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Recent Progress in Structure-Based Evaluation of Compound Promiscuity Erik Gilberg†,‡ and Jürgen Bajorath*,† †
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Department of Life Science Informatics, B-IT, LIMES Program Unit Chemical Biology and Medicinal Chemistry, Rheinische Friedrich-Wilhelms-Universität, Endenicher Allee 19c, D-53115 Bonn, Germany ‡ Pharmaceutical Institute, Rheinische Friedrich-Wilhelms-Universität, An der Immenburg 4, D-53121 Bonn, Germany ABSTRACT: In chemistry and drug discovery, compound promiscuity is a controversial issue and often viewed differently. On the one hand, promiscuity has a clear-cut negative connotation, as promiscuous behavior of small molecules is often associated with nonspecific binding or assay artifacts. On the other hand, it is also well-established that compounds frequently interact with multiple targets including distantly related or unrelated proteins. In drug discovery, multitarget activity of small molecules receives increasing attention because it is therapeutically relevant, giving rise to desired or undesired pharmacological effects. Exploration of compound promiscuity is far from being simple because true and artificial activities are often difficult to discern. In light of these complications, X-ray structures of ligand−target complexes provide a wealth of information about molecular promiscuity, which is just beginning to be recognized and explored. Systematic analysis of structurally confirmed binding events involving artifact-prone compounds or multitarget ligands eliminates some of the uncertainties associated with promiscuity analysis and puts it on a new level, enabling the study of promiscuity-conferring interactions at the molecular level of detail. Herein, we discuss recent progress made in structure-based promiscuity analysis and put key findings into scientific context. inhibitor drug development in oncology19,20 and is also emerging in other therapeutic areas.16−18 However, polypharmacology is also critically viewed in pharmaceutical research because it represents a major departure from the single-target specificity paradigm,21,22 which has long governed discovery efforts. The extent to which drugs and other bioactive compounds might be promiscuous also continues to be debated and views often differ here as well. Several estimates and expectation values have been put forward. For example, on the basis of drug−target network analysis, it has been proposed that drugs might interact with on average 3−13 targets,23,24 depending on drug classes and data sets used. Data incompleteness certainly affects promiscuity estimates23 because not all drugs have been tested in vitro against all possible targets (and likely never will be). Hence, not surprisingly, it has been presumed that drugs should generally be promiscuous.14,23 However, currently available activity data do not support expectation values claiming the presence of widespread and strong promiscuity among drugs and other bioactive compounds.25−27 There are several lines of evidence to balance such expectations. For example, even most extensively tested screening compounds are on average only active against two to three targets (with a median value of two targets) and the majority of compounds
1. INTRODUCTION Apparent multitarget activity of small molecules, also referred to as promiscuity,1 is a controversial issue in chemistry and pharmaceutical research for a variety of reasons. First of all, it is often difficult to draw a line between “true” promiscuity and apparent promiscuity resulting from assay artifacts.2 Such artifacts might be caused by aggregators3−5 or other molecules with assay interference potential such as pan-assay interference compounds (PAINS),6−8 which may have a variety of undesired reactivities under assay conditions. PAINS typically occur as substructures in larger compounds. Assay interference effects are often difficult to recognize. To further complicate matters, even notorious PAINS do not display consistent activity patterns. Compounds containing such PAINS substructures might be frequently active in screening assays, rarely active, or consistently inactive, depending on how these substructures are embedded.9,10 Moreover, some well-known assay interference compounds also have a long history as privileged structures in medicinal chemistry.11,12 Hence, distinguishing target-based activity and promiscuous ligand-binding events from artificial activity signals is far from being a trivial task.13 On the other hand, true multitarget activity of compounds is of increasing relevance in drug discovery because it provides the basis of polypharmacology.14−18 The concept of polypharmacology implies that drugs often elicit their therapeutic effects through in vivo interactions with multiple targets. Polypharmacology has become a major theme for kinase © 2019 American Chemical Society
