operating systems for evaluation purposes and academic use (see http://www.zbh.uni-hamburg.de/raise).
KeywordsLigand-based Virtual screening Molecular similarity Structural alignment 3D similarity searching Lead discovery Partial shape User-defined constraints
Traditionally, ligand-based virtual screening (LBVS) belongs to the most widely applied tools in computer-aided drug design . Recently, we introduced a new method based on the RApid Index-based Screening Engine (RAISE) [2, 3] named mRAISE  and evaluated its capability for virtual screening. Herein, we compared its ability to separate active ligands from a set of decoys for certain targets to a variety of other state of the art methods like ROCS , ShaEP , MolShaCS , LIGSIFT  and Align–It . The overall performance measured by the area under the ROC curve (AUC) as well as the enrichment of actives at early percentages of top ranking hits placed mRAISE at the top ranks of all compared methods.
In a second experiment the quality of the three-dimensional alignments of molecules has been validated on the new mRAISE dataset specially created for this purpose. This dataset consists of 182 prealigned ligands for eleven diverse targets extracted from high-resolution PDB structures . mRAISE is able to calculate alignments with an RMSD of less than 2 Å among the top ten ranked hits for 80.8% of all pairwise alignments. Furthermore, it achieves median RMSD values of less than 2 Å for eight of the eleven target groups.
In its previous release, the methods used in mRAISE already tried to address some of the ongoing challenges in LBVS like the dependency on high quality molecule conformations and the lack of methods performing partial shape matching. This was done by using the so called partial bulk method , which allows to only consider certain parts of the included steric description of the surrounding structure while comparing two RAISE descriptors. Nevertheless, especially the alignment validation showed remaining difficulties during the comparison of molecules, which are highly flexible or significantly diverging in size.
In the following we want to introduce and analyze new methods for partial shape matching in mRAISE. Recently, there has been an increased interest in the combination of LBVS and SBVS methods in order to exploit all the available information and enhance the potential screening success [12, 13, 14, 15, 16, 17, 18]. Following this trend, the new partial shape constraints in mRAISE can be either manually constructed by an experienced user or automatically derived from protein–ligand complexes. Constraints derived from protein–ligand complexes are either used to search for similar small molecules with a higher chance to fit into the binding site when aligned to the query structure or to maintain close contacts to the protein surface. Validation on the Directory of Useful Decoys (DUD)  and the Directory of Useful Decoys Enhanced (DUD-E)  datasets show, that while not superior on average, the new constraints can significantly improve the screening performance on certain targets. We further evaluate the quality of the calculated molecular alignments using the mRAISE dataset . The introduced knowledge-based shape constraints demonstrate their benefits especially during the comparison of highly flexible molecules or molecules that differ greatly in size.
We recently introduced mRAISE , a new method for LBVS based on the RAISE approach representing molecules as three-point pharmacophore descriptors with a canonically oriented shape description. mRAISE achieves efficiency by employing an index-based database based on compressed bitmap indices . Such a prepared index can be repetitively screened using descriptors calculated for any query molecule.
During the screening process, all attributes of a query descriptor are compared with those of the descriptors stored in the index using SQL-like database queries. Each pair of matching descriptors represents a possible structural alignment of the respective molecules, which is scored in mRAISE using atom centered Gaussian functions combined with weights based on matching and mismatching physicochemical properties.
A detailed description of the RAISE descriptor and its adaptations for LBVS can be found in the previous publications of mRAISE  and cRAISE . In short, a RAISE descriptor is a three-point pharmacophore triangle with an additional description of the surrounding molecular surface (see Fig. 1).
Each descriptor includes coordinates of the three triangle corners, which can be of type hydrogen bond donor, hydrogen bond acceptor or hydrophobic, as well as the possible interaction directions in case of polar interactions. Additionally, the descriptor contains a rough description of the surrounding shape in form of the lengths of 80 equally distributed rays measuring the distance from the triangle center to the molecular surface. All rays are defined with respect to a canonical local coordinate system such that a ray-by-ray comparison is made possible. These rays are of great importance for the definition of partial shape constraints on query descriptors and will be utilized in the following. For the progression of this work, this part of the descriptor will be referred to as bulk rays. As in other RAISE applications, tolerances of ±1.0 Å are allowed during the comparison of triangle side lengths as well as bulk ray lengths.
