GPCR antitarget modeling: pharmacophore models for biogenic amine binding GPCRs to avoid GPCR-mediated side effects

Computer-Aided Drug Discovery and Design: Recent Advances and Future Prospects

Computer-aided drug discovery and design involve the use of information technologies to identify and develop, on a rational ground, chemical compounds that align a set of desired physicochemical and biological properties. In its most common form, it involves the identification and/or modification of an active scaffold (or the combination of known active scaffolds), although de novo drug design from scratch is also possible. Traditionally, the drug discovery and design processes have focused on the molecular determinants of the interactions between drug candidates and their known or intended pharmacological target(s). Nevertheless, in modern times, drug discovery and design are conceived as a particularly complex multiparameter optimization task, due to the complicated, often conflicting, property requirements.This chapter provides an updated overview of in silico approaches for identifying active scaffolds and guiding the subsequent optimization process. Recent groundbreaking advances in the field have also analyzed the integration of state-of-the-art machine learning approaches in every step of the drug discovery process (from prediction of target structure to customized molecular docking scoring functions), integration of multilevel omics data, and the use of a diversity of computational approaches to assist target validation and assess plausible binding pockets.

Activity Models of Key GPCR Families in the Central Nervous System: A Tool for Many Purposes

G protein-coupled receptors (GPCRs) are targets of many drugs, of which ∼25% are indicated for central nervous system (CNS) disorders. Drug promiscuity affects their efficacy and safety profiles. Predicting the polypharmacology profile of compounds against GPCRs can thus provide a basis for producing more precise therapeutics by considering the targets and the anti-targets in that family of closely related proteins. We provide a tool for predicting the polypharmacology of compounds within prominent GPCR families in the CNS: serotonin, dopamine, histamine, muscarinic, opioid, and cannabinoid receptors. Our in-house algorithm, "iterative stochastic elimination" (ISE), produces high-quality ligand-based models for agonism and antagonism at 31 GPCRs. The ISE models correctly predict 68% of CNS drug-GPCR interactions, while the "similarity ensemble approach" predicts only 33%. The activity models correctly predict 56% of reported activities of DrugBank molecules for these CNS receptors. We conclude that the combination of interactions and activity profiles generated by screening through our models form the basis for subsequent designing and discovering novel therapeutics, either single, multitargeting, or repurposed.

Structure-based pharmacophore modeling 2. Developing a novel framework for structure-based pharmacophore model generation and selection

Pharmacophore models are three-dimensional arrangements of molecular features required for biological activity that are used in ligand identification efforts for many biological targets, including G protein-coupled receptors (GPCR). Though GPCR are integral membrane proteins of considerable interest as targets for drug development, many of these receptors lack known ligands or experimentally determined structures necessary for ligand- or structure-based pharmacophore model generation, respectively. Thus, we here present a structure-based pharmacophore modeling approach that uses fragments placed with Multiple Copy Simultaneous Search (MCSS) to generate high-performing pharmacophore models in the context of experimentally determined, as well as modeled GPCR structures. Moreover, we have addressed the oft-neglected topic of pharmacophore model selection via development of a cluster-then-predict machine learning workflow. Herein score-based pharmacophore models were generated in experimentally determined and modeled structures of 13 class A GPCR and resulted in pharmacophore models exhibiting high enrichment factors when used to search a database containing 569 class A GPCR ligands. In addition, classification of pharmacophore models with the best performing cluster-then-predict logistic regression classifier resulted in positive predictive values (PPV) of 0.88 and 0.76 for selecting high enrichment pharmacophore models from among those generated in experimentally determined and modeled structures, respectively.

Discovery of 4-((2S,4S)-4-Ethoxy-1-((5-methoxy-7-methyl-1H-indol-4-yl)methyl)piperidin-2-yl)benzoic Acid (LNP023), a Factor B Inhibitor Specifically Designed To Be Applicable to Treating a Diverse Array of Complement Mediated Diseases

The alternative pathway (AP) of the complement system is a key contributor to the pathogenesis of several human diseases including age-related macular degeneration, paroxysmal nocturnal hemoglobinuria (PNH), atypical hemolytic uremic syndrome (aHUS), and various glomerular diseases. The serine protease factor B (FB) is a key node in the AP and is integral to the formation of C3 and C5 convertase. Despite the prominent role of FB in the AP, selective orally bioavailable inhibitors, beyond our own efforts, have not been reported previously. Herein we describe in more detail our efforts to identify FB inhibitors by high-throughput screening (HTS) and leveraging insights from several X-ray cocrystal structures during optimization efforts. This work culminated in the discovery of LNP023 (41), which is currently being evaluated clinically in several diverse AP mediated indications.

