Are Target-Family-Privileged Substructures Truly Privileged?

Synthesis and SAR investigation of biphenylaminoquinoline derivatives with benzyloxy substituents as promising anticancer agents

The biphenyl scaffold represents a prominent privileged structure within the realms of organic chemistry and drug development. Biphenyl derivatives have demonstrated notable biological activities, including antimicrobial, anti-inflammatory, anti-HIV, and the treatment of neuropathic pain. Importantly, their anticancer abilities should not be underestimated. In this context, the present study involves the design and synthesis of a series of biphenyl derivatives featuring an additional privileged structure, namely the quinoline core. We have also diversified the substituents attached to the benzyloxy group at either the meta or para position of the biphenyl ring categorized into two distinct groups: [4,3']biphenylaminoquinoline-substituted and [3,3']biphenylaminoquinoline-substituted compounds. We embarked on an assessment of the cytotoxic activities of these derivatives in colorectal cancer cell line SW480 and prostate cancer cell line DU145 for exploring the structure-activity relationship. Furthermore, we determined the IC50 values of selected compounds that exhibited superior inhibitory effects on cell viability against SW480, DU145 cells, as well as MDA-MB-231 and MiaPaCa-2 cells. Notably, [3,3']biphenylaminoquinoline derivative 7j displayed the most potent cytotoxicity against these four cancer cell lines, SW480, DU145, MDA-MB-231, and MiaPaCa-2, with IC50 values of 1.05 μM, 0.98 μM, 0.38 μM, and 0.17 μM, respectively. This highly promising outcome underscores the potential of [3,3']biphenylaminoquinoline 7j for further investigation as a prospective anticancer agent in future research endeavors.

Challenges of Protein-Protein Docking of the Membrane Proteins

Despite the recent advances in the determination of high-resolution membrane protein (MP) structures, the structural and functional characterization of MPs remains extremely challenging, mainly due to the hydrophobic nature, low abundance, poor expression, purification, and crystallization difficulties associated with MPs. Whereby the major challenges/hurdles for MP structure determination are associated with the expression, purification, and crystallization procedures. Although there have been significant advances in the experimental determination of MP structures, only a limited number of MP structures (approximately less than 1% of all) are available in the Protein Data Bank (PDB). Therefore, the structures of a large number of MPs still remain unresolved, which leads to the availability of widely unplumbed structural and functional information related to MPs. As a result, recent developments in the drug discovery realm and the significant biological contemplation have led to the development of several novel, low-cost, and time-efficient computational methods that overcome the limitations of experimental approaches, supplement experiments, and provide alternatives for the characterization of MPs. Whereby the fine tuning and optimizations of these computational approaches remains an ongoing endeavor.Computational methods offer a potential way for the elucidation of structural features and the augmentation of currently available MP information. However, the use of computational modeling can be extremely challenging for MPs mainly due to insufficient knowledge of (or gaps in) atomic structures of MPs. Despite the availability of numerous in silico methods for 3D structure determination the applicability of these methods to MPs remains relatively low since all methods are not well-suited or adequate for MPs. However, sophisticated methods for MP structure predictions are constantly being developed and updated to integrate the modifications required for MPs. Currently, different computational methods for (1) MP structure prediction, (2) stability analysis of MPs through molecular dynamics simulations, (3) modeling of MP complexes through docking, (4) prediction of interactions between MPs, and (5) MP interactions with its soluble partner are extensively used. Towards this end, MP docking is widely used. It is notable that the MP docking methods yet few in number might show greater potential in terms of filling the knowledge gap. In this chapter, MP docking methods and associated challenges have been reviewed to improve the applicability, accuracy, and the ability to model macromolecular complexes.