Individual shape queries
An important difference between methods based on RAISE, is the comparison of the bulk rays during the screening procedure. On the one hand, mRAISE  and TrixP  search for similarity in the screened structures and therefore match rays of equal lengths. On the other hand, iRAISE  and cRAISE try to fit ligands into protein binding sites which means that ligand descriptor rays need to be shorter than those of a binding site descriptor.
Furthermore, in the case of similarity matching, the so called partial bulk matching has been used to incorporate structural flexibility, which only requires a certain percentage of adjacent rays to match. In the following, we will introduce new methods to incorporate additional information derived from protein–ligand complexes or manual selection into the sampling and comparison of specific fractions of the bulk rays in mRAISE. While queries derived from complexes make use of more information than just the ligand if available, the manual selection of important regions of the molecule enables chemists to guide the virtual screening process towards their individual preferences. This new concept of manual selected constraints allows a unique incorporation of expert knowledge to shape-based virtual screening.
To avoid misconceptions in the following, the previously published version of mRAISE will be referred to as mRAISE_classic.
Molecular interactions between a protein and its bound ligand require geometrically close contacts. Regions where such contacts occur are therefore of great importance for the activity of a ligand and of special interest during virtual screening.
In case a protein–ligand complex is available, contact information can be incorporated into the screening procedure. A new mode was implemented in mRAISE, which only uses bulk rays intersecting with the protein surface in a distance of up to 0.5 Å after leaving the surface of the molecule (see Fig. 2). In some cases, this method leads to triangles with very few used bulk rays. To prevent insignificant matches, descriptors with less than five used rays are excluded during screening. In the following, this mode will be referred to as mRAISE_contact.
If the target of a query structure of a LBVS campaign is known, the steric constraints implied by the binding site can in some cases be of more interest than the actual shape of just one binding ligand. Therefore, an inclusion query aims at finding matches that would fit into the same area of the binding site rather than being of roughly the same shape as the query ligand. Starting from a protein–ligand complex, the lengths of all rays of each query descriptor are extended to the distance where they would enter the binding site molecular surface, if this is within the maximum ray length (see Fig. 3). An exception is made if the van der Waals radii of the atoms overlap and therefore the ray would enter the binding site surface before leaving the molecular surface. In those cases the ray length remains the distance where the ray leaves the molecular surface. Subsequently, the query to the index is changed to match descriptors with shorter rays than the query descriptor. Again insignificant descriptors are excluded during screening. Since the binding site generally surrounds most parts of a molecule, only descriptors with 40 or more remaining rays are used. This mode will be referred to as mRAISE_inclusion in the following.
Manual atom selection
An important benefit of LBVS is its usability in cases without available high quality protein structures. Nevertheless, even in those cases a user might have experience-based perceptions concerning the regions of a molecule that are important for its activity. These might result from, for example, a structural overlay of multiple compounds active against the same target. Therefore, a fourth established method to define queries with mRAISE is the manual selection of ligand atoms via a graphical user interface. In this case, only those bulk rays that would pierce through the molecular surface corresponding to those atoms, will be matched during the screening procedure. In other words, a ray is only used if it ends within the van der Waals radius of a selected atom and is shorter than the defined maximal length (see Fig. 4). For the purpose of exhaustive screening runs, the constraints derived from a manual selection can be saved to SDF files alongside the query molecule and then be used in the command-line version of mRAISE.
This will not guarantee hits containing the selected substructure, but it will more likely provide structural alignments showing roughly the same shape as the query molecule in the selected areas. Like in the contact queries, descriptors with less than five selected rays are excluded during screening. For the remainder of this work, this mode will be referred to as mRAISE_selection.
To analyze the influence of the introduced methods on the results of virtual screening with mRAISE , the experiments of the previous publication were repeated with mRAISE_contact and mRAISE_inclusion. Screening runs were performed on the DUD as well as on its extended version (DUD-E). Additionally, a dataset to validate the quality of structural alignments introduced in the previous publication (mRAISE dataset) has been used.
