Is nontarget screening of emerging contaminants by LC-HRMS successful? A plea for compound libraries and computer tools

This review focuses on the possibilities and limits of nontarget screening of emerging contaminants, with emphasis on recent applications and developments in data evaluation and compound identification by liquid chromatography-high-resolution mass spectrometry (HRMS). The general workflow includes determination of the elemental composition from accurate mass, a further search for the molecular formula in compound libraries or general chemical databases, and a ranking of the proposed structures using further information, e.g., from mass spectrometry (MS) fragmentation and retention times. The success of nontarget screening is in some way limited to the preselection of relevant compounds from a large data set. Recently developed approaches show that statistical analysis in combination with suspect and nontarget screening are useful methods to preselect relevant compounds. Currently, the unequivocal identification of unknowns still requires information from an authentic standard which has to be measured or is already available in user-defined MS/MS reference databases or libraries containing HRMS spectral information and retention times. In this context, we discuss the advantages and future needs of publicly available MS and MS/MS reference databases and libraries which have mostly been created for the metabolomic field. A big step forward has been achieved with computer-based tools when no MS library or MS database entry is found for a compound. The numerous search results from a large chemical database can be condensed to only a few by in silico fragmentation. This has been demonstrated for selected compounds and metabolites in recent publications. Still, only very few compounds have been identified or tentatively identified in environmental samples by nontarget screening. The availability of comprehensive MS libraries with a focus on environmental contaminants would tremendously improve the situation.     

Prediction of ion channel activity using binary kernel discrimination

Voltage-gated ion channels are a diverse family of pharmaceutically important membrane proteins for which limited 3D information is available. A number of virtual screening tools have been used to assist with the discovery of new leads and with the analysis of screening results. One such tool, and the subject of this paper, is binary kernel discrimination (BKD), a machine-learning approach that has recently been applied to applications in chemoinformatics. It uses a training set of compounds, for which both structural and qualitative activity data are known, to produce a model that can then be used to rank another set of compounds in order of likely activity. Here, we report the use of BKD to build models for the prediction of five different ion channel targets using two types of activity data. The results obtained suggest that the approach provides an effective way of prioritizing compounds for acquisition and testing.     

Collaborative filtering on a family of biological targets

Building a QSAR model of a new biological target for which few screening data are available is a statistical challenge. However, the new target may be part of a bigger family, for which we have more screening data. Collaborative filtering or, more generally, multi-task learning, is a machine learning approach that improves the generalization performance of an algorithm by using information from related tasks as an inductive bias. We use collaborative filtering techniques for building predictive models that link multiple targets to multiple examples. The more commonalities between the targets, the better the multi-target model that can be built. We show an example of a multi-target neural network that can use family information to produce a predictive model of an undersampled target. We evaluate JRank, a kernel-based method designed for collaborative filtering. We show their performance on compound prioritization for an HTS campaign and the underlying shared representation between targets. JRank outperformed the neural network both in the single- and multi-target models.     

Synthesis and high-throughput screening of N-acetyl-beta-hexosaminidase inhibitor libraries targeting osteoarthritis

C1 Nitrogen iminocyclitols are potent inhibitors of N-acetyl-beta-hexosaminidases. Given hexosaminidases' important roles in osteoarthritis, we developed two straightforward and efficient syntheses of C1 nitrogen iminocyclitols from two readily available starting materials, D-mannosamine hydrochloride and the microbial oxidation product of fructose. A diversity-oriented synthetic strategy was then performed by coupling these core structures with various aldehydes, carboxylic acids, and alkynes to generate three separate libraries. High-throughput screening of the generated libraries with human N-acetyl-beta-hexosaminidases produced only moderate inhibitory activities. However, the synthetic approach and screening strategy for these compounds will be applied to develop new potent inhibitors of human N-acetyl-beta-hexosaminidases, particularly when combined with the structural information of these enzymes.     

Diversity-oriented synthesis: producing chemical tools for dissecting biology

Small molecule modulators of biological function can be discovered by the screening of compound libraries. However, it became apparent that some human disease related targets could not be addressed by the libraries commonly used which typically are comprised of large numbers of structurally similar compounds. The last decade has seen a paradigm shift in library construction, with particular emphasis now being placed on increasing a library's structural, and thus functional diversity, rather than only its size. Diversity-oriented synthesis (DOS) aims to generate such structural diversity efficiently. This tutorial review has been written to introduce the subject to a broad audience and recent achievements in both the preparation and the screening of structurally diverse compound collections against so-called 'undruggable' targets are highlighted.     

How large does a compound screening collection need to be?

