Drug Discovery Today: Technologies
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Editors-in-Chief Kelvin Lam – Blue Sky Biotech, Inc., Worcester, MA Henk Timmerman – Vrije Universiteit, The Netherlands DRUG DISCOVERY
TODAY
TECHNOLOGIES
Scaffold hopping
De novo design – hop(p)ing against hope Gisbert Schneider Swiss Federal Institute of Technology (ETH), Department of Chemistry and Applied Biosciences, Institute of Pharmaceutical Sciences, 8093 Zu¨rich, Switzerland
Current trends in computational de novo design provide a fresh approach to ‘scaffold-hopping’ in drug
Section editor: D. Lloyd – Trinity College, Dublin, Ireland.
discovery. The methodological repertoire is no longer limited to receptor-based methods, but specifically ligand-based techniques that consider multiple properties in parallel, including the synthetic feasibility of the computer-generated molecules and their polypharmacology, provide innovative ideas for the discovery of new chemical entities. The concept of fragment-based and virtual reaction-driven design enables rapid compound optimization from scratch with a manageable complexity of the search. Starting from known drugs as a reference, such algorithms suggest drug-like molecules with motivated scaffold variations, and advanced mathematical models of structure-activity landscapes and multi-objective design techniques have created new opportunities for hit and lead finding.
Introduction De novo design aims to generate novel bioactive molecular entities ‘from scratch’. Ideally, such computer-designed molecules have drug-like properties, potently interact with a target macromolecule in the low nanomolar range, lack adverse effects, are orally bioavailable, free of intellectual property rights, and may be easily synthesized from cheap starting material. Evidently, such a catalog of demands and expectations is unrealistic. What one can expect from de novo design software solutions, however, is some degree of structural novelty, modest bioactivity, and chemical feasibility of the designed compounds [1]. This has not always been so. E-mail address: G. Schneider ([email protected]) 1740-6749/$ ß 2012 Elsevier Ltd. All rights reserved.
After several remarkable but salutary failures in the pioneering days, it quickly became evident that while constructing molecules atom-by-atom might in fact lead to novel chemical entities that fill a binding cavity to a desired extent in a desired binding pose and form potentially strong interactions with a receptor structure, there is a high risk that these artificial compounds cannot be realized by chemical synthesis in a straightforward fashion or possess adverse physicochemical properties. Contemporary computer-assisted de novo design algorithms largely rely on molecular fragments as the basic building blocks to come up with appealing molecular frameworks rather than fully optimized potential ligands [2]. This development is partly owed to the success of fragment-based hit finding by high-throughput NMR and Xray methods and demonstrates how our current understanding of computational drug design has co-evolved with advancing technological developments [3,4]. Equally important, it has been realized that a desired or observed pharmacological effect often is the result of a compound’s activity on multiple targets. Explicit consideration of potential polypharmacology has become viable in drug design [5,6], enabled by the availability of large functionally annotated compound databases [7,8]. It therefore is a worthwhile exercise to try to de novo generate new chemical entities (NCEs) that exhibit a desired target and property profile. A promising approach is to use a known drug as a reference structure that serves as a template for virtual compound assembly (ligand- or template-based design). In this context, the term ‘scaffold-hopping’ is closely related and in some way linked with computer-assisted molecular design [9]. The first joint occurrence of the mesh words ‘de novo design’ and ‘drug’ in the context of computational chemistry dates back to a
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Figure 1. Number of publications per year with the keywords {‘de novo design’ and drug} (blue bars), and ‘scaffold-hopping’ (stacked orange bars) mentioned in the topic descriptions according to Web of Science (Thomson Reuters, New York, 2011).
seminal article by Moon and Howe from 1991 (Fig. 1) [10]. Ever since, the term ‘de novo design’ has been used on a regular basis to refer to computer-assisted molecular design studies. Notably, there seems to be no significant increase in publications per year, although after an initial small wave of papers published in the mid 1990s, we are currently witnessing a renewed interest in this field, possibly motivated by improved techniques and increased demand [11,12]. This hypothesis is clearly mirrored in the remarkable increase in publications on ‘scaffold-hopping’, especially after 2006, with the inaugural publication dating back to our own work from 1999 [13]. It seems that finding meaningful bioisosters and scaffold replacements has become feasible by systematic molecular de novo design, and is not a product of pure chance. In fact, the field has been reviewed recently presenting numerous success stories [14,15]. We here discuss some aspects of de novo design that require further attention so that the full potential of this scientific approach may be exploited for future drug discovery.