Since the manual selection of atoms is highly dependent on the knowledge of the user and there is no obvious correct selection, the performance of mRAISE_selection is only shown for a few special targets as a proof of concept.
DUD: The DUD dataset  has been originally developed for the validation of docking methods and consists of 2.950 actives and specifically selected decoys for 40 different targets. For each active molecule in the dataset approximately 36 decoys have been selected based on similar physicochemical properties like molecular weight, the number of hydrogen-bond acceptors and donors, the logP value, and the number of rotatable bonds but with dissimilar topology. The data has been downloaded from http://dud.docking.org/.
DUD-E: The DUD-E dataset  is the enhanced version of the original DUD, containing 22.886 active compounds for 102 different targets with an average of 224 ligands per target and 50 decoys for each active. The data has been downloaded from http://dude.docking.org/.
mRAISE dataset: The mRAISE dataset  has been introduced to validate the quality of calculated molecular alignments. It contains 182 ligands for 11 diverse targets, which have been prealigned based on their mutual binding to identical binding sites using SIENA  with a maximal allowed backbone RMSD of 0.5 Å. All structures were taken from a high resolution subset of the PDB  and filtered to match certain molecular properties using MONA . Those properties included the number of heavy atoms and rotational bonds of the molecule as well as the number of actually overlapping atoms in the alignment. The data has been downloaded from http://www.zbh.uni-hamburg.de/mraise-dataset.html.
Retrospective studies on the DUD and DUD-E datasets rate the ability to separate active molecules from a set of decoys. Common evaluation and comparison metrics used for this purpose are the area under the ROC curve (AUC), as well as the enrichment factor (EF) and the hitrate (HR) at a certain percentage of ranked hits.
For the validation of the alignment quality, all pairs of molecules of the same ensemble in the mRAISE dataset are compared with each other. After the comparison of two molecules M and N, the calculated pose \(p'\) of M is then compared to the pose \(p''\) of M taken from the reference alignment by calculating the RMSD between both poses. As in the previous publication, the calculation of the RMSD is restricted to atoms of molecule M showing a maximum distance of 2.0 Å to any atom of N in the reference alignment. This excludes flexible regions of the molecules, which are not aligned in the binding site and therefore are not part of a conserved binding mode. In the following, this special RMSD is referred to as RMSD-O. During the screening runs, the query conformation is taken from the input file and is then compared to up to 250 generated conformations of a target molecule. To sum up the performance of each method, average and median RMSD-O values for each ensemble are used. It should be noted, that in case of comparisons with no single matching descriptor and therefore no RMSD-O value, this can not be represented in the average value. For the median value on the other hand, those pairs are handled as if they had an infinite RMSD-O.
Results and discussion
Average and median AUC values of the ROC curves on the DUD dataset
0.76 ± 0.19
0.70 ± 0.15
0.76 ± 0.19
All targets of the DUD and the DUD-E datasets have been screened using mRAISE_inclusion and mRAISE_contact for automated partial shape constraints as described above and are compared to the performance of mRAISE_classic.
The most apparent improvement is for the Progesterone receptor, which shows an increase of the AUC by 0.19 using mRAISE_inclusion. Further targets showing an improvement using this method are Factor Xa (+0.07), Thrombin (+0.07), Cyclooxygenase 1 (+0.06), Glutocorticoid receptor (+0.05), and Hydroxymethylglutaryl-CoA reductase (+0.05). Furthermore, the mRAISE_contact method shows superior performance on some targets with an improved AUC for HIV protease (+0.1), PDGF receptor (+0.08), and Tyrosine kinase SRC (+0.07).