Increasingly, chemical libraries are being produced which are focused on a biological target or group of related targets, rather than simply being constructed in a combinatorial fashion. A screening collection compiled from such libraries will contain multiple analogues of a number of discrete series of compounds. The question arises as to how many analogues are necessary to represent each series in order to ensure that an active series will be identified. Based on a simple probabilistic argument and supported by in-house screening data, guidelines are given for the number of compounds necessary to achieve a "hit", or series of hits, at various levels of certainty. Obtaining more than one hit from the same series is useful since this gives early acquisition of SAR (structure-activity relationship) and confirms a hit is not a singleton. We show that screening collections composed of only small numbers of analogues of each series are sub-optimal for SAR acquisition. Based on these studies, we recommend a minimum series size of about 200 compounds. This gives a high probability of confirmatory SAR (i.e. at least two hits from the same series). More substantial early SAR (at least 5 hits from the same series) can be gained by using series of about 650 compounds each. With this level of information being generated, more accurate assessment of the likely success of the series in hit-to-lead and later stage development becomes possible.     

TINS, target immobilized NMR screening: an efficient and sensitive method for ligand discovery

We propose a ligand screening method, called TINS (target immobilized NMR screening), which reduces the amount of target required for the fragment-based approach to drug discovery. Binding is detected by comparing 1D NMR spectra of compound mixtures in the presence of a target immobilized on a solid support to a control sample. The method has been validated by the detection of a variety of ligands for protein and nucleic acid targets (K(D) from 60 to 5000 muM). The ligand binding capacity of a protein was undiminished after 2000 different compounds had been applied, indicating the potential to apply the assay for screening typical fragment libraries. TINS can be used in competition mode, allowing rapid characterization of the ligand binding site. TINS may allow screening of targets that are difficult to produce or that are insoluble, such as membrane proteins.     

Learning from the data: mining of large high-throughput screening databases

High-throughput screening (HTS) campaigns in pharmaceutical companies have accumulated a large amount of data for several million compounds over a couple of hundred assays. Despite the general awareness that rich information is hidden inside the vast amount of data, little has been reported for a systematic data mining method that can reliably extract relevant knowledge of interest for chemists and biologists. We developed a data mining approach based on an algorithm called ontology-based pattern identification (OPI) and applied it to our in-house HTS database. We identified nearly 1500 scaffold families with statistically significant structure-HTS activity profile relationships. Among them, dozens of scaffolds were characterized as leading to artifactual results stemming from the screening technology employed, such as assay format and/or readout. Four types of compound scaffolds can be characterized based on this data mining effort: tumor cytotoxic, general toxic, potential reporter gene assay artifact, and target family specific. The OPI-based data mining approach can reliably identify compounds that are not only structurally similar but also share statistically significant biological activity profiles. Statistical tests such as Kruskal-Wallis test and analysis of variance (ANOVA) can then be applied to the discovered scaffolds for effective assignment of relevant biological information. The scaffolds identified by our HTS data mining efforts are an invaluable resource for designing SAR-robust diversity libraries, generating in silico biological annotations of compounds on a scaffold basis, and providing novel target family specific scaffolds for focused compound library design.     

Assessing the scaffold diversity of screening libraries

Medicinal chemists have traditionally realized assessments of chemical diversity and subsequent compound acquisition, although a recent study suggests that experts are usually inconsistent in reviewing large data sets. To analyze the scaffold diversity of commercially available screening collections, we have developed a general workflow aimed at (1) identifying druglike compounds, (2) clustering them by maximum common substructures (scaffolds), (3) measuring the scaffold diversity encoded by each screening collection independently of its size, and finally (4) merging all common substructures in a nonredundant scaffold library that can easily be browsed by structural and topological queries. Starting from 2.4 million compounds out of 12 commercial sources, four categories of libraries could be identified: large- and medium-sized combinatorial libraries (low scaffold diversity), diverse libraries (medium diversity, medium size), and highly diverse libraries (high diversity, low size). The chemical space covered by the scaffold library can be searched to prioritize scaffold-focused libraries.     

Cheminformatics in MS-based environmental exposomics: Current achievements and future directions

Compound annotation using MS/MS data is the major bottleneck in interpretation of mass spectrometry data during non-targeted screening and suspect screening exposomics studies. Apart from compound identification using available databases or mass spectral libraries, the true challenge comes when completely new compounds have to be identified. Along with recent advances in MS instrumentation that set grounds to a new revolutionary age in environmental exposomics, a multitude of cheminformatics annotation approaches has been developed. Herein, we review the basic principles of the cutting-edge cheminformatics MS-based approaches employed in eco-exposome annotation.We give a solid background discussing the eco-exposome concept in relation to the advances in MS instrumentation, and define the three crucial cheminformatics tasks used in the eco-exposome annotation: molecular formula assignment, compound prioritization and compound annotation. The basic principles of compound annotation are discussed, which are based on three approaches of utilizing structural information inherent to MS data. These involve direct, indirect and joint annotation approaches. We assess their performance through the ability to annotate eco-exposome constituents. We discuss future perspectives and give directions to new annotation strategies and performance evaluation protocols aiming to solve current issues hampering the incorporation of cheminformatics annotation approaches in regular eco-exposome annotation workflows.     