The odds are good but the goods are odd From a mere statistical point of view, generating new NCEs from scratch with a clearly defined activity and property profile should be borderline impossible. Even model-based computational methods allow us to construct by far too many virtual compounds with drug-like properties than could ever be exhaustively screened in silico or in vitro. To be successful in this enterprise and increase our chance of finding potent compounds among a small set of selected candidates, we have to take smart shortcuts by focusing on promising regions in chemical space – and accept imperfection. This blunt statement simply means that any knowledge about focal points of interest for a computational search for NCEs, for example, by considering privileged substructures and biophoric molecular patterns in a virtual screen, is likely to reduce the novelty of the designs. While we can expect to find e454
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appealing new compounds by interpolating between reference points (e.g. representations of known drugs and their binding sites) it is unlikely to discover never-before-seen molecular structures that pass our current definition (and perception) of pharmaceutically active agents. Herein lies a dilemma of both ligand- and receptor/structure-based de novo drug designs. From a medicinal chemistry perspective, computationally assembled and virtually screened candidate compounds have always had a certain tendency to be either awkward or boring: one either finds obvious analogs or unexpected similarities to given reference drugs. With the introduction of fragment- and reaction-based approaches to virtual compound assembly and the predictive functions used for compound scoring, the potential synthetic accessibility of candidate structures can be controlled better, although these methods might also come at the cost of reduced structural novelty as they are based on known chemistry. Estimations of the number of theoretically feasible drug-like compounds that could be generated using such a combinatorial technique range between 1015 and 1018 compounds – still a sufficiently large part of chemical space that should allow for finding attractive NCEs [16,17]. Traditionally, receptor-based in situ fragment assembly has been grounded on the assumption that fragment-contributions to the ligand binding energy are additive [18]. While this principle holds if the binding mode and orientation of the individual fragments are only marginally perturbed in the elaborated product, there are growing numbers of reports of unexpected binding modes and non-additive, that is, nonlinear, fragment contributions [19,20]. Non-additivity of DG contributions of individual fragments can lead to marked discrepancies between the sum of fragment affinities and the final ligand (Fig. 2) [21,22]. In order to achieve more reliable estimations of binding energies of virtually assembled compounds, it will be mandatory to account for the effect of linker elements during the construction process. Possibly, the evaluation of the full ligand product is obligatory. Consequently, despite the appeal of fast algorithmic solutions for fragment-based exhaustive or global combinatorial product evaluation that implement the additivity principle, the actual practical applicability of these techniques can hardly be assessed a priori. As a workaround, both stochastic and deterministic local optimization strategies that score the full product have become a main design strategy. Although these approaches do not guarantee finding the globally best compound, there is ample evidence that they identify new bioactive ligands. In fact, a trade-off between conservative and exploratory design can be helpful in hit identification. For example, our ligand-based fragment-growing tool DOGS can be tuned to a desired ratio of scaffold exploitation/exploration during candidate compound assembly [23]. It also features a simplistic deterministic approach to estimating the score contribution of linker parts that originate from
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Figure 2. Example of fragment super-additivity [58]: the experimentally determined free energy of binding DG of the product, a potent factor Xa inhibitor (Ki = 2 nM), exceeds the sum of the individual fragment contributions by 14 kJ mol 1. A single bond was added as linker (filled arrowhead). The cartoon shows the enzyme–ligand complex (PDB ID: 4a7i). Several interacting residue side-chains are highlighted.
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Figure 3. Analysis of molecular frameworks resulting from ligand-based de novo design using the ACE inhibitor Lisinopril as template. The software Flux was employed to generate a large pool of designs (Step 1), which were condensed to promising candidates using a random forest model predicting ACE inhibition (Step 2), and subjected to scaffold analysis (Step 3). The ten most frequently generated molecular frameworks are presented. All of these frameworks are also found in known ACE inhibitors. The molecular framework of Lisinopril is highlighted in magenta.
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synthesis reactions. Similarly, evolutionary design algorithms are easily adaptable to produce desired scaffold diversity of the products [24,25]. Instead of accounting for synthetic accessibility by explicit reaction-based compound construction, one can also rely on software that has especially been designed for synthesis planning. For example, the computer programs CAESA [26], SYLVIA [27], and RouteDesigner [28] propose synthesis plans for de novo generated compounds post hoc. Fragment-based molecular design intrinsically follows the concept of scaffold-hopping, which aims at identifying scaffold replacements in a known drug so that an NCE with the same or improved properties as the reference drug emerges. Intentionally such an approach mimics compound design in medicinal chemistry, where a human mind conceives of new
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molecular structures by drawing ideas from personal but individually unique knowledge. One needs to accept that any medicinal chemist’s choice is influenced and biased towards preferred regions of chemical space by years of training, and that such preferences affect the innovation potential of human-designed compounds – just like computational methods do. For the inclined user, own expertise may be complemented by computer-assisted scaffold-hopping and ultimately de novo design to come up with surprising but acceptable suggestions that can be actually realized in the laboratory and support hit and lead structure identification in drug discovery. A worked example of de novo design for the purpose of scaffold-hopping is presented in Fig. 3. In this pilot study [29], we applied our software Flux [30,31] to generating
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Figure 4. From receptor structure to a potent inhibitor by virtual screening (a), and conservative scaffold-hopping by fragment-based de novo design (b). The template structure for de novo design was obtained in a receptor-based virtual screen for Plk1 inhibitors [59]. It served as input for the software DOGS, which suggested a variation of the template together with a plausible synthesis route. The designed compound also strongly inhibits Plk1 activity [17]. The frameworks of both molecules are shown in blue color. They differ in the size of the rings and the linker in the eastern part of the structures.