The current limits in virtual screening and property prediction

Beyond finding inhibitors that show high binding affinity to the respective target, there is the challenge of optimizing their properties with respect to metabolic and toxicological issues, as well as further off-target effects. To reduce the experimental effort of synthesizing and testing actual substances in corresponding assays, virtual screening has become an indispensable toolbox in preclinical development. The scope of application covers the prediction of molecular properties including solubility, metabolic liability and binding to antitargets, such as the hERG channel. Furthermore, prediction of binding sites and drugable targets are emerging aspects of virtual screening. Issues involved with the currently applied computational models including machine learning algorithms are outlined, such as limitations to the accuracy of prediction and overfitting.

Computer-Aided Drug Design: An Overview

The term drug design describes the search of novel compounds with biological activity, on a systematic basis. In its most common form, it involves modification of a known active scaffold or linking known active scaffolds, although de novo drug design (i.e., from scratch) is also possible. Though highly interrelated, identification of active scaffolds should be conceptually separated from drug design. Traditionally, the drug design process has focused on the molecular determinants of the interactions between the drug and its known or intended molecular target. Nevertheless, current drug design also takes into consideration other relevant processes than influence drug efficacy and safety (e.g., bioavailability, metabolic stability, interaction with antitargets).This chapter provides an overview on possible approaches to identify active scaffolds (including in silico approximations to approach that task) and computational methods to guide the subsequent optimization process. It also discusses in which situations each of the overviewed techniques is more appropriate.

Identification of the Beer Component Hordenine as Food-Derived Dopamine D2 Receptor Agonist by Virtual Screening a 3D Compound Database

The dopamine D2 receptor (D2R) is involved in food reward and compulsive food intake. The present study developed a virtual screening (VS) method to identify food components, which may modulate D2R signalling. In contrast to their common applications in drug discovery, VS methods are rarely applied for the discovery of bioactive food compounds. Here, databases were created that exclusively contain substances occurring in food and natural sources (about 13,000 different compounds in total) as the basis for combined pharmacophore searching, hit-list clustering and molecular docking into D2R homology models. From 17 compounds finally tested in radioligand assays to determine their binding affinities, seven were classified as hits (hit rate = 41%). Functional properties of the five most active compounds were further examined in β-arrestin recruitment and cAMP inhibition experiments. D2R-promoted G-protein activation was observed for hordenine, a constituent of barley and beer, with approximately identical ligand efficacy as dopamine (76%) and a Ki value of 13 μM. Moreover, hordenine antagonised D2-mediated β-arrestin recruitment indicating functional selectivity. Application of our databases provides new perspectives for the discovery of bioactive food constituents using VS methods. Based on its presence in beer, we suggest that hordenine significantly contributes to mood-elevating effects of beer.

5-HT2 receptor affinity, docking studies and pharmacological evaluation of a series of 1,3-disubstituted thiourea derivatives

A series of 10 thiourea derivatives have been synthesized by the reaction of aromatic amine with a substituted aryl (compounds 1-3, 6-8) and alkylphenyl (4, 5, 9, 10) isothiocyanates. Their in vitro and in vivo pharmacological properties were studied. Among the evaluated compounds, two displayed very high affinity for the 5-HT2A receptor (1-0.043 nM and 5-0.6 nM), being selective over the 5-HT2C receptor. Derivatives 3, 5, 9, 10 by 70-89% diminished L-5-HTP-induced head twitch episodes. Compounds 1 and 5 as the 5-HT2A receptor antagonists produced a dose-dependent decrease in the number of DOI-elicited HTR. Compounds 1-5 strongly reduced amphetamine-evoked hyperactivity in rodents. In another test, 1 and 2 caused hyperthermia in mice, whereas 9 and 10 led to hypothermia. Antinociceptive and anticonvulsant properties of selected derivatives were demonstrated. Molecular docking studies using a homology model of 5-HT2A revealed a significant role of hydrogen bonds between both thiourea NH groups and Asp155/Tyr370 residues, as well as π-π interaction with Phe339.

The In Vitro Pharmacological Profile of Drugs as a Proxy Indicator of Potential In Vivo Organ Toxicities

The potential of a drug to cause certain organ toxicities is somehow implicitly contained in its full pharmacological profile, provided the drug reaches and accumulates at the various organs where the different interacting proteins in its profile, both targets and off-targets, are expressed. Under this assumption, a computational approach was implemented to obtain a projected anatomical profile of a drug from its in vitro pharmacological profile linked to protein expression data across 47 organs. It was observed that the anatomical profiles obtained when using only the known primary targets of the drugs reflected roughly the intended organ targets. However, when both known and predicted secondary pharmacology was considered, the projected anatomical profiles of the drugs were able to clearly highlight potential organ off-targets. Accordingly, when applied to sets of drugs known to cause cardiotoxicity and hepatotoxicity, the approach is able to identify heart and liver, respectively, as the organs where the proteins in the pharmacological profile of the corresponding drugs are specifically expressed. When applied to a set of drugs linked to a risk of Torsades de Pointes, heart is again the organ clearly standing out from the rest and a potential protein profile hazard is proposed. The approach can be used as a proxy indicator of potential in vivo organ toxicities.