Surrogate docking: structure-based virtual screening at high throughput speed

Summary Structure-based screening using fully flexible docking is still too slow for large molecular libraries. High quality docking of a million molecule library can take days even on a cluster with hundreds of CPUs. This performance issue prohibits the use of fully flexible docking in the design of large combinatorial libraries. We have developed a fast structure-based screening method, which utilizes docking of a limited number of compounds to build a 2D QSAR model used to rapidly score the rest of the database. We compare here a model based on radial basis functions and a Bayesian categorization model. The number of compounds that need to be actually docked depends on the number of docking hits found. In our case studies reasonable quality models are built after docking of the number of molecules containing 50 docking hits. The rest of the library is screened by the QSAR model. Optionally a fraction of the QSAR-prioritized library can be docked in order to find the true docking hits. The quality of the model only depends on the training set size – not on the size of the library to be screened. Therefore, for larger libraries the method yields higher gain in speed no change in performance. Prioritizing a large library with these models provides a significant enrichment with docking hits: it attains the values of 13 and 35 at the beginning of the score-sorted libraries in our two case studies: screening of the NCI collection and a combinatorial libraries on CDK2 kinase structure. With such enrichments, only a fraction of the database must actually be docked to find many of the true hits. The throughput of the method allows its use in screening of large compound collections and in the design of large combinatorial libraries. The strategy proposed has an important effect on efficiency but does not affect retrieval of actives, the latter being determined by the quality of the docking method itself. Electronic supplementary material is available at http://dx.doi.org/10.1007/s10822-005-9002-6.     

Identification of Structure–Activity Relationships from Screening a Structurally Compact DNA‐Encoded Chemical Library

Methods for the rapid and inexpensive discovery of hit compounds are essential for pharmaceutical research and DNA‐encoded chemical libraries represent promising tools for this purpose. We here report on the design and synthesis of DAL‐100K, a DNA‐encoded chemical library containing 103 200 structurally compact compounds. Affinity screening experiments and DNA‐sequencing analysis provided ligands with nanomolar affinities to several proteins, including prostate‐specific membrane antigen and tankyrase 1. Correlations of sequence counts with binding affinities and potencies of enzyme inhibition were observed and enabled the identification of structural features critical for activity. These results indicate that libraries of this type represent a useful source of small‐molecule binders for target proteins of pharmaceutical interest and information on structural features important for binding.     

ToxAlerts: A Web Server of Structural Alerts for Toxic Chemicals and Compounds with Potential Adverse Reactions

The article presents a Web-based platform for collecting and storing toxicological structural alerts from literature and for virtual screening of chemical libraries to flag potentially toxic chemicals and compounds that can cause adverse side effects. An alert is uniquely identified by a SMARTS template, a toxicological endpoint, and a publication where the alert was described. Additionally, the system allows storing complementary information such as name, comments, and mechanism of action, as well as other data. Most importantly, the platform can be easily used for fast virtual screening of large chemical datasets, focused libraries, or newly designed compounds against the toxicological alerts, providing a detailed profile of the chemicals grouped by structural alerts and endpoints. Such a facility can be used for decision making regarding whether a compound should be tested experimentally, validated with available QSAR models, or eliminated from consideration altogether. The alert-based screening can also be helpful for an easier interpretation of more complex QSAR models. The system is publicly accessible and tightly integrated with the Online Chemical Modeling Environment (OCHEM, http://ochem.eu ). The system is open and expandable: any registered OCHEM user can introduce new alerts, browse, edit alerts introduced by other users, and virtually screen his/her data sets against all or selected alerts. The user sets being passed through the structural alerts can be used at OCHEM for other typical tasks: exporting in a wide variety of formats, development of QSAR models, additional filtering by other criteria, etc. The database already contains almost 600 structural alerts for such endpoints as mutagenicity, carcinogenicity, skin sensitization, compounds that undergo metabolic activation, and compounds that form reactive metabolites and, thus, can cause adverse reactions. The ToxAlerts platform is accessible on the Web at http://ochem.eu/alerts , and it is constantly growing.     