The Importance of the Pyrazole Scaffold in the Design of Protein Kinases Inhibitors as Targeted Anticancer Therapies

The altered activation or overexpression of protein kinases (PKs) is a major subject of research in oncology and their inhibition using small molecules, protein kinases inhibitors (PKI) is the best available option for the cure of cancer. The pyrazole ring is extensively employed in the field of medicinal chemistry and drug development strategies, playing a vital role as a fundamental framework in the structure of various PKIs. This scaffold holds major importance and is considered a privileged structure based on its synthetic accessibility, drug-like properties, and its versatile bioisosteric replacement function. It has proven to play a key role in many PKI, such as the inhibitors of Akt, Aurora kinases, MAPK, B-raf, JAK, Bcr-Abl, c-Met, PDGFR, FGFRT, and RET. Of the 74 small molecule PKI approved by the US FDA, 8 contain a pyrazole ring: Avapritinib, Asciminib, Crizotinib, Encorafenib, Erdafitinib, Pralsetinib, Pirtobrutinib, and Ruxolitinib. The focus of this review is on the importance of the unfused pyrazole ring within the clinically tested PKI and on the additional required elements of their chemical structures. Related important pyrazole fused scaffolds like indazole, pyrrolo[1,2-b]pyrazole, pyrazolo[4,3-b]pyridine, pyrazolo[1,5-a]pyrimidine, or pyrazolo[3,4-d]pyrimidine are beyond the subject of this work.

The pyridazine heterocycle in molecular recognition and drug discovery

The pyridazine ring is endowed with unique physicochemical properties, characterized by weak basicity, a high dipole moment that subtends π-π stacking interactions and robust, dual hydrogen-bonding capacity that can be of importance in drug-target interactions. These properties contribute to unique applications in molecular recognition while the inherent polarity, low cytochrome P450 inhibitory effects and potential to reduce interaction of a molecule with the cardiac hERG potassium channel add additional value in drug discovery and development. The recent approvals of the gonadotropin-releasing hormone receptor antagonist relugolix (24) and the allosteric tyrosine kinase 2 inhibitor deucravacitinib (25) represent the first examples of FDA-approved drugs that incorporate a pyridazine ring. In this review, the properties of the pyridazine ring are summarized in comparison to the other azines and its potential in drug discovery is illustrated through vignettes that explore applications that take advantage of the inherent physicochemical properties as an approach to solving challenges associated with candidate optimization.

Graphical Abstract

Quantitative and Qualitative Analysis of the Anti-Proliferative Potential of the Pyrazole Scaffold in the Design of Anticancer Agents

The current work presents an objective overview of the impact of one important heterocyclic structure, the pyrazole ring, in the development of anti-proliferative drugs. A set of 1551 pyrazole derivatives were extracted from the National Cancer Institute (NCI) database, together with their growth inhibition effects (GI%) on the NCI’s panel of 60 cancer cell lines. The structures of these derivatives were analyzed based on the compounds’ averages of GI% values across NCI-60 cell lines and the averages of the values for the outlier cells. The distribution and the architecture of the Bemis–Murcko skeletons were analyzed, highlighting the impact of certain scaffold structures on the anti-proliferative effect’s potency and selectivity. The drug-likeness, chemical reactivity and promiscuity risks of the compounds were predicted using AMDETlab. The pyrazole ring proved to be a versatile scaffold for the design of anticancer drugs if properly substituted and if connected with other cyclic structures. The 1,3-diphenyl-pyrazole emerged as a useful scaffold for potent and targeted anticancer candidates.

Discovery of an Unexpected Similarity in Ligand Binding between BRD4 and PPARγ

Knowledge about interrelationships between different proteins is crucial in fundamental research for the elucidation of protein networks and pathways. Furthermore, it is especially critical in chemical biology to identify further key regulators of a disease and to take advantage of polypharmacology effects. Here, we present a new concept that combines a scaffold-based analysis of bioactivity data with a subsequent screening to identify novel inhibitors for a protein target of interest. The initial scaffold-based analysis revealed a flavone-like scaffold that can be found in ligands of different unrelated proteins indicating a similarity in ligand binding. This similarity was further investigated by testing compounds on bromodomain-containing protein 4 (BRD4) that were similar to known ligands of the other identified protein targets. Several new BRD4 inhibitors were identified and proven to be validated hits based on orthogonal assays and X-ray crystallography. The most important discovery was an unexpected relationship between BRD4 and peroxisome-proliferator activated receptor gamma (PPARγ). Both proteins share binding site similarities near a common hydrophobic subpocket which should allow the design of a polypharmacology-based ligand targeting both proteins. Such dual-BRD4-PPARγ modulators open up new therapeutic opportunities, because both are important drug targets for cancer therapy and many more important diseases. Thereon, a complex structure of sulfasalazine was obtained that involves two bromodomains and could be a potential starting point for the design of a bivalent BRD4 inhibitor.