Average enrichment factor on the DUD dataset at one five and ten percent of ranked hits
20.2 ± 12.1
9.4 ± 6.0
5.4 ± 3.0
19.3 ± 12.1
8.5 ± 5.4
4.8 ± 2.7
19.9 ± 12.3
9.5 ± 6.0
5.5 ± 3.1
Average hitrate on the DUD dataset at one five and ten percent of ranked hits
55.5 ± 33.3
46.7 ± 30.0
53.9 ± 30.6
53.0 ± 33.2
42.1 ± 26.5
48.0 ± 27.2
54.6 ± 33.9
47.3 ± 30.0
54.6 ± 30.5
Similar observations can be made looking at the average EF (see Table 2) and the average HR (see Table 3) for the DUD dataset at one, five and ten percent of ranked hits. For the early enrichment at one percent, mRAISE_classic is performing best with an average EF of 20.2 ± 12.1, followed by mRAISE_inclusion (19.9 ± 12.3), and mRAISE_contact (19.3 ± 12.1). Interesting is however, that the inclusion mode performs slightly better than the classic mode at the later enrichment stages.
Average and median AUC values of the ROC curves on the DUD-E dataset
0.74 ± 0.15
0.72 ± 0.16
0.72 ± 0.16
In the previous publication it has already been discussed that despite the comparably rare usage, the DUD-E dataset is actually better suited for validation studies on VS methods than the older DUD. This is due to the fact that the DUD-E covers more targets and at the same time includes more molecules per target and an even higher ratio of included decoys for each active. Furthermore the DUD-E got rid of some property biases that made the separation between actives and decoys easier if exploited in some cases. Detailed performances on all 102 targets using mRAISE_contact and mRAISE_inclusion can be found in the supporting information in Tables SI.3 to SI.5 and Tables SI.6 to SI.8.
Average enrichment factor on the DUD-E dataset at one, five, and ten percent of ranked hits
23.45 ± 17.00
7.78 ± 4.92
4.69 ± 2.50
22.67 ± 17.10
7.37 ± 4.96
4.46 ± 2.53
22.76 ± 17.04
7.45 ± 4.99
4.45 ± 2.51
Average hitrate on the DUD-E dataset at one, five, and ten percent of ranked hits
37.95 ± 26.36
38.94 ± 24.44
46.98 ± 24.78
36.79 ± 27.04
36.96 ± 24.60
44.66 ± 25.09
37.12 ± 27.14
37.37 ± 24.73
44.60 ± 24.93
As can be seen, both new modes achieve an average AUC of 0.72 ± 0.16 which is almost the same as the AUC of 0.74 ± 0.15 of mRAISE_classic. However, the median AUC values of mRAISE_contact (0.76) and mRAISE_inclusion (0.75) are slightly higher than the value of mRAISE_classic (0.73).
The values for the average EF and HR at one, five, and ten percent of ranked hits show the same trend as the average AUC with mRAISE_classic slightly exceeding the performance of mRAISE_contact and mRAISE_inclusion on average. While the overall performance of all modes is therefore comparable, individual cases highlight the strength of each mode and the benefit of the derived constraints for virtual screening.
List of targets of the DUD-E which show an increased AUC using mRAISE_contact or mRAISE_equality in comparison to mRAISE_classic
As can be seen, for the DUD as well as the DUD-E there are a lot of targets which profit from the new complex-derived constraints while the average and median performance is similar to mRAISE_classic. The comparable performance of the three methods can to some degree be expected since the diversity of the included compounds per target in DUD and DUD-E is limited. Nevertheless it is of high interest to investigate the cases in which partial shape could enhance the performance of the LBVS. On the smaller DUD dataset no clear coherence between molecular properties and a change of performance could be found. The 102 targets of the DUD-E, however, showed an interesting connection between the average amount of rotatable bonds present in the active molecules and the benefit of partial shape constraints. The most apparent improvements increasing the AUC by 0.1 or more using both mRAISE_contact and mRAISE_inclusion all occurred on highly flexible molecule classes which had an average of eight or more rotatable bonds present in the active compounds. The only exception here are the ligands of the Mineralocorticoid receptor (mcr) which show and increased AUC by 0.11 using mRAISE_contact and only has an average of 4.4 rotatable bonds among the actives. To further highlight this observation, the percentages of targets with an improved or equal performance compared to mRAISE_classic with at least eight, nine, or ten average rotatable bonds among their actives can be seen in Table 8.
