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Strategies for 3D pharmacophore-based virtual screening
Drug Discovery Today: Technologies
Vol. 7, No. 4 2010
Editors-in-Chief Kelvin Lam – Harvard University, USA Henk Timmerman – Vrije Universiteit, The Netherlands DRUG DISCOVERY
TODAY
TECHNOLOGIES
3D Pharmacophore Elucidation and Virtual Screening
Strategies for 3D pharmacophorebased virtual screening Thomas Seidel1, Go¨khan Ibis1, Fabian Bendix1, Gerhard Wolber1,2,* 1 2
Inte:Ligand GmbH, Mariahilferstrasse 74B/11, 1070 Vienna, Austria Freie Universita¨t Berlin, Institute of Pharmacy, Pharmaceutical Chemistry, Computer-Aided Drug Design, 14195 Berlin, Germany
3D pharmacophore-based techniques have become one of the most important approaches for the fast
Section editor: Gerhard Wolber – Freie Universita¨t Berlin, Germany
and accurate virtual screening of databases with millions of compounds. The success of 3D pharmacophores
is
largely
based
on
their
intuitive
interpretation and creation, but the virtual screening with such three-dimensional geometric models still poses a considerable algorithmic and conceptual challenge. Most current implementations favor fast screening speed at the detriment of accuracy. This review describes the general strategies and algorithms employed for 3D pharmacophore searching by some current pharmacophore modeling platforms and will highlight their differences.
Introduction The basic concept of pharmacophores has been established since decades [1,2], and the use of pharmacophore modeling techniques as a tool for the discovery of novel drugs has become increasingly popular since the advent of computeraided structure-activity studies [3–9]. The official 1998 IUPAC definition [10] of a pharmacophore is ‘the ensemble of steric and electronic features that is necessary to ensure the optimal supramolecular interactions with a specific biological target structure and to trigger (or to block) its biological response.’ As often misunderstood in medicinal chemistry literature a pharmacophore is thus not the representation of a real mole*Corresponding author: G. Wolber ([email protected]), ([email protected]) 1740-6749/$ ß 2010 Elsevier Ltd. All rights reserved.
DOI: 10.1016/j.ddtec.2010.11.004
cule or a real association of functional groups, but a purely abstract concept that describes the common steric and electrostatic complementarities of bio-active compounds with the target of interest. To be a useful tool for drug design and show sufficient predictive power, 3D pharmacophore models (i.e. the concrete representation of 3D pharmacophores) must be able to describe the nature and location of the functional groups involved in ligand–target interactions, as well as the different types of non-covalent bonding and their characteristics in an uniform and for medicinal chemists easily to comprehend way. This is achieved by categorizing the fundamental types of observed ligand–receptor interactions into pharmacophoric features such as hydrogen-bond donors, hydrogen-bond acceptors, positively and negatively charged groups, and hydrophobic regions. As such, the pharmacophore concept is closely similar to that of bioisosterism [11–14].
3D pharmacophore models as filters for virtual screening The before mentioned feature types are interpreted and implemented by several pharmacophore modeling and virtual screening platforms such as Catalyst (Accelrys) [15–17], ¨ dinger) MOE (Chemical Computing Group) [18], Phase (Schro [19,20] and LigandScout (Inte:Ligand) [21,22]. Nevertheless, differences exist in the exact definition and placement of the features [23], which has the practical consequence that otherwise comparable pharmacophore models lead to a high degree of variation among the obtained screening hit-lists [24,25]. e221
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Figure 1. Example of a shared feature pharmacophore model that was generated by LigandScout [21] from a set of three CDK2 inhibitors (LS2, LS3, and LS4) in their binding conformation (PDB-codes: 1ke6, 1ke7, and 1ke8). The yellow spheres represent hydrophobic features, red arrows hydrogen-bond acceptor and green arrows hydrogen-bond donor features.
Besides the basic type of the observed ligand–receptor interactions, another key information that needs to be incorporated into a proper pharmacophore model is the threedimensional location of the interactions (with a certain tolerance) and, if an interaction is directed (such as hydrogen bonding), also its spatial orientation (see Fig. 1). Because of their abstract nature and simplicity, 3D pharmacophore models represent efficient filters for the virtual screening of large compound libraries [26]: (i) the computational complexity of the hit identification process is greatly reduced by the sparse pharmacophoric representation of e222
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ligand–target interactions which results in low overall search times, and (ii) pharmacophore-based queries allow to find novel drug candidates with different scaffolds and functional groups than the original ligands used for the modeling of the pharmacophores [27]. This is of special interest for pharmaceutical companies that want to avoid patent infringement issues or need to find new lead candidates with better ADMETox properties and/or higher activity towards the target [6]. However, the simplicity of pharmacophoric representations inevitably also means that they cannot explain the complete biophysical nature of drug interactions. An under-
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Figure 2. 3D Pharmacophore-based virtual screening workflow.
standing of the limitations of the concept is therefore essential for its successful application.
Virtual screening workflow The virtual screening of compound libraries is a process that can be divided into several, well-defined steps. Figure 2 shows the typical workflow of a 3D pharmacophore-based virtual screening campaign [6,17,28,29] which will serve as the anchor point for the discussions in the next few sections. Other screening approaches like descriptor-based methods [30], 2D fingerprint checks [31–34], or methods based on 3Ddocking [35] are not in the scope of this review.
3D pharmacophore model creation The first step in a 3D pharmacophore-based virtual screening workflow is to create a query pharmacophore model that specifies the type and geometric constraints of the chemical features that need to be matched by the screened molecules. There are two different strategies to derive pharmacophore models: structure- [36] and ligand-based [3] approaches. The structure-based method determines chemical features based on complementarities between a ligand and its binding-site. This approach requires structural information about the macromolecule and the active conformation of the binding ligand. Structure-based pharmacophore models bear the www.drugdiscoverytoday.com
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advantage that information about the directionality of binding-site interactions can be incorporated – often resulting in highly restrictive models with orientation constrained features [7]. If the 3D structure of the macromolecule is not available, 3D pharmacophore models can be derived in a ligand-based way by the perception of chemical features in common to a set of ligands (training set) which are known to show the desired biological activity towards the target. The latter method delivers good results if enough ligand information is available, and the training set molecules are known to bind to the protein at a specific location [37].