Design and characterization of libraries of molecular fragments for use in NMR screening against protein targets

We have designed four generations of a low molecular weight fragment library for use in NMR-based screening against protein targets. The library initially contained 723 fragments which were selected manually from the Available Chemicals Directory. A series of in silico filters and property calculations were developed to automate the selection process, allowing a larger database of 1.79 M available compounds to be searched for a further 357 compounds that were added to the library. A kinase binding pharmacophore was then derived to select 174 kinase-focused fragments. Finally, an additional 61 fragments were selected to increase the number of different pharmacophores represented within the library. All of the fragments added to the library passed quality checks to ensure they were suitable for the screening protocol, with appropriate solubility, purity, chemical stability, and unambiguous NMR spectrum. The successive generations of libraries have been characterized through analysis of structural properties (molecular weight, lipophilicity, polar surface area, number of rotatable bonds, and hydrogen-bonding potential) and by analyzing their pharmacophoric complexity. These calculations have been used to compare the fragment libraries with a drug-like reference set of compounds and a set of molecules that bind to protein active sites. In addition, an analysis of the overall results of screening the library against the ATP binding site of two protein targets (HSP90 and CDK2) reveals different patterns of fragment binding, demonstrating that the approach can find selective compounds that discriminate between related binding sites.     

Privileged Structures Revisited

Privileged structures inspire compound library design in medicinal chemistry. We performed a comprehensive analysis of 1.4 million bioactive compounds, with the aim of assessing the prevalence of certain molecular frameworks. We used the Shannon entropy formalism to quantify the promiscuity of the most frequently observed atom scaffolds across the annotated target families. This analysis revealed an apparent inverse relationship between hydrogen-bond-acceptor count of a scaffold and its potential promiscuity. The results further suggest that chemically easily accessible scaffolds can serve as templates for the generation of bespoke compound libraries with differing degrees of multiple target engagement, and heterocyclic, sp³-rich frameworks are particularly suited for target-focused library design. The outcome of our study enables us to place some of the many narratives surrounding the concept of privileged structures into a critical context.     

NIR-mbc94, a fluorescent ligand that binds to endogenous CB(2) receptors and is amenable to high-throughput screening

High-throughput screening (HTS) of chemical libraries is often used for the unbiased identification of compounds interacting with G protein-coupled receptors (GPCRs), the largest family of therapeutic targets. However, current HTS methods require removing GPCRs from their native environment, which modifies their pharmacodynamic properties and biases the screen toward false positive hits. Here, we developed and validated a molecular imaging (MI) agent, NIR-mbc94, which emits near infrared (NIR) light and selectively binds to endogenously expressed cannabinoid CB(2) receptors,?a recognized target for treating autoimmune diseases, chronic pain and cancer. The precision and ease of this assay allows for the HTS of compounds interacting with CB(2) receptors expressed in their native environment.     

Application and limitations of X-ray crystallographic data in structure-based ligand and drug design

Structure-based design usually focuses upon the optimization of ligand affinity. However, successful drug design also requires the optimization of many other properties. The primary source of structural information for protein-ligand complexes is X-ray crystallography. The uncertainties introduced during the derivation of an atomic model from the experimentally observed electron density data are not always appreciated. Uncertainties in the atomic model can have significant consequences when this model is subsequently used as the basis of manual design, docking, scoring, and virtual screening efforts. Docking and scoring algorithms are currently imperfect. A good correlation between observed and calculated binding affinities is usually only observed only when very large ranges of affinity are considered. Errors in the correlation often exceed the range of affinities commonly encountered during lead optimization. Some structure-based design approaches now involve screening libraries by using technologies based on NMR spectroscopy and X-ray crystallography to discover small polar templates, which are used for further optimization. Such compounds are defined as leadlike and are also sought by more traditional high-throughput screening technologies. Structure-based design and HTS technologies show important complementarity and a degree of convergence.     

Comparative performance of several flexible docking programs and scoring functions: enrichment studies for a diverse set of pharmaceutically relevant targets

Virtual screening by molecular docking has become a widely used approach to lead discovery in the pharmaceutical industry when a high-resolution structure of the biological target of interest is available. The performance of three widely used docking programs (Glide, GOLD, and DOCK) for virtual database screening is studied when they are applied to the same protein target and ligand set. Comparisons of the docking programs and scoring functions using a large and diverse data set of pharmaceutically interesting targets and active compounds are carried out. We focus on the problem of docking and scoring flexible compounds which are sterically capable of docking into a rigid conformation of the receptor. The Glide XP methodology is shown to consistently yield enrichments superior to the two alternative methods, while GOLD outperforms DOCK on average. The study also shows that docking into multiple receptor structures can decrease the docking error in screening a diverse set of active compounds.