Phenyl Ether- and Aniline-Containing 2-Aminoquinolines as Potent and Selective Inhibitors of Neuronal Nitric Oxide Synthase

Excess nitric oxide (NO) produced by neuronal nitric oxide synthase (nNOS) is implicated in neurodegenerative disorders. As a result, inhibition of nNOS and reduction of NO levels is desirable therapeutically, but many nNOS inhibitors are poorly bioavailable. Promising members of our previously reported 2-aminoquinoline class of nNOS inhibitors, although orally bioavailable and brain-penetrant, suffer from unfavorable off-target binding to other CNS receptors, and they resemble known promiscuous binders. Rearranged phenyl ether- and aniline-linked 2-aminoquinoline derivatives were therefore designed to (a) disrupt the promiscuous binding pharmacophore and diminish off-target interactions and (b) preserve potency, isoform selectivity, and cell permeability. A series of these compounds was synthesized and tested against purified nNOS, endothelial NOS (eNOS), and inducible NOS (iNOS) enzymes. One compound, 20, displayed high potency, selectivity, and good human nNOS inhibition, and retained some permeability in a Caco-2 assay. Most promisingly, CNS receptor counterscreening revealed that this rearranged scaffold significantly reduces off-target binding.

Identification of structural features in chemicals associated with cancer drug response: a systematic data-driven analysis

Motivation: Analysis of relationships of drug structure to biological response is key to understanding off-target and unexpected drug effects, and for developing hypotheses on how to tailor drug therapies. New methods are required for integrated analyses of a large number of chemical features of drugs against the corresponding genome-wide responses of multiple cell models.

Results: In this article, we present the first comprehensive multi-set analysis on how the chemical structure of drugs impacts on genome-wide gene expression across several cancer cell lines [Connectivity Map (CMap) database]. The task is formulated as searching for drug response components across multiple cancers to reveal shared effects of drugs and the chemical features that may be responsible. The components can be computed with an extension of a recent approach called Group Factor Analysis. We identify 11 components that link the structural descriptors of drugs with specific gene expression responses observed in the three cell lines and identify structural groups that may be responsible for the responses. Our method quantitatively outperforms the limited earlier methods on CMap and identifies both the previously reported associations and several interesting novel findings, by taking into account multiple cell lines and advanced 3D structural descriptors. The novel observations include: previously unknown similarities in the effects induced by 15-delta prostaglandin J2 and HSP90 inhibitors, which are linked to the 3D descriptors of the drugs; and the induction by simvastatin of leukemia-specific response, resembling the effects of corticosteroids.

Availability and implementation: Source Code implementing the method is available at: http://research.ics.aalto.fi/mi/software/GFAsparse

Contact:suleiman.khan@aalto.fi or samuel.kaski@aalto.fi

Supplementary Information:Supplementary data are available at Bioinformatics online.

Exploring the ligand-protein networks in traditional chinese medicine: current databases, methods and applications

While the concept of "single component-single target" in drug discovery seems to have come to an end, "Multi-component-multi-target" is considered to be another promising way out in this field. The Traditional Chinese Medicine (TCM), which has thousands of years' clinical application among China and other Asian countries, is the pioneer of the "Multi-component-multi-target" and network pharmacology. Hundreds of different components in a TCM prescription can cure the diseases or relieve the patients by modulating the network of potential therapeutic targets. Although there is no doubt of the efficacy, it is difficult to elucidate convincing underlying mechanism of TCM due to its complex composition and unclear pharmacology. Without thorough investigation of its potential targets and side effects, TCM is not able to generate large-scale medicinal benefits, especially in the days when scientific reductionism and quantification are dominant. The use of ligand-protein networks has been gaining significant value in the history of drug discovery while its application in TCM is still in its early stage. This article firstly surveys TCM databases for virtual screening that have been greatly expanded in size and data diversity in recent years. On that basis, different screening methods and strategies for identifying active ingredients and targets of TCM are outlined based on the amount of network information available, both on sides of ligand bioactivity and the protein structures. Furthermore, applications of successful in silico target identification attempts are discussed in details along with experiments in exploring the ligand-protein networks of TCM. Finally, it will be concluded that the prospective application of ligand-protein networks can be used not only to predict protein targets of a small molecule, but also to explore the mode of action of TCM.