Annotated database creation An important aspect that needs to be considered when screening molecule libraries against 3D pharmacophore models is conformational flexibility. Most major software applications deal with this problem by creating dedicated screening databases that store pre-computed conformations for each of the molecules. Another approach is to tweak the conformation of the molecules on-the-fly in the pharmacophore fitting process [38]. The advantage of the latter approach is the lower storage requirements. However, it also has the important disadvantage that the screening process is considerably slower and a dramatic reduction of the conformational search space while aligning bears the danger of falling into a local minimum [7,39]. Nowadays enough hard disk storage is available and screening databases with pre-generated conformations are clearly preferred. These databases are usually generated once and can be reused whenever needed which results in a considerable speed-up of the overall screening process.
Database searching The database search is most commonly implemented as a multistep filtering process. First, a fast pre-filtering is applied where compounds are eliminated based on feature-types, feature-counts, and quick distance checks. Second, 3Dmatching algorithms are used, which are normally slower but more restrictive.
Pre-filtering Because the actual three-dimensional overlay of query pharmacophore models and molecules is the time-limiting step in the screening process, pre-filtering is of utmost importance [28]. The aim of pre-filtering is a quick identification and elimination of all molecules that cannot be fitted to the query pharmacophore model in 3D. Only those molecules that pass the filters need to be processed in the final accurate, but computationally expensive 3D alignment step. Descriptorbased similarity methods [40] seem to be appropriate filters because little information is needed, similarity calculations are fast, and the implication of biological similarity from structural similarity is generally valid [41]. Feature-count e224
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matching is a (0D descriptor-based) very simple, but nevertheless effective filtering method that can shift out a large fraction of the database molecules (depending on the complexity of the query) [7]. If feature-counts are determined for the query pharmacophore model and pre-calculated for the database molecules, then only molecules that have the same (or higher) feature-count as the query need to be provided to the time-consuming matching step. Another medium complex method in terms of 3D pharmacophore database searching is the concept of ‘pharmacophore keys’ [42]. Such keys provide a binary representation of a molecule taking into account conformational flexibility and pharmacophoric features. By binning inter-feature distances between any two, three or four features, the fingerprint yields a fixed size and each bit represents one possible 2-point, 3-point or 4-point pharmacophore. As a result, the screening becomes a simple intersection test to identify molecules that do not satisfy the query. Similar approaches used in currently available software applications following this concept with slight modifications include integration of feature tolerance sampling, different feature definitions, varying binning constraints, and usage of hashing instead of binning. For instance, Schrodinger’s program Phase applies a single user-defined tolerance to each inter-feature distance of the query to eliminate k-point pharmacophores that have nothing in common [43]. Because of the nature of their distance partitioning algorithm [20] the tolerance should be set to be at least twice the binning size of the binary partitioning tree to be able to produce self matches. Most programs also include filters that potentially discard molecules that mathematically could fit the query pharmacophore model, but this loss in filtering is accepted for the benefit of higher efficiency. Other programs, such as LigandScout, strictly apply lossless filters that guarantee that all of the discarded molecules are not able to geometrically match the query, which results in geometrically more accurate virtual screening results. In summary, pre-filtering is intended to clip out certain parts of the overall search space in favor of speed, but this process of increasing complexity is constrained to maintain the overall quality of the screening outcome: gain higher enrichment and/or find novel scaffolds [37].
Matching of 3D pharmacophore models Once all database molecules that might match the query have been identified, their conformations need to be examined more closely to see if they are able to match the spatial arrangement of the query features. Special care must be taken in this step because the final decision has to be made whether to reject a database compound or to put it in the hit-list and, thus, directly determines the quality of the obtained screening results.
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Table 1. Comparison summary table. Abbreviations: hydrogen-bond acceptor features (HBA), hydrogen-bond donor features (HBD), positive ionizable features (PI), negative ionizable features (NI), aromatic features (AR), hydrophobic features (H), metal-binding features (MB), exclusion volume spheres (XV), and inclusion volume spheres (IV) Catalyst
Phase
LigandScout
MOE
Conformer generation and flexible search
Pre-generation necessary, flexible tweaking supported
Pre-generation optional, lower screening speed if generated on-the-fly
Pre-generation and annotation necessary
Pre-generation necessary, annotation optionally on-the-fly
Alignment strategy, geometric accuracy and scoring
Feature tolerances are averaged, geometric incorrect alignments can also be scored and occur in hit-list
Decision tree with feature independent distance tolerance, least squares procedure, scoring by contributions from feature point alignments and geometric contributions
Pattern matching alignment algorithm, high geometric accuracy due to full tolerance sub-sampling, scoring by feature point alignments and optionally volume overlap
Clique detection with tolerance checking using a low-level state machine, scoring by feature RMSD
Exclusion volume sphere interpretation
Heavy atoms only with atom radii
Heavy atoms and hydrogens with atom radii
Heavy atoms and hydrogens with atom radii
Heavy atom centers without radii
Supported feature types
HBA, HBD, H, AR, PI, NI, XV, custom feature definitions possible
HBA, HBD, H, AR, PI, NI, XV, up to three custom feature definitions via SMARTS-patterns
HBA, HBD, H, AR, PI, NI, Zn/Mg/Fe MB, XV, open feature definition format (XML/SMARTS)
Depends on selected pharmacophore scheme, IV and XV supported
References
[16,23,24]
[20,24]
[22,24,39]
[24,49]
The geometric alignment of the query pharmacophore model to a single molecule conformation can be reduced to the problem of finding a suitable subset of the features of the database compound that fulfills all n-point distance combinations of the query. Computationally greedy solutions for this problem have been proposed relatively early and range from three-dimensional maximum clique detection algorithms [44] as used in DISCO [45,46] to the sequential buildup of increasingly larger common feature configurations as employed in Catalyst/HipHop [47]. However, pure feature-pair distance comparisons (2-point pharmacophores) alone cannot even distinguish between a pharmacophore and its mirror image [16] and an actual overlay in 3D space is required to be able to correctly identify a match to the query within the defined feature tolerances. This overlay is also necessary to check and/or score additional constraints imposed by vector features like hydrogen-bond acceptors/donors, plane features like aromatic rings and exclusion/inclusion volume spheres. Commercial software packages for pharmacophore modeling that incorporate state-of-the-art screening functionality like Catalyst, Phase, MOE and LigandScout thus all perform some sort of geometric alignment in the 3D pharmacophore matching step, which is usually done by minimizing the RMSD between associated feature pairs [48]. While all other programs implement a search in increasing n-point distances, LigandScout uses a sophisticated pattern-matching technique to identify an initial alignment resulting in lower restrictions regarding the number of features in the query pharmacophore model. Although the general strategies for hit identification are similar, they differ in various details which range from the