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compounds that mimic the reference compound Lisinopril, a potent inhibitor of angiotensin converting enzyme (ACE). Flux used a selection of 10,497 drug-derived virtual building blocks and RECAP-type [32] connectivity for fragment-based molecular design, guided by an evolutionary algorithm (a (1, l) evolution strategy). The design objective was to optimize two-dimensional pharmacophore similarity (CATS descriptor) between the template (here: Lisinopril) and the virtually generated compounds. We condensed the initially large number of more than 250,000 suggestions by virtual screening for potential ACE inhibitors using a random forest [33] classifier model. The remaining 9416 candidate molecules featured a total of 2582 different scaffolds (graph frameworks). Only 55 scaffolds appeared more than 100 times in the final compound set, indicating strong algorithmic scaffold exploration. It is of note that all of the ten most frequent scaffolds are contained in known ACE inhibitors, which clearly mirrors chemotypes memorized by the classifier model. This exercise demonstrates that (i) useful molecular frameworks can be obtained by fragment-based de novo design, and (ii) recurring scaffold motifs might be of particular interest for further analysis. At the same time it becomes apparent that meaningful extrapolation from the domain of training or reference compounds is unlikely when (Q)SAR models are used for candidate compound triaging. An example of a minor scaffold variation is the potent typeII inhibitor of Polo-like kinase 1 (Plk1) which was generated by ligand-based de novo design using the DOGS method, and synthesized according to the reaction scheme proposed by
the software (Fig. 4) [17]. In analogy to biophysical fragmentfinding and extension [34], DOGS grows new compounds from a small start fragment so that virtual products are obtained that possess high pharmacophoric feature similarity to the template. This study suggests that conservative ligandbased de novo design may be applied to exploring the structural neighborhood of known actives in the absence of a known receptor structure. However, when reliable receptor models are available, structural optimization by small incremental modifications could also be performed by computationally more demanding free energy estimation methods like BOMB [35].
Against all odds – but take a look at it now! The central idea of scaffold-hopping is to find structurally different chemotypes with isofunctional bioactivity to a given template. As the actually relevant set of functiondetermining molecular features (substructures, pharmacophore points) is often unknown a priori, virtual screening and de novo design alike are typically performed with a permissive pharmacophore model to ensure sufficiently large scaffold variability among the matching compounds. The degree of ‘fuzziness’ or permissiveness of the model critically influences scaffold diversity in the hit lists (Fig. 5) [36]. Of note, the overlap between individual hit lists is not necessarily large, but occurrences of the same compounds in multiple screens (or consensus scoring approaches) argues for at least partially shared bioactivity (‘Authority Effect’) [37,38]. This observation is extremely useful for de novo design, as it
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Figure 5. Similarity searching with a conservative (model 1) or increasingly permissive pharmacophore hypotheses (models 2 and 3) results in distinctive virtual hit lists with limited overlap. Here, clopidogrel served as the template (query; left panel), and the software LIQUID [60] computed probabilistic pharmacophoric feature densities (green: lipophilic, red: hydrogen-bond acceptor). The top-scoring hits are shown together with their respective LIQUID pharmacophore model. The only compound retrieved from the screening compound pool by all three pharmacophore hypotheses is clopidogrel itself (right panel).
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COBRA
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Figure 6. Property landscapes of a collection of 11,230 drugs and lead compounds (COBRA v10.3) [61]. Molecules were represented by a 210dimensional pharmacophore descriptor (CATS2D v2) and projected to two dimensions using stochastic neighbor embedding [62] (SNE). Surfaces were computed using our software tool LiSARD [46]. Red color indicates undesired areas in chemical space, green color denotes regions containing compounds with desired features. Color intensity correlates with prediction confidence. Upper panel: The 2D distribution of the compound collection (left), and corresponding activity islands formed by factor Xa, PPARg, and cyclooxygenase (COX) ligands. Lower panel: Computed property landscapes for the number of hydrogen-bond donors, total polar surface area (TPSA), and lipophilicity (a log P); the combined landscape (TPSA + a log P) shown on the right indicates a pronounced preferred target area (arrow) for de novo design with regard to these two properties. Note that the combined landscape is tilted and slightly enlarged compared to the other visualizations to highlight hills and valleys. Physicochemical compound properties were computed using the software implementations in MOE (v2011, The Chemical Computing Group, Montreal, Canada).
