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.     

Selection of in silico drug screening results for G-protein-coupled receptors by using universal active probes

We developed a new protocol for in silico drug screening for G-protein-coupled receptors (GPCRs) using a set of "universal active probes" (UAPs) with an ensemble docking procedure. UAPs are drug-like compounds, which are actual active compounds of a variety of known proteins. The current targets were nine human GPCRs whose three-dimensional (3D) structures are unknown, plus three GPCRs, namely β(2)-adrenergic receptor (ADRB2), A(2A) adenosine receptor (A(2A)), and dopamine D3 receptor (D(3)), whose 3D structures are known. Homology-based models of the GPCRs were constructed based on the crystal structures with careful sequence inspection. After subsequent molecular dynamics (MD) simulation taking into account the explicit lipid membrane molecules with periodic boundary conditions, we obtained multiple model structures of the GPCRs. For each target structure, docking-screening calculations were carried out via the ensemble docking procedure, using both true active compounds of the target proteins and the UAPs with the multiple target screening (MTS) method. Consequently, the multiple model structures showed various screening results with both poor and high hit ratios, the latter of which could be identified as promising for use in in silico screening to find candidate compounds to interact with the proteins. We found that the hit ratio of true active compounds showed a positive correlation to that of the UAPs. Thus, we could retrieve appropriate target structures from the GPCR models by applying the UAPs, even if no active compound is known for the GPCRs. Namely, the screening result that showed a high hit ratio for the UAPs could be used to identify actual hit compounds for the target GPCRs.     

Rainbow beads: a color coding method to facilitate high-throughput screening and optimization of one-bead one-compound combinatorial libraries

We have developed a new color-encoding method that facilitates high-throughput screening of one-bead one-compound (OBOC) combinatorial libraries. Polymer beads displaying chemical compounds or families of compounds are stained with oil-based organic dyes that are used as coding tags. The color dyes do not affect cell binding to the compounds displayed on the surface of the beads. We have applied such rainbow beads in a multiplex manner to discover and profile ligands against cell surface receptors. In the first application, a series of OBOC libraries with different scaffolds or motifs are each color-coded; small samples of each library are then combined and screened concurrently against live cells for cell attachment. Preferred libraries can be rapidly identified and selected for subsequent large-scale screenings for cell surface binding ligands. In a second application, beads with a series of peptide analogues (e.g., alanine scan) are color-coded, combined, and tested for binding against a specific cell line in a single-tissue culture well; the critical residues required for binding can be easily determined. In a third application, ligands reacting against a series of integrins are color-coded and used as a readily applied research tool to determine the integrin profile of any cell type. One major advantage of this straightforward and yet powerful method is that only an ordinary inverted microscope is needed for the analysis, instead of sophisticated (and expensive) fluorescent microscopes or flow cytometers.     

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.     

Past and future perspectives of synthetic peptide libraries

Combinatorial preparation and HTS of arrays of compounds have increased the speed of drug discovery. A strong impulse in this field has come by the introduction of the solid phase synthesis method that, through automation and miniaturization, has paved the way to the preparation of large collections of compounds in compact and trackable formats. Due to the well established synthetic procedures, peptides have been largely used to develop the basic concepts of combinatorial chemistry and peptide libraries are still successfully employed in screening programs. However, peptides generally do not fulfil the requirements of low conformational flexibility, stability and bioavailability needed for good drug candidates and peptide leads with high potency and selectivity are often made "druggable" by conversion to more stable structures with improved pharmacological profiles. Such an approach makes the screening of peptide libraries still a valuable tool for drug discovery. We propose here a panoramic review of the most common methods for the preparation and screening of peptide libraries and the most interesting findings of the last decade. We also report on a new approach we follow in our laboratory that is based on the use of "simplified" libraries composed by a minimum number of non-redundant amino acids for the assembly of short peptides. The choice of amino acids is dictated by diversity in lipophilicity, MW, charge and polarity. Newly identified active sequences are then modified by preparing new variants containing analogous amino acids, so that the chemical space occupied by the excluded residues can be explored. This approach offers the advantage of simplifying the synthesis and deconvolution of libraries and provides new active compounds with a molecular size similar to that of small molecules, to which they can be easily converted.     