handling of conformational flexibility and interpretation of query feature constraints to the customization of search parameters. The following sections attempt to provide a short overview (see also Table 1): Catalyst: In addition to the standard screening mode that rigidly fits conformations to a 3D pharmacophore model, Catalyst offers a Best Flexible Search mode that can tweak the examined conformation of a molecule to enforce a fit within a given energy threshold. A database search starts with a rapid pre-filtering process that screens out molecules that do not possess properties required for potential hits. In the next step of the search procedure, Catalyst tries to match the conformations of each compound with the features of the query pharmacophore model in 3D space. In this fitting step, Catalyst checks two sorts of constraints [16]: (a) The RMS deviation must be less than one, that is vffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi u X u1 distðQ i ; Ai Þ 2 t <1 N 1iN tolerancei where N is the number of superimposed query feature-/ ligand-point pairs, Qi is the position of the ith query feature, Ai is the position of the aligned ligand-point, and tolerancei is the radius of the tolerance sphere around Qi. (b) The distance between each pair of features of the database molecule must be such that it is possible to align them within the specified tolerances of the mapped query features. The form of the first criterion has the consequence that hits may be reported where some of the aligned database molecule feature points are outside of the specified query feature www.drugdiscoverytoday.com
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tolerance spheres. This effect was also observed by Spitzer et al. in their comparative study of screening results obtained by Phase, MOE and Catalyst [24]. Once a hit-list has been obtained, Catalyst provides the possibility to compute fit values that can be used for scoring and ranking of the screening results. Phase:3D pharmacophore searching in Phase [20] does not require a dedicated screening database (although supported for faster processing) and supports standard and on-the-fly searching modes. In standard mode, the query pharmacophore model is matched against a set of pre-computed conformers for each molecule in the database or flat file. When searching on-the-fly, conformers are generated in memory using a built-in torsion search method. The primary method of matching applies a single user-defined tolerance to each inter-feature distance in the query pharmacophore model and discards all database molecules whose pharmacophore features fail the distance constraint checks. If it is sufficient to match only a subset of m out of k sites, Phase offers the possibility to restrict subsets to include or exclude specific sites. Matching a particular subset of m sites requires that the user-defined distance tolerance must be satisfied for all m(m 1)/2 inter-site distances in that subset. When a molecule produces a match, the applicable site points are aligned to the pharmacophore model using a standard least-squares procedure. If desired, query feature tolerances may be applied at this stage, so that a match will be eliminated if any single feature in the matching molecule deviates from the corresponding query feature point by more than the associated positional tolerance. Positional tolerances are defined independently from the inter-site distance tolerance, so if matching is to be governed purely by the former, the distance tolerance must be at least twice the largest positional tolerance. Matches for a particular molecule are sorted by decreasing fitness, and the user has the option of retaining any number of high-ranking matches for each molecule. When matches are found, the aligned conformers are placed in a hit-list sorted by decreasing fitness. When the query contains excluded volumes, any matches will be checked for clashes before they get added to the hit-list. The total size of the hitlist can be limited and in addition to applying positional tolerance and exclusion volume filters, a lower limit for the vector score or volume score of matching database molecules can be specified. The volume score threshold then acts as a filter that forces hits to resemble the reference ligand in overall size and shape. MOE: In MOE [49], molecules are stored in databases with their associated pre-computed conformations. No new conformations are generated during a database mining experiment. Pharmacophore models of the database molecules (its features are called ‘annotation points’ in MOE terminology) can be pre-calculated for faster processing or generated one226
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the-fly. Each annotation point contains multiple flags indicating the presence or absence of pharmacophoric features. To match each molecule to the query pharmacophore model, the search algorithm aligns the molecule to the query using rigid-body superposition. No flexible adjustment of the rotatable bonds is done during the search. Each match is represented as a mapping between two sets of points: the database molecule annotation points and the query features. A rigidbody transformation of the molecule brings the matched annotation points inside the tolerance radii of the corresponding query features. When performing a systematic matching, all possible matches of the database molecule and the query are systematically examined. The search then outputs all matches that satisfy the constraints of the query. Partial matching offers that only those features of the query which are marked as ‘essential’ must be matched by a corresponding ligand annotation point. Other query features may be left unmatched, resulting in a partial match. The search outputs all partial matches of the query that contain at least a given minimum number of matched features or do not exceed a specified maximum number of unmatched features. MOE permits to focus the query by the addition of a set of volumes, each being a sphere or union of spheres defining a volume of space in which matching atoms can be either excluded, required or allowed. Only those database compound conformations whose atoms match the associated SMARTS expression and lie within the allowable regions will then be output by the search. Each query feature can be associated with an expression that determines which features must or must not be present at a ligand annotation point for a successful match to the query feature. Additional feature group constraints may be added to further restrict the set of acceptable matches. LigandScout: LigandScout operates on pre-computed conformations and pharmacophore models of the screening dataset molecules that are stored in a dedicated database for fast processing. All conformations of a database molecule that might match the query pharmacophore model (that is, all that survive preliminary feature-count, two-point and three-point pharmacophore checks) are examined in turn and an attempt is made to superimpose them with the query pharmacophore model employing the pattern matching algorithm described in [39]. The geometric accuracy of LigandScout’s pharmacophore alignment algorithm reaches a higher level than in other programs because an aligned database pharmacophore model is considered as matching only if all of the fitted feature points lie within the individual tolerance spheres of the query features. Furthermore, angle constraints of vector and plane features (which are user customizable) have to be fulfilled and no clashes between exclusion volume spheres and atom Van der Waals spheres must be present. If all constraints of the query pharmacophore model are fulfilled, the search for a matching molecule
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conformation terminates and a hit is reported or, if desired, the search is continued to find a better matching conformation. Partial matches are possible by allowing several query features to be omitted during the pharmacophore matching process. Omitted features can be specified directly by marking certain features as optional or by providing a maximum omitted feature count. A unique feature of LigandScout that is not found in any of the other software packages is the possibility to specify multiple query pharmacophore models at once which can be linked together in the form of boolean expressions. For the scoring and prioritization of hit molecules, LigandScout provides several scoring functions that range from alignment RMSD scores to molecular volume overlap scores.
database molecules (not including their conformations). EF ¼
Ya A=N
Goodness of Hit-list (GH) combines Sensitivity, Specificity, and Yield of Actives and is therefore a very useful measure that considers both the true actives ratio and the true inactives ratio. The Goodness of Hit-list is defined as the weighted sum of Ya and Se multiplied with Sp. The quantity of active compounds is usually weighted higher than that of actives in ¨ ner and Henry [3] weight the the hit-list. For example, Gu Yield of Actives with 3/4 and the Sensitivity with only 1/4. Thus, a high value of GH can only be achieved with a high value of actives and a low false-negative ratio at the same time.