Systematic Exploration of Activity Cliffs Containing Privileged Substructures

The privileged substructure (PS) and activity cliff (AC) concepts are popular in pharmaceutical research. PSs have been empirically identified as preferred building blocks for target-class-directed generation of active compounds. Although some PSs are controversially viewed, they continue to receive much attention in drug discovery. ACs are formed by structurally similar active compounds with large potency differences and thus capture structure-activity relationship (SAR) discontinuity and reveal SAR determinants. So far, the PS and AC concepts have not been investigated in context. We have systematically explored ACs formed by compounds containing different PSs (PS-ACs). Such ACs were frequently identified in different series of compounds. PS-ACs were thoroughly characterized and compared to ACs formed by other compounds. The analysis revealed differences in AC formation between PSs. For example, individual PSs with an unusually high proportion of AC-forming compounds were identified. Furthermore, PS-AC network analysis identified clusters of ACs containing the same PS in different compound structure contexts with activity against different targets. From such PS-AC clusters, target-specific SAR information for PSs in different structural environments can be extracted.

Construction of Quantitative Structure Activity Relationship (QSAR) Models to Predict Potency of Structurally Diversed Janus Kinase 2 Inhibitors

Janus kinase 2 (JAK2) inhibitors represent a promising therapeutic class of anticancer agents against many myeloproliferative disorders. Bioactivity data on pIC50 of 2229 JAK2 inhibitors were employed in the construction of quantitative structure-activity relationship (QSAR) models. The models were built from 100 data splits using decision tree (DT), support vector machine (SVM), deep neural network (DNN) and random forest (RF). The predictive power of RF models were assessed via 10-fold cross validation, which afforded excellent predictive performance with R2 and RMSE of 0.74 ± 0.05 and 0.63 ± 0.05, respectively. Moreover, test set has excellent performance of R2 (0.75 ± 0.03) and RMSE (0.62 ± 0.04). In addition, Y-scrambling was utilized to evaluate the possibility of chance correlation of the predictive model. A thorough analysis of the substructure fingerprint count was conducted to provide insights on the inhibitory properties of JAK2 inhibitors. Molecular cluster analysis revealed that pyrazine scaffolds have nanomolar potency against JAK2.

Cheminformatics in the Service of GPCR Drug Discovery

Cheminformatics is a broad discipline covering a wide range of computational approaches, including the characterization of molecular similarity, pattern recognition, and predictive modeling. The unifying theme that these apparently disparate methods have in common is the aim of extracting useable information from the increasing amounts of data that are associated with contemporary drug discovery projects. Both proprietary and publically available data can be exploited to help inform and improve the process of developing novel therapeutic molecules targeting the GPCR family of proteins.

Privileged Substructures to Modulate Protein-Protein Interactions

Given the difficulties to identify chemical probes that can modulate protein-protein interactions (PPIs), actors in the field have started to agree on the necessity to use PPI-tailored screening chemical collections. However, which type of scaffolds may promote the binding of compounds to PPI targets remains unclear. In this big data analysis, we have identified a list of privileged chemical substructures that are most often observed within inhibitors of PPIs. Using molecular frameworks as a way to perceive chemical substructures with the combination of an experimental and a machine-learning based predicted data set of iPPI compounds, we propose a list of privileged substructures in the form of scaffolds and chemical moieties that can be substantially chemically functionalized and do not present any toxicophore nor pan-assay interference (PAINS) alerts. We think that such chemical guidance will be valuable for medicinal chemists in their attempt to identify initial quality chemical probes on PPI targets.