provides a rationale for scoring and analyzing the numerous suggestions that can be obtained from a compound generator. Although the numbers of high-scoring molecules drop away rapidly as more selection criteria are applied, there is motivated hope that the fraction of actives increases. The general optimization paradigm requires acceptably smooth fitness landscapes [39,40], which seems to be fulfilled by using fragments (as opposed to atoms) as molecular building blocks for de novo design. Can we also predict the effect of seemingly minor structural modification on molecular properties, including affinity (e.g. ‘activity cliffs’), physicochemistry (e.g. aqueous solubility), and pharmacokinetics (e.g. oxidative metabolism)? Matched Molecular Pairs Analysis (MMPA) [41] has lately raised great interest in medicinal chemistry, and might also support de novo design in this regard [42,43]. Multi-objective optimization provides another concept for computer-assisted drug design that helps navigate in chemical space under the constraint of partially conflicting objective functions [44]. Focusing on compounds with sub-optimal but still acceptable properties might actually be key to success in de novo design, simply due to the fact that all predictive models are error-prone when applied to an individual molecule. Graphical visualization of the optimization progress in models of the underlying fitness landscape e458
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can help steer into the right direction and avoid artifact designs caused by over-optimization [45]. As an illustration, the graphical combination of two objective functions is presented in Fig. 6. The resulting fitness landscape was generated to implicitly address several design criteria while not being limited to compound potency as the sole driving force [46]. Similar visualizations with different underlying landscape models have been developed recently [47,48]. The appropriate mathematical framework is available to enable multidimensional optimization of synthetically accessible compounds as the next realistic step in practical computerassisted drug discovery. This encouraging development gives new hope for successful NCE identification by fragmentbased de novo design.
Conclusion: back to the future! One may wonder why computational de novo design has become fashionable again [49,50]. Although the basic ideas were conceived approximately three decades ago, its essential ingredients have remained the same – but with a new twist, admittedly. We have come a long way ever since, and have learned from earlier failure. Contemporary approaches include fragment-based compound assembly, a variety of scoring schemes that allow for nonlinear contributions,
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explicit or implicit consideration of synthetic feasibility of the designs, and multi-objective optimization addressing multiple targets and properties. Existing software solutions focus on each of these features to a different extent, and it is only a matter of time and effort to implement working and applicable de novo design tools that actually suggest synthesizable compounds exhibiting desired polypharmacology [51]. Proven scaffold-hopping potential might actually be key to successful identification of NCEs, and ligand-based de novo methods offer themselves as premier tools for rapid analog design. In future applications, de novo compound generators could also be directly linked to patent databases to identify most promising candidate scaffolds and potential activity profiles. Another idea is to compare designed compounds to databases such as ZINC [52], which contain physically available substances with known synthetic accessibility. This could flag up compounds that share the scaffold (both as a framework and more particularly with specific heteroatoms) with a design, to obtain confidence to the feasibility of the design and a potential shortcut to synthetic work. While there has been notable progress during the past years, several limiting factors still prevent de novo design approaches from being overly successful [1]. For example, water molecules modify the size, shape and pharmacophoric features of a ligand-binding pocket, thereby critically affecting the diversity of chemotypes that can be accommodated [53]. Mancera and coworkers were among the first to investigate the role that water molecules, specifically hydration sites in the receptor cavity, can have on the structural interpretation of ligand-derived pharmacophore models [54]. Since water molecules are known to influence molecular diversity in receptor-based design [55], their importance clearly needs to be recognized in the context of ligand-based approaches. Another major limitation originates from insufficient consideration of entropic effects upon ligand binding and consequently the still largely unsolved scoring problem [56], although recent developments of ab initio approaches, like the fragment molecular orbital method, point towards progress in this regard for structure-based de novo design [57]. In the author’s view, the most critical part in computational drug design has always been its actual practical realization, so that rapid feedback loops enable adaptive learning. As long as coupling computer-based design to immediate automated synthesis and testing will remain a future (but certainly attainable) goal, teams of dedicated modelers and steadfast chemists will have to do the hard work. Despite all enthusiasm, we should not forget that any computational prediction is error-prone, and there currently are too few worked examples to allow for a sound statistical assessment of success rates in de novo design. This fact alone should encourage us to find out.
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Acknowledgements The author is grateful to his coworkers and collaborating partners for realizing exciting molecular design studies. Dr. David Lloyd is thanked for helpful comments on the manuscript. The work presented was financially supported by ETH ¨ rich, the Swiss National Science Foundation (SNSF, grant Zu 205321-134783), Deutsche Forschungsgemeinschaft (DFG, ¨ rich. The Chegrant FOR1406_TP4), and OPO-Foundation Zu mical Computing Group Inc. provided a research license of the software MOE. The funding sources had no such involvement in this study.