Received: December 27, 2018 Accepted: January 29, 2019 Published: February 6, 2019 2758
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are consistently inactive.25 Moreover, comprehensive analysis of high-confidence activity data has revealed that the majority of bioactive compounds from medicinal chemistry including drugs are only annotated with a single target and that promiscuous compounds are rare.26,27 Hence, further largescale experimental assessment of single- vs multitarget activity will be required to assess the influence of data incompleteness, advance our understanding of molecular promiscuity, and evaluate preconceived notions of polypharmacological drug actions that cannot be generalized at present. It is confirmed, however, that confined subsets of highly promiscuous compounds and drugs exist19,26 and that multitarget engagement is highly relevant for a number of therapeutic applications.18,19 In addition, multitarget activity also plays a pivotal role for repurposing of existing drugs.28,29 Given the topical nature of molecular promiscuity and the uncertainties associated with its assessment, a variety of computational approaches have been introduced to study promiscuity patterns and/or predict promiscuous compounds. For example, promiscuity was analyzed computationally through systematic compound data mining9,10,25−27,30 and ligand−target network modeling.14,24 In addition, statistically balanced ligand-based similarity searching was employed to infer new targets of active compounds,29,31 algorithms were introduced to detect promiscuity patterns at the level of molecular core structures,32 and machine learning models were developed to systematically distinguish between promiscuous and inactive interference compounds.33 Furthermore, promiscuity was also studied at the target structure level. In particular, protein binding site similarity was analyzed to explain multitarget activity of bound ligands.34,35 In addition, drugs with multitarget activity were assigned to proteins having similar functions on the basis of X-ray structures.36 Hence, promiscuity as the basis of polypharmacology has been, and continues to be, investigated from different perspectives but rationalizing multitarget activity of small molecules is far from being simplistic.
ity analysis was to determine whether or not a sufficient number of X-ray structures containing promiscuous ligands were available.
3. SEARCHING FOR X-RAY STRUCTURES WITH PROMISCUOUS LIGANDS Therefore, we searched the RCSB Protein Data Bank (PDB)37 and its Ligand Expo section38 for complex structures with bound promiscuous compounds. This was required to identify ligands that were complexed with different proteins and classify these targets. Activity measurements for ligands were retrieved from PDBbind39 and ChEMBL40 databases. Crystallographic target proteins were assigned to families on the basis of UniProt identifiers41 and the ChEMBL target classification scheme. In addition, for qualifying crystallographic ligands, a computational search for structural analogues42 was carried out in ChEMBL and in the PDB. To prioritize template compounds from medicinal chemistry for the design of ligands with multitarget activity (see below), X-ray ligands were mapped to ChEMBL and structural analogues available in ChEMBL were identified. We initially searched for X-ray complexes that contained compounds with PAINS substructures. In this case, the initial goal was to determine how frequently PAINS that tend to be reactive under assay conditions might form specific ligand− target interactions. Then, PAINS-containing complex structures were analyzed in detail. Our systematic search identified an unexpectedly large number of 2874 X-ray structures containing 1107 compounds with 70 different PAINS substructures, including many wellknown assay interference motifs.43 Thus, a large number of PAINS motifs were found in ligand−target complexes, revealing their ability to engage in specific interactions, despite assay