Predictive in silico off-target profiling in drug discovery

Drug action can be rationalized as interaction of a molecule with proteins in a regulatory network of targets from a specific biological system. Both drug and side effects are often governed by interaction of the drug molecule with many, often unrelated, targets. Accordingly, arrays of protein–ligand interaction data from numerous in vitro profiling assays today provide growing evidence of polypharmacological drug interactions, even for marketed drugs. In vitro off-target profiling has therefore become an important tool in early drug discovery to learn about potential off-target liabilities, which are sometimes beneficial, but more often safety relevant. The rapidly developing field of in silico profiling approaches is complementing in vitro profiling. These approaches capitalize from large amounts of biochemical data from multiple sources to be exploited for optimizing undesirable side effects in pharmaceutical research. Therefore, current in silico profiling models are nowadays perceived as valuable tools in drug discovery, and promise a platform to support optimally informed decisions.

Synthesis and structure evaluation of new complex butylarylpiperazin-1-yl derivatives

A series of arylpiperazine derivatives of 1,16-diphenyl-19-azahexacyclo-[14.5.1.0^2,15.0^3,8.0^9,14.0^17,21]docosa-2,3,5,7,8,9,11,13,14-nonaene-18,20,22-trione and 4,10-diphenyl-1H,2H,3H,5H-indeno[1,2-f]isoindole-1,3,5-trione was synthesized. The pharmacological profile of compound 4 at the 5-HT_1A receptor was measured by binding assay. The title compounds were tested in cell-based assay against the human immunodeficiency virus type-1. The X-ray crystallographic studies of derivatives 2, 6, 7, 11, 19, and 20 were presented.

A systematic study of chemogenomics of carbohydrates

We explored the potential of carbohydrates in drug discovery by using a network-based multi-target computational approach.

Exploring the ligand-protein networks in traditional chinese medicine: current databases, methods, and applications

The traditional Chinese medicine (TCM), which has thousands of years of clinical application among China and other Asian countries, is the pioneer of the “multicomponent-multitarget” and network pharmacology. Although there is no doubt of the efficacy, it is difficult to elucidate convincing underlying mechanism of TCM due to its complex composition and unclear pharmacology. The use of ligand-protein networks has been gaining significant value in the history of drug discovery while its application in TCM is still in its early stage. This paper firstly surveys TCM databases for virtual screening that have been greatly expanded in size and data diversity in recent years. On that basis, different screening methods and strategies for identifying active ingredients and targets of TCM are outlined based on the amount of network information available, both on sides of ligand bioactivity and the protein structures. Furthermore, applications of successful in silico target identification attempts are discussed in detail along with experiments in exploring the ligand-protein networks of TCM. Finally, it will be concluded that the prospective application of ligand-protein networks can be used not only to predict protein targets of a small molecule, but also to explore the mode of action of TCM.

Pharmacophore modelling: applications in drug discovery

This review highlights the concept of using pharmacophore models in modern drug research and reviews some important examples as well as success stories. This includes papers from both method-development and application areas. As indicated by the number of publications available, the pharmacophore approach has proven to be extremely useful not only in virtual screening and library design for efficient hit discovery, but also in the optimisation of lead compounds to clinical candidates. Recent studies focus on the use of parallel screening using pharmacophore models for bioactivity profiling and early stage risk assessment of potential side effects and toxicity, due to the interaction of drug candidates with antitargets.

Experimental validation of in silico target predictions on synergistic protein targets

Two relatively recent trends have become apparent in current early stage drug discovery settings: firstly, a revival of phenotypic screening strategies and secondly, the increasing acceptance that some drugs work by modulating multiple targets in parallel (‘multi-target drugs’).

Quantitative prediction of antitarget interaction profiles for chemical compounds

The evaluation of possible interactions between chemical compounds and antitarget proteins is an important task of the research and development process. Here, we describe the development and validation of QSAR models for the prediction of antitarget end-points, created on the basis of multilevel and quantitative neighborhoods of atom descriptors and self-consistent regression. Data on 4000 chemical compounds interacting with 18 antitarget proteins (13 receptors, 2 enzymes, and 3 transporters) were used to model 32 sets of end-points (IC(50), K(i), and K(act)). Each set was randomly divided into training and test sets in a ratio of 80% to 20%, respectively. The test sets were used for external validation of QSAR models created on the basis of the training sets. The coverage of prediction for all test sets exceeded 95%, and for half of the test sets, it was 100%. The accuracy of prediction for 29 of the end-points, based on the external test sets, was typically in the range of R(2)(test) = 0.6-0.9; three tests sets had lower R(2)(test) values, specifically 0.55-0.6. The proposed approach showed a reasonable accuracy of prediction for 91% of the antitarget end-points and high coverage for all external test sets. On the basis of the created models, we have developed a freely available online service for in silico prediction of 32 antitarget end-points: http://www.pharmaexpert.ru/GUSAR/antitargets.html.