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Editors-in-Chief Kelvin Lam – Blue Sky Biotech, Inc., Worcester, MA Henk Timmerman – Vrije Universiteit, The Netherlands DRUG DISCOVERY
TODAY
TECHNOLOGIES
Scaffold hopping
De novo design – hop(p)ing against hope Gisbert Schneider Swiss Federal Institute of Technology (ETH), Department of Chemistry and Applied Biosciences, Institute of Pharmaceutical Sciences, 8093 Zu¨rich, Switzerland
Current trends in computational de novo design provide a fresh approach to ‘scaffold-hopping’ in drug
Section editor: D. Lloyd – Trinity College, Dublin, Ireland.
discovery. The methodological repertoire is no longer limited to receptor-based methods, but specifically ligand-based techniques that consider multiple properties in parallel, including the synthetic feasibility of the computer-generated molecules and their polypharmacology, provide innovative ideas for the discovery of new chemical entities. The concept of fragment-based and virtual reaction-driven design enables rapid compound optimization from scratch with a manageable complexity of the search. Starting from known drugs as a reference, such algorithms suggest drug-like molecules with motivated scaffold variations, and advanced mathematical models of structure-activity landscapes and multi-objective design techniques have created new opportunities for hit and lead finding.
Introduction De novo design aims to generate novel bioactive molecular entities ‘from scratch’. Ideally, such computer-designed molecules have drug-like properties, potently interact with a target macromolecule in the low nanomolar range, lack adverse effects, are orally bioavailable, free of intellectual property rights, and may be easily synthesized from cheap starting material. Evidently, such a catalog of demands and expectations is unrealistic. What one can expect from de novo design software solutions, however, is some degree of structural novelty, modest bioactivity, and chemical feasibility of the designed compounds [1]. This has not always been so. E-mail address: G. Schneider ([email protected]) 1740-6749/$ ß 2012 Elsevier Ltd. All rights reserved.
After several remarkable but salutary failures in the pioneering days, it quickly became evident that while constructing molecules atom-by-atom might in fact lead to novel chemical entities that fill a binding cavity to a desired extent in a desired binding pose and form potentially strong interactions with a receptor structure, there is a high risk that these artificial compounds cannot be realized by chemical synthesis in a straightforward fashion or possess adverse physicochemical properties. Contemporary computer-assisted de novo design algorithms largely rely on molecular fragments as the basic building blocks to come up with appealing molecular frameworks rather than fully optimized potential ligands [2]. This development is partly owed to the success of fragment-based hit finding by high-throughput NMR and Xray methods and demonstrates how our current understanding of computational drug design has co-evolved with advancing technological developments [3,4]. Equally important, it has been realized that a desired or observed pharmacological effect often is the result of a compound’s activity on multiple targets. Explicit consideration of potential polypharmacology has become viable in drug design [5,6], enabled by the availability of large functionally annotated compound databases [7,8]. It therefore is a worthwhile exercise to try to de novo generate new chemical entities (NCEs) that exhibit a desired target and property profile. A promising approach is to use a known drug as a reference structure that serves as a template for virtual compound assembly (ligand- or template-based design). In this context, the term ‘scaffold-hopping’ is closely related and in some way linked with computer-assisted molecular design [9]. The first joint occurrence of the mesh words ‘de novo design’ and ‘drug’ in the context of computational chemistry dates back to a
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Figure 1. Number of publications per year with the keywords {‘de novo design’ and drug} (blue bars), and ‘scaffold-hopping’ (stacked orange bars) mentioned in the topic descriptions according to Web of Science (Thomson Reuters, New York, 2011).
seminal article by Moon and Howe from 1991 (Fig. 1) [10]. Ever since, the term ‘de novo design’ has been used on a regular basis to refer to computer-assisted molecular design studies. Notably, there seems to be no significant increase in publications per year, although after an initial small wave of papers published in the mid 1990s, we are currently witnessing a renewed interest in this field, possibly motivated by improved techniques and increased demand [11,12]. This hypothesis is clearly mirrored in the remarkable increase in publications on ‘scaffold-hopping’, especially after 2006, with the inaugural publication dating back to our own work from 1999 [13]. It seems that finding meaningful bioisosters and scaffold replacements has become feasible by systematic molecular de novo design, and is not a product of pure chance. In fact, the field has been reviewed recently presenting numerous success stories [14,15]. We here discuss some aspects of de novo design that require further attention so that the full potential of this scientific approach may be exploited for future drug discovery.