liabilities. These structures provided a basis for investigating binding of PAINS vs potential reactivity in a variety of protein environments,43 as further discussed below. Subsequently, we identified crystallographic ligands bound to multiple targets (termed multitarget ligands) and targets from different families (multifamily ligands). Such ligands were considered to represent prime examples of promiscuous compounds with confirmed target-based interactions. Multifamily ligands were of particular interest because they bound to distantly related or unrelated targets and hence provided test cases for exploring how specific interactions were formed in distinct binding sites. Again, an unexpectedly large number of 1418 crystallographic multitarget ligands (with molecular weight >300 Da) were identified, which contained 702 multifamily ligands.44 These multifamily ligands were active against a median of three targets from two families. For 355 ligands available in ChEMBL, the median target number increased to 17 (but the family median remained at 2). For 168 of the 702 multifamily ligands, series of analogues (comprising a total of 4829 compounds covering 190 additional targets) were identified in ChEMBL, which yielded 133 different analogue series-based (ASB) scaffolds45 as templates for polypharmacology-directed compound design. “Polypharmacology by design” represents an extension of the polypharmacology concept and another emerging theme in drug discovery.46−48 Multifamily ligands we identified also included a subset of 192 compounds for which one or more potency values of at least 10 μM or lower (pIC50, pKi, or pKd ≥ 5) were reported in
2. PROMISCUOUS COMPOUNDS AND THE QUEST FOR CONFIRMATORY EVIDENCE Given the uncertainties associated with interpreting compound promiscuity on the basis of assay data, the question becomes how to best evaluate multitarget binding events. In our view, the most conservative and least error-prone approach to exploring compound promiscuity is the analysis of X-ray structures of ligand−target complexes with promiscuous compounds. Of course, X-ray data also have intrinsic limitations. For example, details of ligand−target interactions might not always be fully discernible due to limited resolution or other crystallographic ambiguities. Moreover, X-ray structures of ligand−target complexes provide a time-average static view of interactions that might well be dynamically driven and as such difficult to reconcile. However, in light of possible assay artifacts, a major advantage of focusing promiscuity assessment on X-ray structures is that these structures confirm binding of a chemical entity to a given target. Simply put, we can “see” electron density of a bound ligand and know a compound is active against the target. In addition, complex X-ray structures enable the study of multitarget activity at the molecular level of detail. A major limitation of structure-based promiscuity analysis is the sparseness of X-ray structures relative to biological assay data. Thus, an essential prerequisite for meaningful promiscu2759
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compound databases. Hence, in these instances, activity measurements corroborated structural evidence of specific binding events. This subset of multifamily ligands was further analyzed to rationalize and compare multifamily interactions in detail.49 The 192 ligands were represented by a total of 3378 X-ray structures that involved proteins from 2 to 16 different families per ligand. The multifamily ligands were found to contain 40 endogenous compounds and 10 others that preferentially bound to metabolizing enzymes or serum proteins. For each of the remaining 142 ligands, the targets were compared in detail to identify ligands that interacted with structurally or functionally related proteins assigned to different families, similar binding domains in different proteins, or distinct targets. On the basis of this analysis, a subset of 91 multifamily ligands was identified that exclusively bound to unrelated proteins. This subset was assigned highest priority for promiscuity analysis and their complexes were studied in detail.49 Taken together, analyses of complex X-ray structures yielded an unexpectedly large knowledge base of multitarget and multifamily ligands, which provided a sound basis for structurebased promiscuity analysis. In the following, key findings from our studies43,44,49 are discussed using representative examples.