Novel computational approaches to polypharmacology as a means to define responses to individual drugs

Polypharmacology, which focuses on designing therapeutics to target multiple receptors, has emerged as a new paradigm in drug discovery. Polypharmacological effects are an attribute of most, if not all, drug molecules. The efficacy and toxicity of drugs, whether designed as single- or multitarget therapeutics, result from complex interactions between pharmacodynamic, pharmacokinetic, genetic, epigenetic, and environmental factors. Ultimately, to predict a drug response phenotype, it is necessary to understand the change in information flow through cellular networks resulting from dynamic drug-target interactions and the impact that this has on the complete biological system. Although such is a future objective, we review recent progress and challenges in computational techniques that enable the prediction and analysis of in vitro and in vivo drug-response phenotypes.

From in silico target prediction to multi-target drug design: current databases, methods and applications

Given the tremendous growth of bioactivity databases, the use of computational tools to predict protein targets of small molecules has been gaining importance in recent years. Applications span a wide range, from the 'designed polypharmacology' of compounds to mode-of-action analysis. In this review, we firstly survey databases that can be used for ligand-based target prediction and which have grown tremendously in size in the past. We furthermore outline methods for target prediction that exist, both based on the knowledge of bioactivities from the ligand side and methods that can be applied in situations when a protein structure is known. Applications of successful in silico target identification attempts are discussed in detail, which were based partly or in whole on computational target predictions in the first instance. This includes the authors' own experience using target prediction tools, in this case considering phenotypic antibacterial screens and the analysis of high-throughput screening data. Finally, we will conclude with the prospective application of databases to not only predict, retrospectively, the protein targets of a small molecule, but also how to design ligands with desired polypharmacology in a prospective manner.

A novel structural framework for α(1A/D)-adrenoceptor selective antagonists identified using subtype selective pharmacophores

In this study four and five-feature pharmacophores for selective antagonists at each of the three α(1)-adrenoceptor (AR) subtypes were used to identify novel α(1)-AR subtype selective compounds in the National Cancer Institute and Tripos LeadQuest databases. 12 compounds were selected, based on diversity of structure, predicted high affinity and selectivity at the α(1D)- subtype compared to α(1A)- and α(1B)-ARs. 9 out of 12 of the tested compounds displayed affinity at the α(1A) and α(1D) -AR subtypes and 6 displayed affinity at all three α(1)-AR subtypes, no α(1B)-AR selective compounds were identified. 8 of the 9 compounds with α(1)-AR affinity were antagonists and one compound displayed partial agonist characteristics. This virtual screening has successfully identified an α(1A/D)-AR selective antagonist, with low µM affinity with a novel structural scaffold of a an isoquinoline fused three-ring system and good lead-like qualities ideal for further drug development.

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.

Binding of protein kinase inhibitors to synapsin I inferred from pair-wise binding site similarity measurements

Predicting off-targets by computational methods is getting increasing importance in early drug discovery stages. We herewith present a computational method based on binding site three-dimensional comparisons, which prompted us to investigate the cross-reaction of protein kinase inhibitors with synapsin I, an ATP-binding protein regulating neurotransmitter release in the synapse. Systematic pair-wise comparison of the staurosporine-binding site of the proto-oncogene Pim-1 kinase with 6,412 druggable protein-ligand binding sites suggested that the ATP-binding site of synapsin I may recognize the pan-kinase inhibitor staurosporine. Biochemical validation of this hypothesis was realized by competition experiments of staurosporine with ATP-gamma(35)S for binding to synapsin I. Staurosporine, as well as three other inhibitors of protein kinases (cdk2, Pim-1 and casein kinase type 2), effectively bound to synapsin I with nanomolar affinities and promoted synapsin-induced F-actin bundling. The selective Pim-1 kinase inhibitor quercetagetin was shown to be the most potent synapsin I binder (IC50 = 0.15 microM), in agreement with the predicted binding site similarities between synapsin I and various protein kinases. Other protein kinase inhibitors (protein kinase A and chk1 inhibitor), kinase inhibitors (diacylglycerolkinase inhibitor) and various other ATP-competitors (DNA topoisomerase II and HSP-90alpha inhibitors) did not bind to synapsin I, as predicted from a lower similarity of their respective ATP-binding sites to that of synapsin I. The present data suggest that the observed downregulation of neurotransmitter release by some but not all protein kinase inhibitors may also be contributed by a direct binding to synapsin I and phosphorylation-independent perturbation of synapsin I function. More generally, the data also demonstrate that cross-reactivity with various targets may be detected by systematic pair-wise similarity measurement of ligand-annotated binding sites.