Hit-list analysis Hits retrieved from the 3D database search are a good starting point for the validation and refinement of the pharmacophore model. There are several useful measures [3,25,28,29] which are described in more detail below. Sensitivity (Se) is the ratio of the retrieved true positive compounds TP to all active compounds in the database, which is the sum of TP and the number of false negative compounds FN. Sensitivity values can range from 0 to 1, where Se = 0 means that the search did not find any of the actives in the database and Se = 1 means that the search returned all active compounds. Se ¼
TP TP þ FN
Specificity (Sp) is the amount of rejected truly negative compounds TN divided by the sum of TN and the number of retrieved false positive compounds FP. Specificity ranges from 0 to 1 and denotes the percentage of truly inactive compounds. Sp = 0 means that none of the inactive compounds could be identified as such and Sp = 1 means that all inactive compounds have been correctly rejected during the screening process. Sp ¼
TN TN þ FP
Yield of Actives (Ya) is a measure that shows the amount of the retrieved truly active compounds TP in relation to the size of the hit-list n. The Yield of Actives can for example be used to compare hit-lists retrieved for databases created with different conformer sampling techniques [25]. Ya ¼
TP n
Enrichment Factor (EF) measures the Yield of Actives proportionally to the ratio of actives in the database, where A is the amount of actives in the database and N is the total number of
w1 þ w2 ¼ 1 w1 > w2
GH ¼ ðw1 Ya þ w2 SeÞ S p
A modern tool for the assessment of screening results is Receiver Operating Characteristic (ROC) curves [50,51]. The ROC curve displays the increase of false positives that results with increased true positives. The Y-coordinate of the ROC curve represents the true-positive rate, whereas the X-coordinate denotes the appropriate false-positive rate. An ideal curve would rise vertically along the Y-axis until it reaches the maximum true positive rate, which is 1, and then continues horizontally to the right, which means that the hit-list contains all active compounds in the database and that no one of the hits is a false positive. The ROC curve of a random database search is represented by the median.
Pharmacophore model refinement On the basis of an analysis of the hit-list with the above measures and tools, the pharmacophore model is often refined to achieve more satisfying results. Adaption of feature definitions, modification of feature tolerances, addition or removal of features and exclusion volumes are some of the adjustments that can help to tune a pharmacophore model. Another possibility is to modify the database by readjusting the number of pre-generated conformations to address molecular flexibility more adequately. Because pharmacophore modeling and database screening are very complex tasks, several iterations of screening – analysis – refinement are usually necessary to achieve good results.
Conclusions 3D pharmacophore-based screening techniques have found their well-established place in modern drug discovery processes and are thus widely used in academia and industry. The reason for their success is mostly the easy interpretability of the virtual hit-lists due to the three-dimensional alignment of a molecule to its putative chemical interactions. A second strength is the ease of modification and tuning of pharmawww.drugdiscoverytoday.com
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cophore models according to knowledge about the target for a better predictability. The different computational strategies for three-dimensional pharmacophore screening, however, are based on considerably different pharmacophore definitions and algorithms and often yield method-specific results, whereas those differences are often intransparent for the computational chemists using the software packages. New algorithmic concepts, such as pattern matching and full tolerance sub-sampling allow for higher geometric accuracy and thus more consistent results.
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Kirchmair, J. et al. (2007) Fast and efficient in silico 3D screening: toward maximum computational efficiency of pharmacophore-based and shapebased approaches. J. Chem. Inf. Model. 47, 2182–2196 Wolber, G. and Langer, T. (2004) Pharmacophore definition and 3D searches. Drug Discov. Today 1 (3), 203–207 Schneider, G. et al. (1999) ‘Scaffold-Hopping’ by topological pharmacophore search: a contribution to virtual screening. Angew. Chem. Int. Ed. 38 (19), 2894–2896 Laggner, C. et al. (2008) Pharmacophore-based virtual screening in drug discovery. Chemoinformatics: an approach to virtual screening. In Royal (Varnek, A. and Tropsha, A., eds), pp. 76–119, Society of Chemistry Langer, T. and Wolber, G. (2004) Pharmacophore definition and 3D searches. Drug Discov. Today: Technol. 1, 203–207 Matter, H. and Rarey, M. (1999) . In Design and diversity analysis of compound libraries for lead discovery (Jung, G., ed.), Comb. Org. Chem. 409–439 Brown, R.D. and Martin, Y.C. (1997) The information content of 2D and 3D structural descriptors relevant to ligand receptor binding. J. Chem. Inf. Comput. Sci. 37, 1–9 Ewing, T. et al. (2006) Novel 2D fingerprints for ligand-based virtual screening. J. Chem. Inf. Model 46, 2423–2431 Hert, J. et al. (2004) Comparison of fingerprint-based methods for virtual screening using multiple bioactive reference structures. J. Chem. Inf. Comput. Sci. 44, 1177–1185 Willet, P. (2005) Searching techniques for databases of two- and threedimensional chemical structures. J. Med. Chem. 48, 4183–4199 Humblet, C. and Dunbar, J.B., Jr (1993) Chapter VI. Topics in drug design and discovery. In Annual Reports in Medicinal Chemistry, (Vol. 28) (Venuti, M.C., ed.), pp. 275–284, Academic Press Wermuth, C.G. and Langer, T. (1993) Pharmacophore Identification. ESCOM Science Publishers Evers, A. et al. (2005) Virtual screening of biogenic amine-binding Gprotein coupled receptors: comparative evaluation of protein- and ligandbased virtual screening protocols. J. Med. Chem. 48, 5448–5465 Hurst, T. (1994) Flexible 3D searching: the directed tweak technique. J. Chem. Inf. Comput. Sci. 34, 190–196 Wolber, G. et al. (2006) Efficient overlay of small organic molecules using 3D pharmacophores. J. Comput.