The odds are good but the goods are odd From a mere statistical point of view, generating new NCEs from scratch with a clearly defined activity and property profile should be borderline impossible. Even model-based computational methods allow us to construct by far too many virtual compounds with drug-like properties than could ever be exhaustively screened in silico or in vitro. To be successful in this enterprise and increase our chance of finding potent compounds among a small set of selected candidates, we have to take smart shortcuts by focusing on promising regions in chemical space – and accept imperfection. This blunt statement simply means that any knowledge about focal points of interest for a computational search for NCEs, for example, by considering privileged substructures and biophoric molecular patterns in a virtual screen, is likely to reduce the novelty of the designs. While we can expect to find e454
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appealing new compounds by interpolating between reference points (e.g. representations of known drugs and their binding sites) it is unlikely to discover never-before-seen molecular structures that pass our current definition (and perception) of pharmaceutically active agents. Herein lies a dilemma of both ligand- and receptor/structure-based de novo drug designs. From a medicinal chemistry perspective, computationally assembled and virtually screened candidate compounds have always had a certain tendency to be either awkward or boring: one either finds obvious analogs or unexpected similarities to given reference drugs. With the introduction of fragment- and reaction-based approaches to virtual compound assembly and the predictive functions used for compound scoring, the potential synthetic accessibility of candidate structures can be controlled better, although these methods might also come at the cost of reduced structural novelty as they are based on known chemistry. Estimations of the number of theoretically feasible drug-like compounds that could be generated using such a combinatorial technique range between 1015 and 1018 compounds – still a sufficiently large part of chemical space that should allow for finding attractive NCEs [16,17]. Traditionally, receptor-based in situ fragment assembly has been grounded on the assumption that fragment-contributions to the ligand binding energy are additive [18]. While this principle holds if the binding mode and orientation of the individual fragments are only marginally perturbed in the elaborated product, there are growing numbers of reports of unexpected binding modes and non-additive, that is, nonlinear, fragment contributions [19,20]. Non-additivity of DG contributions of individual fragments can lead to marked discrepancies between the sum of fragment affinities and the final ligand (Fig. 2) [21,22]. In order to achieve more reliable estimations of binding energies of virtually assembled compounds, it will be mandatory to account for the effect of linker elements during the construction process. Possibly, the evaluation of the full ligand product is obligatory. Consequently, despite the appeal of fast algorithmic solutions for fragment-based exhaustive or global combinatorial product evaluation that implement the additivity principle, the actual practical applicability of these techniques can hardly be assessed a priori. As a workaround, both stochastic and deterministic local optimization strategies that score the full product have become a main design strategy. Although these approaches do not guarantee finding the globally best compound, there is ample evidence that they identify new bioactive ligands. In fact, a trade-off between conservative and exploratory design can be helpful in hit identification. For example, our ligand-based fragment-growing tool DOGS can be tuned to a desired ratio of scaffold exploitation/exploration during candidate compound assembly [23]. It also features a simplistic deterministic approach to estimating the score contribution of linker parts that originate from
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Figure 2. Example of fragment super-additivity [58]: the experimentally determined free energy of binding DG of the product, a potent factor Xa inhibitor (Ki = 2 nM), exceeds the sum of the individual fragment contributions by 14 kJ mol 1. A single bond was added as linker (filled arrowhead). The cartoon shows the enzyme–ligand complex (PDB ID: 4a7i). Several interacting residue side-chains are highlighted.
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Figure 3. Analysis of molecular frameworks resulting from ligand-based de novo design using the ACE inhibitor Lisinopril as template. The software Flux was employed to generate a large pool of designs (Step 1), which were condensed to promising candidates using a random forest model predicting ACE inhibition (Step 2), and subjected to scaffold analysis (Step 3). The ten most frequently generated molecular frameworks are presented. All of these frameworks are also found in known ACE inhibitors. The molecular framework of Lisinopril is highlighted in magenta.
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synthesis reactions. Similarly, evolutionary design algorithms are easily adaptable to produce desired scaffold diversity of the products [24,25]. Instead of accounting for synthetic accessibility by explicit reaction-based compound construction, one can also rely on software that has especially been designed for synthesis planning. For example, the computer programs CAESA [26], SYLVIA [27], and RouteDesigner [28] propose synthesis plans for de novo generated compounds post hoc. Fragment-based molecular design intrinsically follows the concept of scaffold-hopping, which aims at identifying scaffold replacements in a known drug so that an NCE with the same or improved properties as the reference drug emerges. Intentionally such an approach mimics compound design in medicinal chemistry, where a human mind conceives of new
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molecular structures by drawing ideas from personal but individually unique knowledge. One needs to accept that any medicinal chemist’s choice is influenced and biased towards preferred regions of chemical space by years of training, and that such preferences affect the innovation potential of human-designed compounds – just like computational methods do. For the inclined user, own expertise may be complemented by computer-assisted scaffold-hopping and ultimately de novo design to come up with surprising but acceptable suggestions that can be actually realized in the laboratory and support hit and lead structure identification in drug discovery. A worked example of de novo design for the purpose of scaffold-hopping is presented in Fig. 3. In this pilot study [29], we applied our software Flux [30,31] to generating
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Figure 4. From receptor structure to a potent inhibitor by virtual screening (a), and conservative scaffold-hopping by fragment-based de novo design (b). The template structure for de novo design was obtained in a receptor-based virtual screen for Plk1 inhibitors [59]. It served as input for the software DOGS, which suggested a variation of the template together with a plausible synthesis route. The designed compound also strongly inhibits Plk1 activity [17]. The frameworks of both molecules are shown in blue color. They differ in the size of the rings and the linker in the eastern part of the structures.