Ligand-based pharmacophore model of N-Aryl and N-Heteroaryl piperazine alpha 1A-adrenoceptors antagonists using GALAHAD

Computer aided drug discovery for selective antagonism effects on alpha(1A) subtypes of G-protein coupled receptors are important in the treatment of benign prostatic hyperplasia (BPH). Ligand-based pharmacophore models of N-Aryl and N-Heteroaryl piperazine alpha(1A)-antagonists were developed using two separate training sets. Pharmacophore models were generated using the flexible align method within the GALAHAD module, implemented in SYBYL8.1 software. The most significant pharmacophore hypothesis, characterized by the conflicting demands of maximizing pharmacophore consensus, maximizing steric consensus, and minimizing energy, consisted of one positive nitrogen center, one donor atom center, two acceptor atom centers, and two hydrophobic groups. The most active compound in each class training set showed a good fit with all features of the pharmacophore proposed. The resulting models also had something in common with the hypothesis using the Catalyst software reported in other publications. These alpha(1A) pharmacophore models could predict compounds well, both in the training set and the test set. The pharmacophore models were also validated by an external dataset using a portion of the ZINC database. A 3D-QSAR model using the pharmacophore model to align the compounds was established in this study. The CoMFA model with the cross-validated q(2) value of 0.735 revealed that the model was valid. Our research provides a valuable tool for designing new therapeutic compounds with desired biological activity.

Novel approach for efficient pharmacophore-based virtual screening: method and applications

Virtual screening is emerging as a productive and cost-effective technology in rational drug design for the identification of novel lead compounds. An important model for virtual screening is the pharmacophore. Pharmacophore is the spatial configuration of essential features that enable a ligand molecule to interact with a specific target receptor. In the absence of a known receptor structure, a pharmacophore can be identified from a set of ligands that have been observed to interact with the target receptor. Here, we present a novel computational method for pharmacophore detection and virtual screening. The pharmacophore detection module is able to (i) align multiple flexible ligands in a deterministic manner without exhaustive enumeration of the conformational space, (ii) detect subsets of input ligands that may bind to different binding sites or have different binding modes, (iii) address cases where the input ligands have different affinities by defining weighted pharmacophores based on the number of ligands that share them, and (iv) automatically select the most appropriate pharmacophore candidates for virtual screening. The algorithm is highly efficient, allowing a fast exploration of the chemical space by virtual screening of huge compound databases. The performance of PharmaGist was successfully evaluated on a commonly used data set of G-Protein Coupled Receptor alpha1A. Additionally, a large-scale evaluation using the DUD (directory of useful decoys) data set was performed. DUD contains 2950 active ligands for 40 different receptors, with 36 decoy compounds for each active ligand. PharmaGist enrichment rates are comparable with other state-of-the-art tools for virtual screening.

Definition of the G protein-coupled receptor transmembrane bundle binding pocket and calculation of receptor similarities for drug design

Recent advances in structural biology for G-protein-coupled receptors (GPCRs) have provided new opportunities to improve the definition of the transmembrane binding pocket. Here a reference set of 44 residue positions accessible for ligand binding was defined through detailed analysis of all currently available crystal structures. This was used to characterize pharmacological relationships of Family A/Rhodopsin family GPCRs, minimizing evolutionary influence from parts of the receptor that do not generally affect ligand binding. The resultant dendogram tended to group receptors according to endogenous ligand types, although it revealed subdivision of certain classes, notably peptide and lipid receptors. The transmembrane binding site reference set, particularly when coupled with a means of identifying the subset of ligand binding residues, provides a general paradigm for understanding the pharmacology/selectivity profile of ligands at Family A GPCRs. This has wide applicability to GPCR drug design problems across many disease areas.