- Aided Mol. Des. 20 (12), 773–788 Sheridan, R.P. and Kearsley, S.K. (2002) Why do we need so many chemical similarity search methods? Drug Discov. Today 7, 903–911 Johnson, M.A. and Maggiora, G.M. (1990) Concepts and Applications of Molecular Similarity. John Wiley & Sons, Inc. Leach, A. (2001) Molecular Modelling: Principles and Applications (2nd edition), Prentice Hall Zhu, F. and Agrafiotis, D.K. (2007) Recursive distance partitioning algorithm for common pharmacophore identification. J. Chem. Inf. Model. 47, 1619–1625 Brint, A.T. and Willet, P. (1987) Algorithms for the identification of threedimensional maximal common substructures. J. Chem. Inf. Comput. Sci. 27, 152–158 Martin, Y.C. (2000) DISCO: what we did right and what we missed. In ¨ ner, O.F., Pharmacophore Perception, Development and Use in Drug Design (Gu ed.), pp. 51–66, International University Line Martin, Y.C. et al. (1993) A fast new approach to pharmacophore mapping and its application to dopaminergic and benzodiazepine agonists. J. Comput.-Aided Mol. Des. 7, 83–102 Barnum, D. et al. (1996) Identification of common functional configurations among molecules. J. Chem. Inf. Comput. Sci. 36, 563–571 Lemmen, C. and Lengauer, T. (2000) Computational methods for the structural alignment of molecules. J. Comput.-Aided Mol. Des. 14, 215–232 (2008) Molecular Operating Environment (MOE) User Manual. Chemical Computing Group, Inc Pepe, M.S. (2003) The Statistical Evaluation of Medical Tests for Classification and Prediction. Oxford University Press Triballeau, N. et al. (2005) Virtual screening workflow development guided by the ‘Receiver Operating Characteristic’ curve approach. Application to high-throughput docking on metabotropic glutamate receptor subtype 4. J. Med. Chem. 48, 2534–2547
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Editors-in-Chief Kelvin Lam – Harvard University, USA Henk Timmerman – Vrije Universiteit, The Netherlands DRUG DISCOVERY
TODAY
TECHNOLOGIES
3D Pharmacophore Elucidation and Virtual Screening
Strategies for 3D pharmacophorebased virtual screening Thomas Seidel1, Go¨khan Ibis1, Fabian Bendix1, Gerhard Wolber1,2,* 1 2
Inte:Ligand GmbH, Mariahilferstrasse 74B/11, 1070 Vienna, Austria Freie Universita¨t Berlin, Institute of Pharmacy, Pharmaceutical Chemistry, Computer-Aided Drug Design, 14195 Berlin, Germany
3D pharmacophore-based techniques have become one of the most important approaches for the fast
Section editor: Gerhard Wolber – Freie Universita¨t Berlin, Germany
and accurate virtual screening of databases with millions of compounds. The success of 3D pharmacophores
is
largely
based
on
their
intuitive
interpretation and creation, but the virtual screening with such three-dimensional geometric models still poses a considerable algorithmic and conceptual challenge. Most current implementations favor fast screening speed at the detriment of accuracy. This review describes the general strategies and algorithms employed for 3D pharmacophore searching by some current pharmacophore modeling platforms and will highlight their differences.
Introduction The basic concept of pharmacophores has been established since decades [1,2], and the use of pharmacophore modeling techniques as a tool for the discovery of novel drugs has become increasingly popular since the advent of computeraided structure-activity studies [3–9]. The official 1998 IUPAC definition [10] of a pharmacophore is ‘the ensemble of steric and electronic features that is necessary to ensure the optimal supramolecular interactions with a specific biological target structure and to trigger (or to block) its biological response.’ As often misunderstood in medicinal chemistry literature a pharmacophore is thus not the representation of a real mole*Corresponding author: G. Wolber ([email protected]), ([email protected]) 1740-6749/$ ß 2010 Elsevier Ltd. All rights reserved.
DOI: 10.1016/j.ddtec.2010.11.004
cule or a real association of functional groups, but a purely abstract concept that describes the common steric and electrostatic complementarities of bio-active compounds with the target of interest. To be a useful tool for drug design and show sufficient predictive power, 3D pharmacophore models (i.e. the concrete representation of 3D pharmacophores) must be able to describe the nature and location of the functional groups involved in ligand–target interactions, as well as the different types of non-covalent bonding and their characteristics in an uniform and for medicinal chemists easily to comprehend way. This is achieved by categorizing the fundamental types of observed ligand–receptor interactions into pharmacophoric features such as hydrogen-bond donors, hydrogen-bond acceptors, positively and negatively charged groups, and hydrophobic regions. As such, the pharmacophore concept is closely similar to that of bioisosterism [11–14].
3D pharmacophore models as filters for virtual screening The before mentioned feature types are interpreted and implemented by several pharmacophore modeling and virtual screening platforms such as Catalyst (Accelrys) [15–17], ¨ dinger) MOE (Chemical Computing Group) [18], Phase (Schro [19,20] and LigandScout (Inte:Ligand) [21,22]. Nevertheless, differences exist in the exact definition and placement of the features [23], which has the practical consequence that otherwise comparable pharmacophore models lead to a high degree of variation among the obtained screening hit-lists [24,25]. e221
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Figure 1. Example of a shared feature pharmacophore model that was generated by LigandScout [21] from a set of three CDK2 inhibitors (LS2, LS3, and LS4) in their binding conformation (PDB-codes: 1ke6, 1ke7, and 1ke8). The yellow spheres represent hydrophobic features, red arrows hydrogen-bond acceptor and green arrows hydrogen-bond donor features.