4. STRUCTURE-BASED COMPOUND PROMISCUITY ANALYSIS 4.1. Assay Interference Compounds: Specific Interactions vs Artifacts. Figure 1 shows different complexes formed by assay interference compounds, which illustrate their dual ability to engage in specific ligand−target interactions or cause artifacts in assays. Figure 1a shows quercetin in complex with two unrelated targets including a kinase and a Niquercetinase, an oxidoreductase. Quercetin contains a catechol substructure, a prominent PAINS motif known to cause assay artifacts due to undesired metal chelation, redox activity, reactivity against biological nucleophiles in its oxidized form, or membrane perturbation.5,7,50,51 Catechol substructures are not only found in synthetic compounds but also in a variety of natural products. Quercetin’s catechol moiety is part of a polyphenolic flavonoid scaffold. In drug discovery, flavonoids have for long been considered privileged structures52 that preferentially interact with kinases,53 DNA,54 or oxidoreductases.55 The ability of quercetin and related polyphenolic flavonoids to bind to these targets was confirmed by many Xray structures. Quercetin is a relatively small and rigid ligand, and its reactive catechol moiety participates in the formation of specific interactions. For example, in the kinase complex, it contributes to a hydrogen-bond network involving water molecules and residue Glu64 (Figure 1a, left).53 In contrast, in the active site of Ni-quercetinase, the catechol forms π-stacking interactions with Phe78 and a backbone hydrogen bond with residue Val77 (Figure 1a, right).55 Clearly, despite their reactivity and biological membrane perturbation potential, quercetin and other polyphenolic flavonoids are capable of engaging in specific target−ligand interactions, consistent with their proposed privileged structure character. It follows that apparent activity of such catecholcontaining compounds must be carefully evaluated on a caseby-case basis.14,43 Importantly, neither artificial reactivity nor specific activity can be generally assumed. Figure 1b shows a contrasting example revealing consequences of an interference mechanism. Unsaturated fivemembered heterocycles such as rhodanines represent another
Figure 1. X-ray structures with assay interference compounds. Bound crystallographic ligands are shown (carbon atoms are colored green) that contain PAINS substructures implicated in assay interference mechanisms. Protein backbone ribbons and carbon atoms of selected residues are colored in silver or orange. Other ligand and protein atoms are shown using standard atom coloring. Residues discussed in 2760
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Figure 1. continued the text are labeled. In molecular graphs, PAINS substructures are colored red. Activity values are reported, if available. (a) Quercetin (PAINS code: catechol_A) in complex with death-associated protein kinase 1 (left, PDB ID: 5AUW) and Ni-quercetinase (right, PDB ID: 5FLJ). In the X-ray structures, the catechol moiety is colored dark red. (b) Inhibitory reaction product in complex with metallo-β-lactamase type 2 (PDB ID: 4PVO) resulting from hydrolysis of an unsaturated rhodanine (PAINS code: ene_rhod_A). The figure was adopted from ref 43 and modified. (c) Mannich base-type inhibitor (PAINS code: mannich_A) in the active site of polyketide synthase 13 (PDB ID: 5V3X). The figure was adopted from ref 43 and modified. All structural representations were generated with Molecular Operating Environment.68
class of prominent PAINS but have also been considered as privileged scaffolds in drug discovery,11,56 similar to flavonoids. Rhodanines are prone to ring-opening reactions and have a strong tendency to react with nucleophilic thiol groups through a Michael-type addition at the exocyclic double bond.57,58 As shown in Figure 1b (bottom left), β-lactamase was cocrystallized with an arylidene rhodanine inhibitor but this inhibitor was not found in the X-ray structure. Rather, the enzyme formed a complex with a smaller hydrolyzed thioenolate that was produced by the rhodanine ring-opening reaction (Figure 1b, bottom right).59 This degradation product specifically bound to β-lactamase with well-defined interactions. The thiol and hydroxyl groups of the inhibitor formed an interaction network with the zinc cations in the active site involving residues Asp120, Cys221, and His263. Hence, the Xray structure identified a reaction product of unsaturated rhodanine as the active component and illustrated the outcome of an interference mechanism. The structurally confirmed “pseudo prodrug” character of rhodanines should be taken into consideration when judging potential activities of these compounds and might also be further explored and exploited in compound design. Figure 1c shows another example of a well-known PAINS motif, a Mannich base. In this case, however, the structural context in which the Mannich base was presented restricted its reactivity and led to specific inhibition. The assay interference potential of Mannich bases primarily depends on their ability to form reactive quinone methides.60 Notably, this structural motif also frequently occurs in natural products.60 However, as shown in Figure 1c, the Mannich base inhibitor complexed with polyketide synthase 1361 contained a tertiary amine as part of an aliphatic piperidine ring system that prevented the formation of the reactive tautomer. Moreover, in the X-ray structure, the aliphatic amine formed a charge-assisted hydrogen bond with the side chain of Asn1640, whereas the phenolic core of the inhibitor was involved in aromatic stacking interactions with Phe1670. Hence, in this case, the structural context of the PAINS motif impeded reactivity and the stabilizing chemical modification led to a potent inhibitor of polyketide synthase. 4.2. Crystallographic Multitarget Compounds and Expansion into Analogue Space. The many crystallographically characterized multitarget and multifamily compounds we identified yielded extensive target coverage and displayed a variety of binding patterns. Figure 2 shows a representative example, the matrix metalloproteinase inhibitor galardin62 in a complex with the zinc metalloproteinase domain of anthrax toxin.63 Galardin was found in six X-ray
Figure 2. Multitarget inhibitor and promiscuous analogues. Shown is galardin bound to the zinc metalloproteinase domain of anthrax toxin (PDB ID: 4PKW). The inhibitor was present in six X-ray structures with three different targets from the same family. The ASB scaffold45 representing a galardin analogue series with modifications at two sites (R1 and R2) is shown on the lower left. Analogues were reported to be active against a total of 22 target proteins from 10 different families. “#” means “number of”. The figure was adopted from ref 44 and modified.