Self-organizing molecular fingerprints: a ligand-based view on drug-like chemical space and off-target prediction

Background: Reliable prediction of multiple ligand–receptor interactions for a given bioactive compound helps recognize and understand off-target effects, and enables drug re-purposing and scaffold-hopping in lead discovery. We developed a ligand-based computational method for drug-target prediction that is independent from protein structural analysis. Method: The idea is to infer drug targets from the pharmacophoric feature similarity of known ligands, and define functional target similarity from a ligand perspective, which also provides access to targets with unknown structures. First, known ligands were represented by topological pharmacophoric features. Then, the self-organizing map technique was used to generate fingerprint patterns for similarity analysis, where each resulting fingerprint represents a drug target. Target fingerprints were clustered and analyzed for correlations. Well-structured dendrograms were obtained presenting interpretable and meaningful relationships between drug targets. Conclusion: Self-organization of fingerprints reduces noise from molecular pharmacophore descriptors, captures their essential features, and reveals potential cross-activities of ligand classes and off-target effects of bioactive compounds.

Analysis of pharmacology data and the prediction of adverse drug reactions and off-target effects from chemical structure

Preclinical Safety Pharmacology (PSP) attempts to anticipate adverse drug reactions (ADRs) during early phases of drug discovery by testing compounds in simple, in vitro binding assays (that is, preclinical profiling). The selection of PSP targets is based largely on circumstantial evidence of their contribution to known clinical ADRs, inferred from findings in clinical trials, animal experiments, and molecular studies going back more than forty years. In this work we explore PSP chemical space and its relevance for the prediction of adverse drug reactions. Firstly, in silico (computational) Bayesian models for 70 PSP-related targets were built, which are able to detect 93% of the ligands binding at IC(50) < or = 10 microM at an overall correct classification rate of about 94%. Secondly, employing the World Drug Index (WDI), a model for adverse drug reactions was built directly based on normalized side-effect annotations in the WDI, which does not require any underlying functional knowledge. This is, to our knowledge, the first attempt to predict adverse drug reactions across hundreds of categories from chemical structure alone. On average 90% of the adverse drug reactions observed with known, clinically used compounds were detected, an overall correct classification rate of 92%. Drugs withdrawn from the market (Rapacuronium, Suprofen) were tested in the model and their predicted ADRs align well with known ADRs. The analysis was repeated for acetylsalicylic acid and Benperidol which are still on the market. Importantly, features of the models are interpretable and back-projectable to chemical structure, raising the possibility of rationally engineering out adverse effects. By combining PSP and ADR models new hypotheses linking targets and adverse effects can be proposed and examples for the opioid mu and the muscarinic M2 receptors, as well as for cyclooxygenase-1 are presented. It is hoped that the generation of predictive models for adverse drug reactions is able to help support early SAR to accelerate drug discovery and decrease late stage attrition in drug discovery projects. In addition, models such as the ones presented here can be used for compound profiling in all development stages.

Toward deciphering the code to aminergic G protein-coupled receptor drug design

The trace amine-associated receptor 1 (TAAR(1)) is a biogenic amine G protein-coupled receptor (GPCR) that is potently activated by 3-iodothyronamine (1, T(1)AM) in vitro. Compound 1 is an endogenous derivative of the thyroid hormone thyroxine which rapidly induces hypothermia, anergia, and bradycardia when administered to mice. To explore the role of TAAR(1) in mediating the effects of 1, we rationally designed and synthesized rat TAAR(1) superagonists and lead antagonists using the rotamer toggle switch model of aminergic GPCR activation. The functional activity of a ligand is proposed to be correlated to its probable interactions with the rotamer switch residues; agonists allow the rotamer switch residues to toggle to their active conformation, whereas antagonists interfere with this conformational transition. These agonist and antagonist design principles provide a conceptual model for understanding the relationship between the molecular structure of a drug and its pharmacological properties.

Concept of combinatorial de novo design of drug-like molecules by particle swarm optimization

We present a fast stochastic optimization algorithm for fragment-based molecular de novo design (COLIBREE, Combinatorial Library Breeding). The search strategy is based on a discrete version of particle swarm optimization. Molecules are represented by a scaffold, which remains constant during optimization, and variable linkers and side chains. Different linkers represent virtual chemical reactions. Side-chain building blocks were obtained from pseudo-retrosynthetic dissection of large compound databases. Here, ligand-based design was performed using chemically advanced template search (CATS) topological pharmacophore similarity to reference ligands as fitness function. A weighting scheme was included for particle swarm optimization-based molecular design, which permits the use of many reference ligands and allows for positive and negative design to be performed simultaneously. In a case study, the approach was applied to the de novo design of potential peroxisome proliferator-activated receptor subtype-selective agonists. The results demonstrate the ability of the technique to cope with large combinatorial chemistry spaces and its applicability to focused library design. The technique was able to perform exploitation of a known scheme and at the same time explorative search for novel ligands within the framework of a given molecular core structure. It thereby represents a practical solution for compound screening in the early hit and lead finding phase of a drug discovery project.