Besides the basic type of the observed ligand–receptor interactions, another key information that needs to be incorporated into a proper pharmacophore model is the threedimensional location of the interactions (with a certain tolerance) and, if an interaction is directed (such as hydrogen bonding), also its spatial orientation (see Fig. 1). Because of their abstract nature and simplicity, 3D pharmacophore models represent efficient filters for the virtual screening of large compound libraries [26]: (i) the computational complexity of the hit identification process is greatly reduced by the sparse pharmacophoric representation of e222
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ligand–target interactions which results in low overall search times, and (ii) pharmacophore-based queries allow to find novel drug candidates with different scaffolds and functional groups than the original ligands used for the modeling of the pharmacophores [27]. This is of special interest for pharmaceutical companies that want to avoid patent infringement issues or need to find new lead candidates with better ADMETox properties and/or higher activity towards the target [6]. However, the simplicity of pharmacophoric representations inevitably also means that they cannot explain the complete biophysical nature of drug interactions. An under-
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Figure 2. 3D Pharmacophore-based virtual screening workflow.
standing of the limitations of the concept is therefore essential for its successful application.
Virtual screening workflow The virtual screening of compound libraries is a process that can be divided into several, well-defined steps. Figure 2 shows the typical workflow of a 3D pharmacophore-based virtual screening campaign [6,17,28,29] which will serve as the anchor point for the discussions in the next few sections. Other screening approaches like descriptor-based methods [30], 2D fingerprint checks [31–34], or methods based on 3Ddocking [35] are not in the scope of this review.
3D pharmacophore model creation The first step in a 3D pharmacophore-based virtual screening workflow is to create a query pharmacophore model that specifies the type and geometric constraints of the chemical features that need to be matched by the screened molecules. There are two different strategies to derive pharmacophore models: structure- [36] and ligand-based [3] approaches. The structure-based method determines chemical features based on complementarities between a ligand and its binding-site. This approach requires structural information about the macromolecule and the active conformation of the binding ligand. Structure-based pharmacophore models bear the www.drugdiscoverytoday.com
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advantage that information about the directionality of binding-site interactions can be incorporated – often resulting in highly restrictive models with orientation constrained features [7]. If the 3D structure of the macromolecule is not available, 3D pharmacophore models can be derived in a ligand-based way by the perception of chemical features in common to a set of ligands (training set) which are known to show the desired biological activity towards the target. The latter method delivers good results if enough ligand information is available, and the training set molecules are known to bind to the protein at a specific location [37].
Annotated database creation An important aspect that needs to be considered when screening molecule libraries against 3D pharmacophore models is conformational flexibility. Most major software applications deal with this problem by creating dedicated screening databases that store pre-computed conformations for each of the molecules. Another approach is to tweak the conformation of the molecules on-the-fly in the pharmacophore fitting process [38]. The advantage of the latter approach is the lower storage requirements. However, it also has the important disadvantage that the screening process is considerably slower and a dramatic reduction of the conformational search space while aligning bears the danger of falling into a local minimum [7,39]. Nowadays enough hard disk storage is available and screening databases with pre-generated conformations are clearly preferred. These databases are usually generated once and can be reused whenever needed which results in a considerable speed-up of the overall screening process.
Database searching The database search is most commonly implemented as a multistep filtering process. First, a fast pre-filtering is applied where compounds are eliminated based on feature-types, feature-counts, and quick distance checks. Second, 3Dmatching algorithms are used, which are normally slower but more restrictive.
Pre-filtering Because the actual three-dimensional overlay of query pharmacophore models and molecules is the time-limiting step in the screening process, pre-filtering is of utmost importance [28]. The aim of pre-filtering is a quick identification and elimination of all molecules that cannot be fitted to the query pharmacophore model in 3D. Only those molecules that pass the filters need to be processed in the final accurate, but computationally expensive 3D alignment step. Descriptorbased similarity methods [40] seem to be appropriate filters because little information is needed, similarity calculations are fast, and the implication of biological similarity from structural similarity is generally valid [41]. Feature-count e224
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matching is a (0D descriptor-based) very simple, but nevertheless effective filtering method that can shift out a large fraction of the database molecules (depending on the complexity of the query) [7]. If feature-counts are determined for the query pharmacophore model and pre-calculated for the database molecules, then only molecules that have the same (or higher) feature-count as the query need to be provided to the time-consuming matching step. Another medium complex method in terms of 3D pharmacophore database searching is the concept of ‘pharmacophore keys’ [42]. Such keys provide a binary representation of a molecule taking into account conformational flexibility and pharmacophoric features. By binning inter-feature distances between any two, three or four features, the fingerprint yields a fixed size and each bit represents one possible 2-point, 3-point or 4-point pharmacophore. As a result, the screening becomes a simple intersection test to identify molecules that do not satisfy the query. Similar approaches used in currently available software applications following this concept with slight modifications include integration of feature tolerance sampling, different feature definitions, varying binning constraints, and usage of hashing instead of binning. For instance, Schrodinger’s program Phase applies a single user-defined tolerance to each inter-feature distance of the query to eliminate k-point pharmacophores that have nothing in common [43]. Because of the nature of their distance partitioning algorithm [20] the tolerance should be set to be at least twice the binning size of the binary partitioning tree to be able to produce self matches. Most programs also include filters that potentially discard molecules that mathematically could fit the query pharmacophore model, but this loss in filtering is accepted for the benefit of higher efficiency. Other programs, such as LigandScout, strictly apply lossless filters that guarantee that all of the discarded molecules are not able to geometrically match the query, which results in geometrically more accurate virtual screening results. In summary, pre-filtering is intended to clip out certain parts of the overall search space in favor of speed, but this process of increasing complexity is constrained to maintain the overall quality of the screening outcome: gain higher enrichment and/or find novel scaffolds [37].
Matching of 3D pharmacophore models Once all database molecules that might match the query have been identified, their conformations need to be examined more closely to see if they are able to match the spatial arrangement of the query features. Special care must be taken in this step because the final decision has to be made whether to reject a database compound or to put it in the hit-list and, thus, directly determines the quality of the obtained screening results.