structures bound to three different proteases. Thus, following our classification scheme, galardin was a crystallographic multitarget ligand. A computational search for structural analogues of galardin in ChEMBL identified 22 compounds with substitutions at two sites. The ASB scaffold representing the analogue series is also shown in Figure 2 (bottom left). Taken together, for the analogues, activities against a total of 22 targets belonging to 10 different families were reported, hence suggesting additional target hypotheses for galardin and closely related compounds and indicating high potential for polypharmacology. Thus, galardin was among the structurally confirmed promiscuous compounds for which analogue series were identified. These series provided more than 100 ASB scaffolds as templates for polypharmacological ligand design.44 4.3. Multifamily Compounds and Varying Interaction Hotspots. Given the unexpectedly large number of crystallographic multifamily compounds we identified, a particularly interesting question was how these compounds facilitated binding to distantly or unrelated targets.49 Figure 3 shows a representative example. Indomethacin is a drug with known polypharmacology and a multifamily ligand for which a variety of crystallographic complexes with unrelated targets were available including cyclooxygenases,64 peroxisome proliferatoractivated receptor γ (PPARγ),65 phospholipases,66 and serum albumin.67 As shown in Figure 3, indomethacin bound with similar conformations to cyclooxygenase-2 (top left) and PPARγ (top right), with an all-atom root-mean-square deviation of 1.2 Å. However, in these complexes, polar and van der Waals (vdW) interactions were centered on different regions of indomethacin such that different atoms became target-dependent interaction hotspots. In cyclooxygenase-2, πstacking interactions were formed with the central indole core 2761
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family. Nonetheless, the quest for specificity has been the guiding principle in compound design and development for many years. Prior to the availability of profiling assays and compound arrays covering many different targets, specificity was typically evaluated by testing active compounds against their primary (intended) target and a few related ones. Hence, it is not surprising that a rather narrow view of in vitro specificity has dominated drug development for long time. Even the first clinical kinase inhibitors such as imatinib, approved for cancer treatment 15 years ago, were originally thought to be kinase-specific, before it was discovered that they were promiscuous and that polypharmacology was responsible for their efficacy. Kinase inhibitors were only the beginning. Over the past two decades, increasing insights into the complex multifactorial nature and cellular network dependence of many diseases were complemented with mounting evidence that drugs were often active against multiple targets. Consequently, polypharmacology became a new paradigm in drug discovery. Evidence of multitarget activity helped rationalize desired therapeutic as well as undesired side effects. Furthermore, several existing drugs were identified to interact with unrelated targets having diverse functions, which provided the conceptual basis for drug repurposing. Given the increasing popularity of the polypharmacology paradigm, it was frequently proposed that drugs would generally act on multiple targets. In contrast, examples of drugs requiring target specificity were emphasized such as agents used for the treatment of chronic diseases. Such assumptions and controversial views prompted us and others to have a careful look at large collections of bioactive compounds and activity data and determine the frequency and magnitude of promiscuity. Large-scale analyses of activity data, including data from high-throughput screening, did not support the assumption that drugs and other bioactive compounds would generally be promiscuous. Confined subsets of highly promiscuous drugs and other bioactive compounds were identified, including clinical kinase inhibitors. However, the majority of kinase inhibitors reported to date in the medicinal chemistry literature are only annotated with a single kinase. This also applies to other active compounds covering the current spectrum of therapeutic targets. In this context, data incompleteness must be taken into consideration, giving rise to principally low data-driven promiscuity estimates. On the other hand, currently available volumes of compounds and activity data are already so large that statistical trends deduced from these data cannot be disregarded, indicating that data sparseness alone can hardly be responsible for overall low promiscuity detected among bioactive compounds. These observations do not call the principles of polypharmacology