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compounds that mimic the reference compound Lisinopril, a potent inhibitor of angiotensin converting enzyme (ACE). Flux used a selection of 10,497 drug-derived virtual building blocks and RECAP-type [32] connectivity for fragment-based molecular design, guided by an evolutionary algorithm (a (1, l) evolution strategy). The design objective was to optimize two-dimensional pharmacophore similarity (CATS descriptor) between the template (here: Lisinopril) and the virtually generated compounds. We condensed the initially large number of more than 250,000 suggestions by virtual screening for potential ACE inhibitors using a random forest [33] classifier model. The remaining 9416 candidate molecules featured a total of 2582 different scaffolds (graph frameworks). Only 55 scaffolds appeared more than 100 times in the final compound set, indicating strong algorithmic scaffold exploration. It is of note that all of the ten most frequent scaffolds are contained in known ACE inhibitors, which clearly mirrors chemotypes memorized by the classifier model. This exercise demonstrates that (i) useful molecular frameworks can be obtained by fragment-based de novo design, and (ii) recurring scaffold motifs might be of particular interest for further analysis. At the same time it becomes apparent that meaningful extrapolation from the domain of training or reference compounds is unlikely when (Q)SAR models are used for candidate compound triaging. An example of a minor scaffold variation is the potent typeII inhibitor of Polo-like kinase 1 (Plk1) which was generated by ligand-based de novo design using the DOGS method, and synthesized according to the reaction scheme proposed by
the software (Fig. 4) [17]. In analogy to biophysical fragmentfinding and extension [34], DOGS grows new compounds from a small start fragment so that virtual products are obtained that possess high pharmacophoric feature similarity to the template. This study suggests that conservative ligandbased de novo design may be applied to exploring the structural neighborhood of known actives in the absence of a known receptor structure. However, when reliable receptor models are available, structural optimization by small incremental modifications could also be performed by computationally more demanding free energy estimation methods like BOMB [35].
Against all odds – but take a look at it now! The central idea of scaffold-hopping is to find structurally different chemotypes with isofunctional bioactivity to a given template. As the actually relevant set of functiondetermining molecular features (substructures, pharmacophore points) is often unknown a priori, virtual screening and de novo design alike are typically performed with a permissive pharmacophore model to ensure sufficiently large scaffold variability among the matching compounds. The degree of ‘fuzziness’ or permissiveness of the model critically influences scaffold diversity in the hit lists (Fig. 5) [36]. Of note, the overlap between individual hit lists is not necessarily large, but occurrences of the same compounds in multiple screens (or consensus scoring approaches) argues for at least partially shared bioactivity (‘Authority Effect’) [37,38]. This observation is extremely useful for de novo design, as it
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Figure 5. Similarity searching with a conservative (model 1) or increasingly permissive pharmacophore hypotheses (models 2 and 3) results in distinctive virtual hit lists with limited overlap. Here, clopidogrel served as the template (query; left panel), and the software LIQUID [60] computed probabilistic pharmacophoric feature densities (green: lipophilic, red: hydrogen-bond acceptor). The top-scoring hits are shown together with their respective LIQUID pharmacophore model. The only compound retrieved from the screening compound pool by all three pharmacophore hypotheses is clopidogrel itself (right panel).
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COBRA
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Figure 6. Property landscapes of a collection of 11,230 drugs and lead compounds (COBRA v10.3) [61]. Molecules were represented by a 210dimensional pharmacophore descriptor (CATS2D v2) and projected to two dimensions using stochastic neighbor embedding [62] (SNE). Surfaces were computed using our software tool LiSARD [46]. Red color indicates undesired areas in chemical space, green color denotes regions containing compounds with desired features. Color intensity correlates with prediction confidence. Upper panel: The 2D distribution of the compound collection (left), and corresponding activity islands formed by factor Xa, PPARg, and cyclooxygenase (COX) ligands. Lower panel: Computed property landscapes for the number of hydrogen-bond donors, total polar surface area (TPSA), and lipophilicity (a log P); the combined landscape (TPSA + a log P) shown on the right indicates a pronounced preferred target area (arrow) for de novo design with regard to these two properties. Note that the combined landscape is tilted and slightly enlarged compared to the other visualizations to highlight hills and valleys. Physicochemical compound properties were computed using the software implementations in MOE (v2011, The Chemical Computing Group, Montreal, Canada).