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Table 1. Comparison summary table. Abbreviations: hydrogen-bond acceptor features (HBA), hydrogen-bond donor features (HBD), positive ionizable features (PI), negative ionizable features (NI), aromatic features (AR), hydrophobic features (H), metal-binding features (MB), exclusion volume spheres (XV), and inclusion volume spheres (IV) Catalyst
Phase
LigandScout
MOE
Conformer generation and flexible search
Pre-generation necessary, flexible tweaking supported
Pre-generation optional, lower screening speed if generated on-the-fly
Pre-generation and annotation necessary
Pre-generation necessary, annotation optionally on-the-fly
Alignment strategy, geometric accuracy and scoring
Feature tolerances are averaged, geometric incorrect alignments can also be scored and occur in hit-list
Decision tree with feature independent distance tolerance, least squares procedure, scoring by contributions from feature point alignments and geometric contributions
Pattern matching alignment algorithm, high geometric accuracy due to full tolerance sub-sampling, scoring by feature point alignments and optionally volume overlap
Clique detection with tolerance checking using a low-level state machine, scoring by feature RMSD
Exclusion volume sphere interpretation
Heavy atoms only with atom radii
Heavy atoms and hydrogens with atom radii
Heavy atoms and hydrogens with atom radii
Heavy atom centers without radii
Supported feature types
HBA, HBD, H, AR, PI, NI, XV, custom feature definitions possible
HBA, HBD, H, AR, PI, NI, XV, up to three custom feature definitions via SMARTS-patterns
HBA, HBD, H, AR, PI, NI, Zn/Mg/Fe MB, XV, open feature definition format (XML/SMARTS)
Depends on selected pharmacophore scheme, IV and XV supported
References
[16,23,24]
[20,24]
[22,24,39]
[24,49]
The geometric alignment of the query pharmacophore model to a single molecule conformation can be reduced to the problem of finding a suitable subset of the features of the database compound that fulfills all n-point distance combinations of the query. Computationally greedy solutions for this problem have been proposed relatively early and range from three-dimensional maximum clique detection algorithms [44] as used in DISCO [45,46] to the sequential buildup of increasingly larger common feature configurations as employed in Catalyst/HipHop [47]. However, pure feature-pair distance comparisons (2-point pharmacophores) alone cannot even distinguish between a pharmacophore and its mirror image [16] and an actual overlay in 3D space is required to be able to correctly identify a match to the query within the defined feature tolerances. This overlay is also necessary to check and/or score additional constraints imposed by vector features like hydrogen-bond acceptors/donors, plane features like aromatic rings and exclusion/inclusion volume spheres. Commercial software packages for pharmacophore modeling that incorporate state-of-the-art screening functionality like Catalyst, Phase, MOE and LigandScout thus all perform some sort of geometric alignment in the 3D pharmacophore matching step, which is usually done by minimizing the RMSD between associated feature pairs [48]. While all other programs implement a search in increasing n-point distances, LigandScout uses a sophisticated pattern-matching technique to identify an initial alignment resulting in lower restrictions regarding the number of features in the query pharmacophore model. Although the general strategies for hit identification are similar, they differ in various details which range from the
handling of conformational flexibility and interpretation of query feature constraints to the customization of search parameters. The following sections attempt to provide a short overview (see also Table 1): Catalyst: In addition to the standard screening mode that rigidly fits conformations to a 3D pharmacophore model, Catalyst offers a Best Flexible Search mode that can tweak the examined conformation of a molecule to enforce a fit within a given energy threshold. A database search starts with a rapid pre-filtering process that screens out molecules that do not possess properties required for potential hits. In the next step of the search procedure, Catalyst tries to match the conformations of each compound with the features of the query pharmacophore model in 3D space. In this fitting step, Catalyst checks two sorts of constraints [16]: (a) The RMS deviation must be less than one, that is vffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi u X u1 distðQ i ; Ai Þ 2 t <1 N 1iN tolerancei where N is the number of superimposed query feature-/ ligand-point pairs, Qi is the position of the ith query feature, Ai is the position of the aligned ligand-point, and tolerancei is the radius of the tolerance sphere around Qi. (b) The distance between each pair of features of the database molecule must be such that it is possible to align them within the specified tolerances of the mapped query features. The form of the first criterion has the consequence that hits may be reported where some of the aligned database molecule feature points are outside of the specified query feature www.drugdiscoverytoday.com
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tolerance spheres. This effect was also observed by Spitzer et al. in their comparative study of screening results obtained by Phase, MOE and Catalyst [24]. Once a hit-list has been obtained, Catalyst provides the possibility to compute fit values that can be used for scoring and ranking of the screening results. Phase:3D pharmacophore searching in Phase [20] does not require a dedicated screening database (although supported for faster processing) and supports standard and on-the-fly searching modes. In standard mode, the query pharmacophore model is matched against a set of pre-computed conformers for each molecule in the database or flat file. When searching on-the-fly, conformers are generated in memory using a built-in torsion search method. The primary method of matching applies a single user-defined tolerance to each inter-feature distance in the query pharmacophore model and discards all database molecules whose pharmacophore features fail the distance constraint checks. If it is sufficient to match only a subset of m out of k sites, Phase offers the possibility to restrict subsets to include or exclude specific sites. Matching a particular subset of m sites requires that the user-defined distance tolerance must be satisfied for all m(m 1)/2 inter-site distances in that subset. When a molecule produces a match, the applicable site points are aligned to the pharmacophore model using a standard least-squares procedure. If desired, query feature tolerances may be applied at this stage, so that a match will be eliminated if any single feature in the matching molecule deviates from the corresponding query feature point by more than the associated positional tolerance. Positional tolerances are defined independently from the inter-site distance tolerance, so if matching is to be governed purely by the former, the distance tolerance must be at least twice the largest positional tolerance. Matches for a particular molecule are sorted by decreasing fitness, and the user has the option of retaining any number of high-ranking matches for each molecule. When matches are found, the aligned conformers are placed in a hit-list sorted by decreasing fitness. When the query contains excluded volumes, any matches will be checked for clashes before they get added to the hit-list. The total size of the hitlist can be limited and in addition to applying positional tolerance and exclusion volume filters, a lower limit for the vector score or volume score of matching database molecules can be specified. The volume score threshold then acts as a filter that forces hits to resemble the reference ligand in overall size and shape. MOE: In MOE [49], molecules are stored in databases with their associated pre-computed conformations. No new conformations are generated during a database mining experiment. Pharmacophore models of the database molecules (its features are called ‘annotation points’ in MOE terminology) can be pre-calculated for faster processing or generated one226