Computational chemistry approaches to drug discovery in signal transduction

The advent of therapeutic strategies aimed at targeting specific macromolecular components of deregulated signaling pathways associated with particular disease states has given rise to the idea that it should be possible to design ligands as drug candidates to these targets from first principles. This concept has been beckoning for a long time but structure-based ligand design only became feasible once it was possible to determine the 3-D structures of molecular targets at atomic resolution. However, structure-based design turned out to be difficult, chiefly because under physiological conditions both receptors and ligands are not static but they behave dynamically. While it is possible to design ligands with high steric and electronic complementarity to a receptor site, it is always uncertain how biologically relevant the assumed conformations of both ligand and receptor actually are. The fact that it remains beyond our current abilities to predict with sufficient accuracy the affinity between hypothetical ligand and receptor poses is in part connected with this problem and continues to confound the reliable prediction of drug-like ligands for therapeutic targets. Nevertheless, significant progress has been made and so-called virtual screening methods that use computational methods to dock candidate ligands into receptor sites and to score the resulting complexes are now used routinely as one of the components in drug discovery screening campaigns. Here an overview is given of the underlying principles, implementations, and applications of structure-guided computational design technologies. Although the emphasis is on receptor-based strategies, mention will also be made of some of the more established ligand-based approaches, such as similarity analyses and quantitative structure-activity relationship methods.

Exploring structure-selectivity relationships of biogenic amine GPCR antagonists using similarity searching and dynamic compound mapping

We design and analyze compound selectivity sets of antagonists with differential selectivity against seven biogenic amine G-protein coupled receptors. The selectivity sets consist of a total of 267 antagonists and contain a spectrum of in part closely related molecular scaffolds. Each set represents a different selectivity profile. Using these com- pound sets, a systematic computational analysis of structure-selectivity relationships is carried out with different 2D similarity methods including fingerprints, recursive partitioning, clustering, and dynamic compound mapping. Screening calculations are performed in a background database containing nearly four million molecules. Fingerprint searching and compound mapping are found to enrich target-selective antagonists over family-selective ones. Dynamic compound mapping effectively discriminates database compounds from GPCR antagonists and consistently retains target-selective antagonists during the final dimension extension levels. Furthermore, the widely used MACCS key fingerprint displays a strong tendency to distinguish between target- and family-selective GPCR antagonists. Taken together, the results indicate that different types of 2D similarity methods are capable of distinguishing closely related molecules having different selectivity. The reported compound benchmark system is made freely available in order to enable selectivity-oriented analyses using other computational approaches.

In silico elucidation of the molecular mechanism defining the adverse effect of selective estrogen receptor modulators

Early identification of adverse effect of preclinical and commercial drugs is crucial in developing highly efficient therapeutics, since unexpected adverse drug effects account for one-third of all drug failures in drug development. To correlate protein–drug interactions at the molecule level with their clinical outcomes at the organism level, we have developed an integrated approach to studying protein–ligand interactions on a structural proteome-wide scale by combining protein functional site similarity search, small molecule screening, and protein–ligand binding affinity profile analysis. By applying this methodology, we have elucidated a possible molecular mechanism for the previously observed, but molecularly uncharacterized, side effect of selective estrogen receptor modulators (SERMs). The side effect involves the inhibition of the Sacroplasmic Reticulum Ca2+ ion channel ATPase protein (SERCA) transmembrane domain. The prediction provides molecular insight into reducing the adverse effect of SERMs and is supported by clinical and in vitro observations. The strategy used in this case study is being applied to discover off-targets for other commercially available pharmaceuticals. The process can be included in a drug discovery pipeline in an effort to optimize drug leads and reduce unwanted side effects.

Early identification of the side effects of preclinical and commercial drugs is crucial in developing highly efficient therapeutics, as unexpected side effects account for one-third of all drug failures in drug development and lead to drugs being withdrawn from the market. Compared with the experimental identification of off-target proteins that cause side effects, computational approaches not only save time and costs by providing a candidate list of potential off-targets, but also provide insight into understanding the molecular mechanisms of protein–drug interactions. In this paper we describe an integrated approach to identifying similar drug binding pockets across protein families that have different global shapes. In a case study, we elucidate a possible molecular mechanism for the observed side effects of selective estrogen receptor modulators (SERMs), which are widely used to treat and prevent breast cancer and other diseases. The prediction provides molecular insight into reducing the side effects of SERMs and is supported by clinical and biochemical observations. The strategy used in this case study is being applied to discover off-targets for other commercially available pharmaceuticals and to repurpose existing safe pharmaceuticals to treat different diseases. The process can be included in a drug discovery pipeline in an effort to optimize drug leads, reduce unwanted side effects, and accelerate development of new drugs.