Opportunities and Challenges for Fatty Acid Mimetics in Drug Discovery

Fatty acids beyond their role as an endogenous energy source and storage are increasingly considered as signaling molecules regulating various physiological effects in metabolism and inflammation. Accordingly, the molecular targets involved in formation and physiological activities of fatty acids hold significant therapeutic potential. A number of these fatty acid targets are addressed by some of the oldest and most widely used drugs such as cyclooxygenase inhibiting NSAIDs, whereas others remain unexploited. Compounds orthosterically binding to proteins that endogenously bind fatty acids are considered as fatty acid mimetics. On the basis of their structural resemblance, fatty acid mimetics constitute a family of bioactive compounds showing specific binding thermodynamics and following similar pharmacokinetic mechanisms. This perspective systematically evaluates targets for fatty acid mimetics, investigates their common structural characteristics, and highlights demands in their discovery and design. In summary, fatty acid mimetics share particularly favorable characteristics justifying the conclusion that their therapeutic potential vastly outweighs the challenges in their design.

The impact of in silico screening in the discovery of novel and safer drug candidates

Drug discovery is a multidisciplinary and multivariate optimization endeavor. As such, in silico screening tools have gained considerable importance to archive, analyze and exploit the vast and ever-increasing amount of experimental data generated throughout the process. The current review will focus on the computer-aided prediction of the numerous properties that need to be controlled during the discovery of a preliminary hit and its promotion to a viable clinical candidate. It does not pretend to the almost impossible task of an exhaustive report but will highlight a few key points that need to be collectively addressed both by chemists and biologists to fuel the drug discovery pipeline with innovative and safe drug candidates.

In silico Prediction of Drug Induced Liver Toxicity Using Substructure Pattern Recognition Method

Drug-induced liver injury (DILI) is a leading cause of acute liver failure in the US and less severe liver injury worldwide. It is also one of the major reasons of drug withdrawal from the market. Thus, DILI has become one of the most important concerns of drugs, and should be predicted in very early stage of drug discovery process. In this study, a comprehensive data set containing 1317 diverse compounds was collected from publications. Then, high accuracy classification models were built using five machine learning methods based on MACCS and FP4 fingerprints after evaluating by substructure pattern recognition method. The best model was built using SVM method together with FP4 fingerprint at the IG value threshold of 0.0005. Its overall predictive accuracies were 79.7 % and 64.5 % for the training and test sets, separately, which yielded overall accuracy of 75.0 % for the external validation dataset, consisting of 88 compounds collected from a benchmark DILI database - the Liver Toxicity Knowledge Base. This model could be used for drug-induced liver toxicity prediction. Moreover, some key substructure patterns correlated with drug-induced liver toxicity were also identified as structural alerts.

Understanding the foundations of the structural similarities between marketed drugs and endogenous human metabolites

BACKGROUND

A recent comparison showed the extensive similarities between the structural properties of metabolites in the reconstructed human metabolic network ("endogenites") and those of successful, marketed drugs ("drugs").

RESULTS

Clustering indicated the related but differential population of chemical space by endogenites and drugs. Differences between the drug-endogenite similarities resulting from various encodings and judged by Tanimoto similarity could be related simply to the fraction of the bitstrings set to 1. By extracting drug/endogenite substructures, we develop a novel family of fingerprints, the Drug Endogenite Substructure (DES) encodings, based on the ranked frequency of the various substructures. These provide a natural assessment of drug-endogenite likeness, and may be used as descriptors with which to derive quantitative structure-activity relationships (QSARs).

CONCLUSIONS

"Drug-endogenite likeness" seems to have utility, and leads to a simple, novel and interpretable substructure-based molecular encoding for cheminformatics.