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the-fly. Each annotation point contains multiple flags indicating the presence or absence of pharmacophoric features. To match each molecule to the query pharmacophore model, the search algorithm aligns the molecule to the query using rigid-body superposition. No flexible adjustment of the rotatable bonds is done during the search. Each match is represented as a mapping between two sets of points: the database molecule annotation points and the query features. A rigidbody transformation of the molecule brings the matched annotation points inside the tolerance radii of the corresponding query features. When performing a systematic matching, all possible matches of the database molecule and the query are systematically examined. The search then outputs all matches that satisfy the constraints of the query. Partial matching offers that only those features of the query which are marked as ‘essential’ must be matched by a corresponding ligand annotation point. Other query features may be left unmatched, resulting in a partial match. The search outputs all partial matches of the query that contain at least a given minimum number of matched features or do not exceed a specified maximum number of unmatched features. MOE permits to focus the query by the addition of a set of volumes, each being a sphere or union of spheres defining a volume of space in which matching atoms can be either excluded, required or allowed. Only those database compound conformations whose atoms match the associated SMARTS expression and lie within the allowable regions will then be output by the search. Each query feature can be associated with an expression that determines which features must or must not be present at a ligand annotation point for a successful match to the query feature. Additional feature group constraints may be added to further restrict the set of acceptable matches. LigandScout: LigandScout operates on pre-computed conformations and pharmacophore models of the screening dataset molecules that are stored in a dedicated database for fast processing. All conformations of a database molecule that might match the query pharmacophore model (that is, all that survive preliminary feature-count, two-point and three-point pharmacophore checks) are examined in turn and an attempt is made to superimpose them with the query pharmacophore model employing the pattern matching algorithm described in [39]. The geometric accuracy of LigandScout’s pharmacophore alignment algorithm reaches a higher level than in other programs because an aligned database pharmacophore model is considered as matching only if all of the fitted feature points lie within the individual tolerance spheres of the query features. Furthermore, angle constraints of vector and plane features (which are user customizable) have to be fulfilled and no clashes between exclusion volume spheres and atom Van der Waals spheres must be present. If all constraints of the query pharmacophore model are fulfilled, the search for a matching molecule
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conformation terminates and a hit is reported or, if desired, the search is continued to find a better matching conformation. Partial matches are possible by allowing several query features to be omitted during the pharmacophore matching process. Omitted features can be specified directly by marking certain features as optional or by providing a maximum omitted feature count. A unique feature of LigandScout that is not found in any of the other software packages is the possibility to specify multiple query pharmacophore models at once which can be linked together in the form of boolean expressions. For the scoring and prioritization of hit molecules, LigandScout provides several scoring functions that range from alignment RMSD scores to molecular volume overlap scores.
database molecules (not including their conformations). EF ¼
Ya A=N
Goodness of Hit-list (GH) combines Sensitivity, Specificity, and Yield of Actives and is therefore a very useful measure that considers both the true actives ratio and the true inactives ratio. The Goodness of Hit-list is defined as the weighted sum of Ya and Se multiplied with Sp. The quantity of active compounds is usually weighted higher than that of actives in ¨ ner and Henry [3] weight the the hit-list. For example, Gu Yield of Actives with 3/4 and the Sensitivity with only 1/4. Thus, a high value of GH can only be achieved with a high value of actives and a low false-negative ratio at the same time.
Hit-list analysis Hits retrieved from the 3D database search are a good starting point for the validation and refinement of the pharmacophore model. There are several useful measures [3,25,28,29] which are described in more detail below. Sensitivity (Se) is the ratio of the retrieved true positive compounds TP to all active compounds in the database, which is the sum of TP and the number of false negative compounds FN. Sensitivity values can range from 0 to 1, where Se = 0 means that the search did not find any of the actives in the database and Se = 1 means that the search returned all active compounds. Se ¼
TP TP þ FN
Specificity (Sp) is the amount of rejected truly negative compounds TN divided by the sum of TN and the number of retrieved false positive compounds FP. Specificity ranges from 0 to 1 and denotes the percentage of truly inactive compounds. Sp = 0 means that none of the inactive compounds could be identified as such and Sp = 1 means that all inactive compounds have been correctly rejected during the screening process. Sp ¼
TN TN þ FP
Yield of Actives (Ya) is a measure that shows the amount of the retrieved truly active compounds TP in relation to the size of the hit-list n. The Yield of Actives can for example be used to compare hit-lists retrieved for databases created with different conformer sampling techniques [25]. Ya ¼
TP n
Enrichment Factor (EF) measures the Yield of Actives proportionally to the ratio of actives in the database, where A is the amount of actives in the database and N is the total number of
w1 þ w2 ¼ 1 w1 > w2
GH ¼ ðw1 Ya þ w2 SeÞ S p
A modern tool for the assessment of screening results is Receiver Operating Characteristic (ROC) curves [50,51]. The ROC curve displays the increase of false positives that results with increased true positives. The Y-coordinate of the ROC curve represents the true-positive rate, whereas the X-coordinate denotes the appropriate false-positive rate. An ideal curve would rise vertically along the Y-axis until it reaches the maximum true positive rate, which is 1, and then continues horizontally to the right, which means that the hit-list contains all active compounds in the database and that no one of the hits is a false positive. The ROC curve of a random database search is represented by the median.
Pharmacophore model refinement On the basis of an analysis of the hit-list with the above measures and tools, the pharmacophore model is often refined to achieve more satisfying results. Adaption of feature definitions, modification of feature tolerances, addition or removal of features and exclusion volumes are some of the adjustments that can help to tune a pharmacophore model. Another possibility is to modify the database by readjusting the number of pre-generated conformations to address molecular flexibility more adequately. Because pharmacophore modeling and database screening are very complex tasks, several iterations of screening – analysis – refinement are usually necessary to achieve good results.
Conclusions 3D pharmacophore-based screening techniques have found their well-established place in modern drug discovery processes and are thus widely used in academia and industry. The reason for their success is mostly the easy interpretability of the virtual hit-lists due to the three-dimensional alignment of a molecule to its putative chemical interactions. A second strength is the ease of modification and tuning of pharmawww.drugdiscoverytoday.com
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cophore models according to knowledge about the target for a better predictability. The different computational strategies for three-dimensional pharmacophore screening, however, are based on considerably different pharmacophore definitions and algorithms and often yield method-specific results, whereas those differences are often intransparent for the computational chemists using the software packages. New algorithmic concepts, such as pattern matching and full tolerance sub-sampling allow for higher geometric accuracy and thus more consistent results.
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28
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