The design and application of target-focused compound libraries

Target-focused compound libraries are collections of compounds which are designed to interact with an individual protein target or, frequently, a family of related targets (such as kinases, voltage-gated ion channels, serine/cysteine proteases). They are used for screening against therapeutic targets in order to find hit compounds that might be further developed into drugs. The design of such libraries generally utilizes structural information about the target or family of interest. In the absence of such structural information, a chemogenomic model that incorporates sequence and mutagenesis data to predict the properties of the binding site can be employed. A third option, usually pursued when no structural data are available, utilizes knowledge of the ligands of the target from which focused libraries can be developed via scaffold hopping. Consequently, the methods used for the design of target-focused libraries vary according to the quantity and quality of structural or ligand data that is available for each target family. This article describes examples of each of these design approaches and illustrates them with case studies, which highlight some of the issues and successes observed when screening target-focused libraries.     

Combinatorial library design using a multiobjective genetic algorithm

Early results from screening combinatorial libraries have been disappointing with libraries either failing to deliver the improved hit rates that were expected or resulting in hits with characteristics that make them undesirable as lead compounds. Consequently, the focus in library design has shifted toward designing libraries that are optimized on multiple properties simultaneously, for example, diversity and "druglike" physicochemical properties. Here we describe the program MoSELECT that is based on a multiobjective genetic algorithm and which is able to suggest a family of solutions to multiobjective library design where all the solutions are equally valid and each represents a different compromise between the objectives. MoSELECT also allows the relationships between the different objectives to be explored with competing objectives easily identified. The library designer can then make an informed choice on which solution(s) to explore. Various performance characteristics of MoSELECT are reported based on a number of different combinatorial libraries.     

Phenotypic screening of small molecule libraries by high throughput cell imaging

We have developed high throughput fluorescence cell imaging methods to screen chemical libraries for compounds with effects on diverse aspects of cell physiology. We describe screens for compounds that arrest cells in mitosis, that block cell migration, and that block the secretory pathway. Each of these screens yielded specific inhibitors for research use, and the mitosis screen identified Eg5 as a potential target protein for cancer chemotherapy. Cell imaging provides a large amount of information from primary screening data that can be used to distinguish compounds with different effects on cells, and together with automated analysis, to quantitate compound effects.     

Cell-based screening: a high throughput flow cytometry platform for identification of cell-specific targeting molecules

Targeting of drugs and genes to specific cell types is an emerging paradigm in the treatment of many medical conditions. However, targeting structures such as peptides are susceptible to rapid inactivation in vivo. To address this problem, novel targeting molecules can now be rapidly synthesized using a combinatorial approach. Methods to screen the large libraries created in this process are often lacking or compatible only with solution-based screening. This report describes a high-throughput cell-based method utilizing flow cytometry, capable of rapidly screening large libraries of molecules simultaneously for biological functionality and stability. In this method, each library molecule is attached to a microsphere exhibiting a unique set of optical properties, or "fingerprint", conferring modularity and multiplex capability. We investigated the multiplex capability of our flow cytometric method to determine its capacity for high-throughput screening. Current instrumentation in our laboratory allows the screening of at least 75 unique compounds in a single well, a number comparable to available solution-based assays. In state-of-the-art configuration, however, this methodology can support the screening of up to 1875 compounds per well, achieving high-throughput potential in a single multiwell plate. We also investigated the binding capability of targeted microspheres to adherent target cells. These microspheres exhibited a 12-fold increase in binding over control, untargeted microspheres. Competitive inhibition experiments with soluble ligand confirmed the specificity of microsphere binding. Overall, the methodology proposed here is capable of quickly and effectively screening large libraries of targeting molecules using instrumentation readily available to the greater research community.     