Percentage of DUD-E targets with equal or improved performance compared to mRAISE_classic and a certain number of rotatable bonds
Average number of rotational bonds
Number of targets
mRAISE_classic achieves an average RMSD-O of less than 2.0 Å for four ensembles and a median RMSD-O of less than 2.0 Å for eight ensembles within the ten best ranked hits. In comparison, the contact and inclusion modes achieve median RMSD-O values of less than 2.0 Å for nine of the eleven ensembles. Looking at the average RMSD-O values, mRAISE_inclusion achieves a value smaller than 2.0 Å for seven cases and mRAISE_contact succeeds in six cases. This alignment quality can, to a certain degree, already be observed only considering the best scored conformation (Top 1). Looking at the average values, mRAISE_classic achieved an RMSD-O of less than 2.0 Å in four cases while the new modes are only able to achieve this in three cases. However, looking at the less outlier-dependent median values, mRAISE_classic shows only five ensembles with a median RMSD-O of less than 2.0 Å while the other modes achieve this in seven cases.
Comparison of the different methods on the mRAISE dataset
Matrix metalloproteinase-12 (MMP-12)
CDK 2 Kinase
Carbonic Anhydrase II
Bromodomain-containing protein 4
Isopenicillin N Synthase
Comparison of the different methods on the mRAISE dataset
Matrix metalloproteinase-12 (MMP-12)
CDK 2 Kinase
Carbonic Anhydrase II
Bromodomain-containing protein 4 (BRD4)
Isopenicillin N Synthase
Looking at the Thermolysin ligands, the main reason for the average RMSD-O of 2.67 Å using mRAISE_classic is the comparably small ligand of the protein structure 3QGO. It only consists of 13 heavy atoms, which is half the number of the next smallest molecule. An overview of all ligands of the ensemble can be seen in the supporting information Figure 1. It has been shown in the previous publication , that the phenyl ring of the molecule can be superimposed onto a respective ring contained in most of the other molecules which is highly preferable concerning the maximization of shape overlap but at the same time contradicts the actual binding mode of the molecule and would not place it in the binding site at all. mRAISE_classic therefore tends to misplace the ligand and the alignments result in RMSD-O values greater than 10 Å in six out of the nine superpositions with this ligand at first rank. However, using the partial shape constraints derived from the binding site greatly improves the placement of this ligand as can be seen in the lower average RMSD-O values of 1.83 Å using mRAISE_contact and 1.76 Å using mRAISE_inclusion. For all of the six cases showing an RMSD-O greater than 10 Å at first rank for mRAISE_classic, mRAISE_inclusion achieves RMSD-O values of less than 2.71 Å already at first rank. On the other hand, mRAISE_contact also achieves values of less than 2.71 Å for five of the six cases with only one outlier showing an RMSD-O of 4.64 Å.
The ligands of the HIV protease are another example for improved alignment quality using binding site derived partial shape constraints. The difficulty concerning this ensemble is the high flexibility of its ligands, with nine of ten molecules having 12 or more rotatable bonds. The smaller median and average RMSD-O values of mRAISE_inclusion and especially mRAISE_contact show that the dependency on the conformational quality is reduced using the contact shape constraints. This is due to the fact that mRAISE_contact only requires shape similarity for special shape features and simultaneously allows more flexibility in other regions. The improved handling of the HIV protease ligands could already be seen on the DUD and DUD-E studies (see supporting information Tables SI.1 and SI.4). The experiments on the DUD-E also suggest a generally improved handling of highly flexible compound classes with partial shape constraints.
Percentage of pairs with an RMSD-O smaller than a certain threshold
Percentage \(\le\) 2.5 Å
Percentage \(\le\) 2.0 Å
Percentage \(\le\) 1.5 Å
Another way to look at the results is to calculate the overall percentage of pairwise comparisons achieving a certain RMSD-O value. Table 11 shows the percentage of comparisons achieving RMSD-O values of less than 2.5, 2.0 and 1.5 Å for the respective modes. It can be seen that the mRAISE_inclusion performs slightly better than all other modes with 87.8% of comparisons showing an RMSD-O of less than 2.5 Å, 81.1% showing and RMSD-O of less than 2.0 Å and 65.7% showing an RMSD-O of less than 1.5 Å. While mRAISE_contact shows slightly inferior values to the original method here (see Table 11), the higher count of ensembles achieving median and average RMSD-O values of less than 2.0 Å (see Tables 9 and 10) shows however that this method produces less outliers with high RMSD-O values. The performed experiments highlight the fact that there is no single method providing the best alignments for all ensembles. Most of the time one has to try multiple methods or use a combination of them in order to get the best possible performance. mRAISE now offers multiple different approaches to make use of all available information in order to improve the results in any given scenario.