into question; neither does polypharmacology per se invalidate the compound specificity paradigm. Rather, it is likely that there is a balance between polypharmacological and target-specific drug actions and ensuing in vivo effects. Regardless, increasing attention has also been paid to the generation of compounds with activity against two or more therapeutic targets of interest. Going forward, multitarget compound design will greatly benefit from a deeper and more refined understanding of multitarget activity at the molecular level of detail. The discussion of structure-based promiscuity analysis presented herein should be viewed in light of the above considerations. Moreover, in this context, another critically important aspect must be taken into account. Promiscuity
Figure 3. Crystallographic interaction hotspots of multifamily ligands. Shown is indomethacin, an exemplary multifamily ligand, bound to cyclooxygenase-2 (left, orange, PDB ID: 4COX) and peroxisome proliferator-activated receptor γ (right, blue, PDB ID: 3ADS). Dashed lines represent polar ligand−target interactions. In each complex, polar and van der Waals (vdW) interactions were determined for each ligand atom using Molecular Operating Environment68 and mapped onto the molecular graph of the ligand.69 Atom positions were colorcoded according to vdW or polar interaction counts using a continuous color spectrum (light, minimal (min) number of respective interactions; dark, maximal (max) number).69 Polar and vdW interactions were separately counted for each participating protein and ligand atom (e.g., if a ligand atom was in vdW contact with three protein atoms, the interaction count was 3). The conformation of indomethacin was similar in both binding sites. However, the two-dimensional ligand depictions reveal the formation of different interaction hotspots in these binding sites.
of the ligand and hydrogen bonds with the polarized methoxy group, whereas the 4-chlorophenyl substituent and an aliphatic methyl residue were involved in vdW contacts. In contrast, in PPARγ, the 4-chlorophenyl moiety participated in stacking interactions and both the methoxy group and the indole core were involved in vdW contacts. In the presence of similar binding conformations, the formation of different targetdependent interaction hotspots provided a molecular rationale for multifamily binding of indomethacin. Equivalent observations were made for many multifamily ligands that interacted with distinct proteins.49
5. PERSPECTIVE Generating active compounds that are target-specific has long been a paradigm in pharmaceutical research. It is well known that specificity is often difficult to achieve, especially among closely related targets such as members of the same enzyme 2762
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proteins, confirmed by manual curation of structural data. These compounds provide an extensive knowledge base for rationalizing promiscuity at the molecular level of detail. A key question is how small molecules interact with diverse targets. Are there common patterns? Are the interactions specific that these compounds form in different binding sites? Previous studies have largely focused on exploring binding site similarity to rationalize promiscuous binding events, mostly on a case-bycase basis. Binding site similarity encompasses geometry and shape as well as chemical features, and it is intuitive that similar binding sites should be likely to accommodate the same or similar ligands. This usually applies to members of the same protein family. Our extensive knowledge base of multifamily ligands has enabled us to go further and systematically explore binding conformations of ligands in different binding sites, including unrelated targets, and quantitatively analyze their interaction patterns. Many multifamily ligands interacting with distinct proteins were found to display surprisingly similar binding conformations in different active sites. Many of these active sites had only little or no detectable similarity. Similar binding conformations were not only observed for small and fairly rigid ligands but also for increasingly flexible compounds. In other cases, such ligands bound in different conformations and formed different types of interactions. However, similar binding conformations in different active sites generally resulted in the formation of distinct interaction hotspots. In our studies, the formation of different interaction hotspots on the basis of preferred binding conformations has emerged as a promiscuity-conferring mechanism, which helps us to better understand how small molecules are capable of interacting with unrelated targets. In conclusion, in this work, we have discussed the potential of structure-based promiscuity analysis by focusing on crystallographic ligands in complexes with different targets. Our analyses have relied on the systematic identification of such ligands, which yielded a large, and previously unconsidered, knowledge base of crystallographically confirmed multitarget and multifamily compounds. The structures revealed a variety of binding characteristics and helped to rationalize promiscuity at the molecular level of detail. Promiscuous X-ray ligands we have identified and associated activity information have been made freely available as a part of the original studies.43,44,49