In silico prediction of chemical toxicity on avian species using chemical category approaches

Avian species are sensitive to pesticides and industrial chemicals, and hence used as model species in evaluation of chemical toxicity. In present study, we assessed the toxicity of more than 663 diverse chemicals on 17 avian species. All the chemicals were classified into three categories, i.e. highly toxic, slightly toxic and non-toxic, based on the toxicity classification criteria of the United States Environmental Protection Agency (EPA). To evaluate these chemicals, the toxicity prediction models were built using chemical category approaches with molecular descriptors and five commonly used fingerprints, in which five machine learning methods were performed on two standard test species: aquatic bird mallard duck and terrestrial bird northern bobwhite quail. The support vector machine (SVM) method with Pubchem fingerprint performed best as revealed by 5-fold cross-validation and the external validation set on Japanese quail. No species difference existed in our database despite several chemicals with different toxicity on some avian species. The best model had an overall accuracy at 0.851 for the prediction of toxicity on avian species, which outperformed the work of Mazzatorta et al. Furthermore, several representative substructures for characterizing avian toxicity were identified via information gain (IG) method. This study would provide a new tool for chemical safety assessment.

Many drugs contain unique scaffolds with varying structural relationships to scaffolds of currently available bioactive compounds

Molecular scaffolds were systematically extracted from approved drugs and analyzed. The majority of drug scaffolds, 552 of 700, were found to represent only a single drug. Moreover, 221 drug scaffolds were not detected in currently available bioactive compounds, i.e., the pool from which drug candidates usually originate. These "drug-unique" scaffolds displayed a variety of structural relationships to currently known bioactive scaffolds, reflecting rather different degrees of relatedness. Many drug-unique scaffolds formed only very limited structural relationships to bioactive scaffolds. These drug scaffolds should represent promising candidates for further chemical exploration and drug repositioning efforts and are made freely available.

Enhanced hit-to-lead process using bioanalogous lead evolution and chemogenomics: application in designing selective matrix metalloprotease inhibitors

The authors describe an innovative approach for designing novel inhibitors. This approach effectively integrates the emerging chemogenomics concept of target-family-based drug discovery with bioanalogous design strategies, including privileged structures, molecular frameworks as well as bioisosteric and bioanalogous/isofunctional modifications. The authors applied this method in the design of selective inhibitors of matrix metalloproteases (MMPs), also referred to as matrixins, on the basis of a unique analysis of the ligand-target knowledge base, the 'matrixinome'. For this analysis, the authors created an annotated MMP database containing ∼ 300 inhibitors with their published activity profile. The ligand space was then arranged into a lead evolution tree, where the substructural transformations in each virtual step led to marked changes in the activity pattern. This allowed subtype-specific privileged fragments to be extracted as well as modifications, which improve activity and/or selectivity. Furthermore, the compounds with the preferred activity profile were correlated with sequence homology as well as binding site similarity within the target family, thereby leading to the identification of substructural modifications that turn non-selective, biohomologous structures into selective inhibitors. The matrixinomic application of the authors' approach, therefore, provides an example of how the combination of ligand space knowledge with sequence-related data can radically improve the outcome of the lead optimisation process to achieve higher selectivity within a given target family.

Biodiversity of small molecules--a new perspective in screening set selection

How is the 'diversity' of a compound set defined and how is the most appropriate compound subset identified for assay when screening the entire HTS deck is not an option? A common approach has so far been to cover as much of the chemical space as possible by screening a chemically diverse set of compounds. We show that, rather than chemical diversity, the biologic diversity of a compound library is an essential requirement for hit identification. We describe a simple and efficient approach for the design of a HTS library based on compound-target diversity. Biodiverse compound subsets outperform chemically diverse libraries regarding hit rate and the total number of unique chemical scaffolds present among hits. Specifically, by screening ~19% of a HTS collection, we expect to discover ~50-80% of all desired bioactive compounds.