Stalis: A Computational Method for Template-Based Ab Initio Ligand Design

Proteins interact with small molecules through specific molecular recognition, which is central to essential biological functions in living systems. Therefore, understanding such interactions is crucial for basic sciences and drug discovery. Here, we present S tructure t emplate-based a b initio li gand design s olution (Stalis), a knowledge-based approach that uses structure templates from the Protein Data Bank libraries of whole ligands and their fragments and generates a set of molecules (virtual ligands) whose structures represent the pocket shape and chemical features of a given target binding site. Our benchmark performance evaluation shows that ligand structure-based virtual screening using virtual ligands from Stalis outperforms a receptor structure-based virtual screening using AutoDock Vina, demonstrating reliable overall screening performance applicable to computational high-throughput screening. However, virtual ligands from Stalis are worse in recognizing active compounds at the small fraction of a rank-ordered list of screened library compounds than crystal ligands, due to the low resolution of the virtual ligand structures. In conclusion, Stalis can facilitate drug discovery research by designing virtual ligands that can be used for fast ligand structure-based virtual screening. Moreover, Stalis provides actual three-dimensional ligand structures that likely bind to a target protein, enabling to gain structural insight into potential ligands. Stalis can be an efficient computational platform for high-throughput ligand design for fundamental biological study and drug discovery research at the proteomic level. © 2019 Wiley Periodicals, Inc.     

Rationalizing the role of SAR tolerance for ligand-based virtual screening

It is well appreciated that the results of ligand-based virtual screening (LBVS) are much influenced by methodological details, given the generally strong compound class dependence of LBVS methods. It is less well understood to what extent structure-activity relationship (SAR) characteristics might influence the outcome of LBVS. We have assessed the hypothesis that the success of prospective LBVS depends on the SAR tolerance of screening targets, in addition to methodological aspects. In this context, SAR tolerance is rationalized as the ability of a target protein to specifically interact with series of structurally diverse active compounds. In compound data sets, SAR tolerance articulates itself as SAR continuity, i.e., the presence of structurally diverse compounds having similar potency. In order to analyze the role of SAR tolerance for LBVS, activity landscape representations of compounds active against 16 different target proteins were generated for which successful LBVS applications were reported. In all instances, the activity landscapes of known active compounds contained multiple regions of local SAR continuity. When analyzing the location of newly identified LBVS hits and their SAR environments, we found that these hits almost exclusively mapped to regions of distinct local SAR continuity. Taken together, these findings indicate the presence of a close link between SAR tolerance at the target level, SAR continuity at the ligand level, and the probability of LBVS success.     

Two-vial, LC-MS identification of ephedrine receptors from a solution-phase dynamic combinatorial library of over 9000 components

Reports on dynamic combinatorial chemistry have almost exclusively involved small libraries of 10-100 compounds. We now show how more than 9000 compounds can be screened in a single LC-MS analysis to reveal a series of new receptors that bind ephedrine in water. These results demonstrate the feasibility of screening DCLs that are substantially larger than the solution-phase libraries reported thus far.     

Activity-aware clustering of high throughput screening data and elucidation of orthogonal structure-activity relationships

From a medicinal chemistry point of view, one of the primary goals of high throughput screening (HTS) hit list assessment is the identification of chemotypes with an informative structure-activity relationship (SAR). Such chemotypes may enable optimization of the primary potency, as well as selectivity and phamacokinetic properties. A common way to prioritize them is molecular clustering of the hits. Typical clustering techniques, however, rely on a general notion of chemical similarity or standard rules of scaffold decomposition and are thus insensitive to molecular features that are enriched in biologically active compounds. This hinders SAR analysis, because compounds sharing the same pharmacophore might not end up in the same cluster and thus are not directly compared to each other by the medicinal chemist. Similarly, common chemotypes that are not related to activity may contaminate clusters, distracting from important chemical motifs. We combined molecular similarity and Bayesian models and introduce (I) a robust, activity-aware clustering approach and (II) a feature mapping method for the elucidation of distinct SAR determinants in polypharmacologic compounds. We evaluated the method on 462 dose-response assays from the Pubchem Bioassay repository. Activity-aware clustering grouped compounds sharing molecular cores that were specific for the target or pathway at hand, rather than grouping inactive scaffolds commonly found in compound series. Many of these core structures we also found in literature that discussed SARs of the respective targets. A numerical comparison of cores allowed for identification of the structural prerequisites for polypharmacology, i.e., distinct bioactive regions within a single compound, and pointed toward selectivity-conferring medchem strategies. The method presented here is generally applicable to any type of activity data and may help bridge the gap between hit list assessment and designing a medchem strategy.     