analysis is often influenced by artifacts, which further complicate matters. Importantly, just around the time when the first promiscuous kinase inhibitors were in final stages of clinical development, evidence was mounting that artificial activity readouts were affecting biological assays on a large scale due to various interference mechanisms. Compound aggregation as a cause of activity artifacts first emerged as a common threat to biological screening and medicinal chemistry. A few years later, different facets of chemical reactivity were established as recurrent assay interference mechanisms. These caveats also complicate the study of compound promiscuity and provide another compelling reason to investigate promiscuity at the level of X-ray structures confirming binding events. As we have shown, currently available X-ray data yield many crystallographic multitarget and multifamily ligands, more so than we originally anticipated, which establish a sound basis for structure-based promiscuity exploration. Structurally characterized assay interference compounds such as PAINS provide prototypes for rationalizing promiscuity at different levels. Representative examples presented herein delineate a wide spectrum of possible effects. Even prominent PAINS with undeniable assay interference potential such as catechols or rhodanines have the structurally confirmed ability to interact with a variety of targets. PAINS-containing ligands can be truly promiscuous, although they might also cause assay artifacts. Accordingly, the privileged structure character of, for example, flavonoids or rhodanines, which has long been considered in medicinal chemistry, cannot simply be disregarded, despite their interference potential. However, we have also shown that degradation products of interference compounds might be active and form specific ligand−target interactions. Moreover, chemical modifications of PAINS can prevent undesired reactions to occur and contribute to targetspecific binding. These observations emphasize the critically important role of the structural context in which PAINS or other interference motifs occur. As discussed, there is firm crystallographic evidence for different binding characteristics. Taken together, these findings strongly suggest that activity of interference compounds must be carefully evaluated and judged on a case-by-case basis. Disregarding the ability of such compounds to specifically interact with diverse targets would be as careless scientifically as disregarding their interference potential. We have also shown that taking (noncrystallographic) analogues of multitarget and multifamily compounds into account provides additional information. Compounds forming an analogue series are likely to have similar binding characteristics and share targets. Thus, by considering analogues of crystallographic ligands, additional targets might be inferred, as long as the underlying activity data are carefully curated and reliable. The larger the analogue series containing crystallographic ligands, the more the opportunities to evaluate the consistency of activity annotations and identify “outliers”. Hence, complementing X-ray structures of multitarget or multifamily ligands with analogue series and medicinal chemistry data is a promising approach, as long as the accumulated data are not overinterpreted. We have identified more than 700 crystallographic multifamily ligands in complexes with distantly related or unrelated targets, and for nearly 200 of these ligands, well-defined activity measurements were available. A subset of nearly 100 of these multifamily ligands exclusively interacted with unrelated
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AUTHOR INFORMATION
Corresponding Author
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[email protected]. Phone: +49-228-7369100. ORCID
Jürgen Bajorath: 0000-0002-0557-5714 Author Contributions
The study was carried out and the manuscript was written with contributions of all authors. All authors have approved the final version of the manuscript. Notes
The authors declare no competing financial interest.
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ACKNOWLEDGMENTS We thank OpenEye and Chemical Computing Group for academic software licenses. 2763
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