Systematic identification of scaffolds representing compounds active against individual targets and single or multiple target families

Given the enormous growth of compound activity data we currently observe, we have revisited the previously introduced concepts of privileged substructures and community-selective scaffolds and systematically searched for molecular scaffolds representing compounds active against single targets, multiple targets belonging to the same target family, or targets belonging to different families. The influence of different types of activity measurements on scaffold assignments has been determined. Furthermore, scaffold assignments have also been carried out after applying a potency threshold to exclude weakly active compounds from the comparison and address the issue of molecular selectivity. In both instances, the results were very similar indicating that single-target and single-family scaffolds display target- and family-selective tendencies, respectively. Unexpectedly large numbers of 630 unique single-target, 489 single-family, and 336 multi-family scaffolds have been identified in public domain compound data that represented relatively large numbers of compounds. Other important findings are that most of the growth in high-confidence compound activity data has been due to the evaluation of new compounds, rather than additional measurements for previously tested compounds or analog series for previously explored scaffolds. The majority of scaffolds have remained in the same category over time. Activity measurement type-dependent sets of single-target, single-family, and multi-family scaffolds are also provided as an up-to-date scaffold knowledge base.

On the origins of drug polypharmacology

The ability of many drugs, unintended most often, to interact with multiple proteins is commonly referred to as polypharmacology. Could this be a reminiscent chemical signature of early protein evolution?

Recent trends and observations in the design of high-quality screening collections

The design of a high-quality screening collection is of utmost importance for the early drug-discovery process and provides, in combination with high-quality assay systems, the foundation of future discoveries. Herein, we review recent trends and observations to successfully expand the access to bioactive chemical space, including the feedback from hit assessment interviews of high-throughput screening campaigns; recent successes with chemogenomics target family approaches, the identification of new relevant target/domain families, diversity-oriented synthesis and new emerging compound classes, and non-classical approaches, such as fragment-based screening and DNA-encoded chemical libraries. The role of in silico library design approaches are emphasized.

The concept of privileged structures in rational drug design: focus on acridine and quinoline scaffolds in neurodegenerative and protozoan diseases

INTRODUCTION

For nearly 20 years, privileged structures have offered an optimal source of core scaffolds and capping fragments for the design of combinatorial libraries directed at a broad spectrum of targets. From describing structures promiscuous within a given target family, the concept has evolved to include frameworks that can modulate proteins lacking a strict target class relation.

AREAS COVERED

Based on a literature search from 2000 to 2010, we discuss how two privileged motifs, quinolines and acridines, are particularly recurrent in compounds active against two quite different pathologies, neurodegenerative and protozoan diseases.

EXPERT OPINION

As privileged structures, quinolines and acridines could improve the productivity of drug discovery projects in the field of neurodegenerative and protozoan diseases. They could be particularly relevant for protozoan diseases because of the importance of cost-effective strategies and less stringent intellectual property concerns. Furthermore, because of their inherent affinity for various targets, privileged structures could offer a viable starting point in the search for novel multi-target ligands. Finally, from a broader perspective, they can serve as effective probes for investigating unknown but interrelated mechanisms of action.

Methods for combinatorial and parallel library design

Diversity has historically played a critical role in design of combinatorial libraries, screening sets and corporate collections for lead discovery. Large library design dominated the field in the 1990s with methods ranging anywhere from purely arbitrary through property based reagent selection to product based approaches. In recent years, however, there has been a downward trend in library size. This was due to increased information about the desirable targets gleaned from the genomics revolution and to the ever growing availability of target protein structures from crystallography and homology modeling. Creation of libraries directed toward families of receptors such as GPCRs, kinases, nuclear hormone receptors, proteases, etc., replaced the generation of libraries based primarily on diversity while single target focused library design has remained an important objective. Concurrently, computing grids and cpu clusters have facilitated the development of structure based tools that screen hundreds of thousands of molecules. Smaller "smarter" combinatorial and focused parallel libraries replaced those early un-focused large libraries in the twenty-first century drug design paradigm. While diversity still plays a role in lead discovery, the focus of current library design methods has shifted to receptor based methods, scaffold hopping/bio-isostere searching, and a much needed emphasis on synthetic feasibility. Methods such as "privileged substructures based design" and pharmacophore based design still are important methods for parallel and small combinatorial library design. This chapter discusses some of the possible design methods and presents examples where they are available.