Design of a compound screening collection for use in high throughput screening

In this paper we introduce a quantitative model that relates chemical structural similarity to biological activity, and in particular to the activity of lead series of compounds in high-throughput assays. From this model we derive the optimal screening collection make up for a given fixed size of screening collection, and identify the conditions under which a diverse collection of compounds or a collection focusing on particular regions of chemical space are appropriate strategies. We derive from the model a diversity function that may be used to assess compounds for acquisition or libraries for combinatorial synthesis by their ability to complement an existing screening collection. The diversity function is linked directly through the model to the goal of more frequent discovery of lead series from high-throughput screening. We show how the model may also be used to derive relationships between collection size and probabilities of lead discovery in high-throughput screening, and to guide the judicious application of structural filters.     

Optimization of high throughput virtual screening by combining shape-matching and docking methods

Receptor flexibility is a critical issue in structure-based virtual screening methods. Although a multiple-receptor conformation docking is an efficient way to account for receptor flexibility, it is still too slow for large molecular libraries. It was reported that a fast ligand-centric, shape-based virtual screening was more consistent for hit enrichment than a typical single-receptor conformation docking. Thus, we designed a "distributed docking" method that improves virtual high throughput screening by combining a shape-matching method with a multiple-receptor conformation docking. Database compounds are classified in advance based on shape similarities to one of the crystal ligands complexed with the target protein. This classification enables us to pick the appropriate receptor conformation for a single-receptor conformation docking of a given compound, thereby avoiding time-consuming multiple docking. In particular, this approach utilizes cross-docking scores of known ligands to all available receptor structures in order to optimize the algorithm. The present virtual screening method was tested for reidentification of known PPARgamma and p38 MAP kinase active compounds. We demonstrate that this method improves the enrichment while maintaining the computation speed of a typical single-receptor conformation docking.     

Identification of Novel β3-Adrenoceptor Agonists Using Energetic Analysis, Structure Based Pharmacophores and Virtual Screening

β3 Adrenergic receptor (β3-AR), is a potential therapeutic target for the treatment of type II diabetes and obesity. We report the identification of novel compounds as β3-AR agonists by integrating different approaches of energetic analysis, structure based pharmacophore designing and virtual screening. In a step wise filtering protocol, structure based virtual screening of 2,33,450 compounds was done. These molecules were docked into the active site of the receptor utilizing three levels of accuracy; ligands passing the HTVS (high throughput virtual screening) step were subsequently analyzed in Glide SP (Standard Precision) and finally in Glide XP (Extra Precision) to estimate the receptor ligand binding affinities. In the second step a total of 300 pharmacophore hypotheses were generated from a set of known and diverse β3-AR agonists. The best hypothesis showed six features: three hydrogen bond acceptors, one positively charged group, and two aromatic rings. To cross validate, pharmacophore filtering was done on the set of shortlisted compounds from structure based VS (virtual screening). The different screening techniques employed were validated using enrichment factor calculations. The energetic based Pharmacophore performed fairly well at distinguishing active from the inactive compounds and yielded a greater diversity of active molecules whereas the number of actives retrieved in the case of structure based screening was the highest.     

Discovery of 4H-thieno[3,2-b]pyrrole derivatives as potential anticancer agents

In this study, we describe the discovery of the 4H-thieno[3,2-b]pyrrole derivatives as an useful scaffold to obtain potent lead compounds for the treatment of colon cancer. We first started with the 4H-thieno[3,2-b]pyrrole derivatives which come from compound libraries screening, and then optimized their structures based on the cellular activities and pharmacophore models. The inhibition rate of cell growth assay demonstrated that this series compounds showed better inhibitory activities against colon cancer cells than other tested tumor cells. Moreover, the target of the most active compound 8i was explored by target fishing strategy and validated by molecular docking and biological activity analysis. The results of apoptosis and flow cytometry demonstrated that compound 8i induces cell apoptosis probably by inhibiting activity of methionine aminopeptidase 2, therefore compound 8i may be a potent inhibitor to methionine aminopeptidase 2.     

Efficient microwave-assisted synthesis of fused benzoxazepine–isoquinoline derivatives via an Ugi reaction/tautomerization/intramolecular SNAr reaction sequence

A series of compounds containing the benzoxazepine-isoquinoline scaffold was synthesized in a one-pot procedure via an Ugi reaction followed by tautomerization reaction between the keto and enol forms and intramolecular nucleophilic substitution. The microwave-assisted strategy allowed the facile synthesis of a library of target compounds and is applicable to the construction of diverse libraries of related compounds for high throughput screening in medicinal chemistry.