provides a rationale for scoring and analyzing the numerous suggestions that can be obtained from a compound generator. Although the numbers of high-scoring molecules drop away rapidly as more selection criteria are applied, there is motivated hope that the fraction of actives increases. The general optimization paradigm requires acceptably smooth fitness landscapes [39,40], which seems to be fulfilled by using fragments (as opposed to atoms) as molecular building blocks for de novo design. Can we also predict the effect of seemingly minor structural modification on molecular properties, including affinity (e.g. ‘activity cliffs’), physicochemistry (e.g. aqueous solubility), and pharmacokinetics (e.g. oxidative metabolism)? Matched Molecular Pairs Analysis (MMPA) [41] has lately raised great interest in medicinal chemistry, and might also support de novo design in this regard [42,43]. Multi-objective optimization provides another concept for computer-assisted drug design that helps navigate in chemical space under the constraint of partially conflicting objective functions [44]. Focusing on compounds with sub-optimal but still acceptable properties might actually be key to success in de novo design, simply due to the fact that all predictive models are error-prone when applied to an individual molecule. Graphical visualization of the optimization progress in models of the underlying fitness landscape e458
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can help steer into the right direction and avoid artifact designs caused by over-optimization [45]. As an illustration, the graphical combination of two objective functions is presented in Fig. 6. The resulting fitness landscape was generated to implicitly address several design criteria while not being limited to compound potency as the sole driving force [46]. Similar visualizations with different underlying landscape models have been developed recently [47,48]. The appropriate mathematical framework is available to enable multidimensional optimization of synthetically accessible compounds as the next realistic step in practical computerassisted drug discovery. This encouraging development gives new hope for successful NCE identification by fragmentbased de novo design.
Conclusion: back to the future! One may wonder why computational de novo design has become fashionable again [49,50]. Although the basic ideas were conceived approximately three decades ago, its essential ingredients have remained the same – but with a new twist, admittedly. We have come a long way ever since, and have learned from earlier failure. Contemporary approaches include fragment-based compound assembly, a variety of scoring schemes that allow for nonlinear contributions,
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explicit or implicit consideration of synthetic feasibility of the designs, and multi-objective optimization addressing multiple targets and properties. Existing software solutions focus on each of these features to a different extent, and it is only a matter of time and effort to implement working and applicable de novo design tools that actually suggest synthesizable compounds exhibiting desired polypharmacology [51]. Proven scaffold-hopping potential might actually be key to successful identification of NCEs, and ligand-based de novo methods offer themselves as premier tools for rapid analog design. In future applications, de novo compound generators could also be directly linked to patent databases to identify most promising candidate scaffolds and potential activity profiles. Another idea is to compare designed compounds to databases such as ZINC [52], which contain physically available substances with known synthetic accessibility. This could flag up compounds that share the scaffold (both as a framework and more particularly with specific heteroatoms) with a design, to obtain confidence to the feasibility of the design and a potential shortcut to synthetic work. While there has been notable progress during the past years, several limiting factors still prevent de novo design approaches from being overly successful [1]. For example, water molecules modify the size, shape and pharmacophoric features of a ligand-binding pocket, thereby critically affecting the diversity of chemotypes that can be accommodated [53]. Mancera and coworkers were among the first to investigate the role that water molecules, specifically hydration sites in the receptor cavity, can have on the structural interpretation of ligand-derived pharmacophore models [54]. Since water molecules are known to influence molecular diversity in receptor-based design [55], their importance clearly needs to be recognized in the context of ligand-based approaches. Another major limitation originates from insufficient consideration of entropic effects upon ligand binding and consequently the still largely unsolved scoring problem [56], although recent developments of ab initio approaches, like the fragment molecular orbital method, point towards progress in this regard for structure-based de novo design [57]. In the author’s view, the most critical part in computational drug design has always been its actual practical realization, so that rapid feedback loops enable adaptive learning. As long as coupling computer-based design to immediate automated synthesis and testing will remain a future (but certainly attainable) goal, teams of dedicated modelers and steadfast chemists will have to do the hard work. Despite all enthusiasm, we should not forget that any computational prediction is error-prone, and there currently are too few worked examples to allow for a sound statistical assessment of success rates in de novo design. This fact alone should encourage us to find out.
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Acknowledgements The author is grateful to his coworkers and collaborating partners for realizing exciting molecular design studies. Dr. David Lloyd is thanked for helpful comments on the manuscript. The work presented was financially supported by ETH ¨ rich, the Swiss National Science Foundation (SNSF, grant Zu 205321-134783), Deutsche Forschungsgemeinschaft (DFG, ¨ rich. The Chegrant FOR1406_TP4), and OPO-Foundation Zu mical Computing Group Inc. provided a research license of the software MOE. The funding sources had no such involvement in this study.
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