Overview of the five selected DUD targets for the mRAISE_selection experiment
Avg number of
The manual selection of atoms to define partial shape constraints is a very promising but also difficult endeavor. Since an ideal selection of important regions of a molecule requires good knowledge of the target or the compound class one is interested in, an objective validation of this method is not trivial. In the following we will show some examples for manual partial shape constraints based on a selection of atoms to guide the screening procedure.
AUC values of mRAISE with and without aid of manual selection
Table 13 shows the performance of mRAISE_selection in comparison to mRAISE_classic. As can be seen, the manual selection of steric constraints improved the performance for all five targets.
These results highlight the power of knowledge-based manually selected constraints on the performance of LBVS, especially on otherwise difficult highly flexible compounds. The quality of the results hereby depends on the actual selection of atoms and therefore on the knowledge of the user. It should also be noted that a better suited selection of atoms could improve the results even further in any given case.
We introduced new methods to create knowledge-based partial shape constraints for virtual screening with mRAISE. These constraints can either be created by a manual selection of atoms in important regions of a molecule or they can be automatically derived from protein–ligand complexes if the respective information is available. Complex-based constraints either try to search for hits that might form the same close contacts to the protein as the query molecule or they aim on finding molecules that have a higher chance to fit into the binding site when superimposed onto the query molecule.
The influence of these new strategies on the screening performance as well as on the quality of molecular alignments has been evaluated and compared to the original performance of mRAISE. With an average area under the ROC curve of 0.76 ± 0.19 for mRAISE_inclusion and 0.70 ± 0.15 for mRAISE_contact, the average performance for all targets of the DUD is comparable to the original version. Similar observations were made using the DUD-E dataset, with an average AUC of 0.72 ± 0.16 for both mRAISE_contact and mRAISE_inclusion in comparison to 0.74 ± 0.15 using mRAISE_classic. However, one could see multiple examples for screenings with partial shape constraints to improve the performance on multiple individual targets.
Further, looking at the quality of calculated molecular alignments highlights the benefits of the new constraints. Here, the overall performance increased with mRAISE_contact achieving an average RMSD-O of less than 2.0 Å for seven and a median RMSD-O of less than 2.0 Å for nine of the 11 ensembles within the top ten ranked hits and mRAISE_inclusion achieving one average RMSD-O of less than 2.0 Å less. Furthermore, the complex-based constraints improved the alignment quality especially for difficult cases with highly flexible molecules like in the HIV Protease ensemble or in case of an outlier of much smaller size than the rest of the ensemble like in the Thermolysin ensemble.
For the non-automatic manual selection of partial shape constraints a validation has been performed using five targets of the DUD dataset. An ideal atom selection requires detailed knowledge about the compound class or the respective target. The validation therefore focused on the most flexible and hence difficult targets, which had an inferior performance using mRAISE_classic. It could be shown that a good selection of atoms and the derived partial shape constraints were able to improve the screening quality in all five cases.
Overall the partial shape constraints proved to be a viable tool to assist LBVS especially in difficult cases with highly flexible compounds. This is the case for constraints derived from additionally available data of the protein–ligand complex as well as for the knowledge-based manual constraints defined by a user.
Next steps will focus on a further validation of the manual selection mode and on an application in real screening experiments with experimental validation of the results. For the validation of the manual selection mode one would require experienced users and challenging scenarios, nevertheless there seems to be a high potential in such studies. Furthermore, it would be interesting to sequentially use combinations of the described modes on the same library. Such a combination might lead to improved rankings compared to the performance of just one method alone. Since the dependency on high quality conformations seems to be reduced using the complex-based shape constraints, it would also be interesting to see if the new modes would perform equally well with less generated conformations.
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