Computational medicinal chemistry in fragment-based drug discovery: what, how and when

The use of fragment-based drug discovery (FBDD) has increased in the last decade due to the encouraging results obtained to date. In this scenario, computational approaches, together with experimental information, play an important role to guide and speed up the process. By default, FBDD is generally considered as a constructive approach. However, such additive behavior is not always present, therefore, simple fragment maturation will not always deliver the expected results. In this review, computational approaches utilized in FBDD are reported together with real case studies, where applicability domains are exemplified, in order to analyze them, and then, maximize their performance and reliability. Thus, a proper use of these computational tools can minimize misleading conclusions, keeping the credit on FBDD strategy, as well as achieve higher impact in the drug-discovery process. FBDD goes one step beyond a simple constructive approach. A broad set of computational tools: docking, R group quantitative structure–activity relationship, fragmentation tools, fragments management tools, patents analysis and fragment-hopping, for example, can be utilized in FBDD, providing a clear positive impact if they are utilized in the proper scenario – what, how and when. An initial assessment of additive/non-additive behavior is a critical point to define the most convenient approach for fragments elaboration.

A novel chemogenomics analysis of G protein-coupled receptors (GPCRs) and their ligands: a potential strategy for receptor de-orphanization

Background

G protein-coupled receptors (GPCRs) represent a family of well-characterized drug targets with significant therapeutic value. Phylogenetic classifications may help to understand the characteristics of individual GPCRs and their subtypes. Previous phylogenetic classifications were all based on the sequences of receptors, adding only minor information about the ligand binding properties of the receptors. In this work, we compare a sequence-based classification of receptors to a ligand-based classification of the same group of receptors, and evaluate the potential to use sequence relatedness as a predictor for ligand interactions thus aiding the quest for ligands of orphan receptors.

Results

We present a classification of GPCRs that is purely based on their ligands, complementing sequence-based phylogenetic classifications of these receptors. Targets were hierarchically classified into phylogenetic trees, for both sequence space and ligand (substructure) space. The overall organization of the sequence-based tree and substructure-based tree was similar; in particular, the adenosine receptors cluster together as well as most peptide receptor subtypes (e.g. opioid, somatostatin) and adrenoceptor subtypes. In ligand space, the prostanoid and cannabinoid receptors are more distant from the other targets, whereas the tachykinin receptors, the oxytocin receptor, and serotonin receptors are closer to the other targets, which is indicative for ligand promiscuity. In 93% of the receptors studied, de-orphanization of a simulated orphan receptor using the ligands of related receptors performed better than random (AUC > 0.5) and for 35% of receptors de-orphanization performance was good (AUC > 0.7).

Conclusions

We constructed a phylogenetic classification of GPCRs that is solely based on the ligands of these receptors. The similarities and differences with traditional sequence-based classifications were investigated: our ligand-based classification uncovers relationships among GPCRs that are not apparent from the sequence-based classification. This will shed light on potential cross-reactivity of GPCR ligands and will aid the design of new ligands with the desired activity profiles. In addition, we linked the ligand-based classification with a ligand-focused sequence-based classification described in literature and proved the potential of this method for de-orphanization of GPCRs.

Exploring target-selectivity patterns of molecular scaffolds

We investigate the question of whether target-selective molecular scaffolds can be identified on the basis of currently available compound activity data. Starting from a pool of 17745 public domain compounds with activity annotations for 433 human targets, we ultimately identify, through a selectivity classification and database-mining approach, 42 molecular scaffolds represented by multiple compounds that are highly selective for a particular target over one or more others. In many other cases, individual compounds representing unique scaffolds are target-selective. Hence, currently available public domain compound selectivity data are sparse. However, we also identify selectivity patterns that evolve around specific targets and are formed by multiple target-selective scaffolds. These scaffolds should provide interesting starting points for further chemical exploration.