Chemometric analysis of ligand receptor complementarity: identifying Complementary Ligands Based on Receptor Information (CoLiBRI)

Electron Density Analysis of SARS-CoV-2 RNA-Dependent RNA Polymerase Complexes

The work is devoted to the study of the complementarity of the electronic structures of the ligands and SARS-CoV-2 RNA-dependent RNA polymerase. The research methodology was based on determining of 3D maps of electron densities of complexes using an original quantum free-orbital AlteQ approach. We observed a positive relationship between the parameters of the electronic structure of the enzyme and ligands. A complementarity factor of the enzyme-ligand complexes has been proposed. The console applications of the AlteQ complementarity assessment for Windows and Linux (alteq_map_enzyme_ligand_4_win.exe and alteq_map_enzyme_ligand_4_linux) are available for free at the ChemoSophia webpage.

Quantitative structure–activity relationships study of potent pyridinone scaffold derivatives as HIV-1 integrase inhibitors with therapeutic applications

Human immunodeficiency virus-1 (HIV-1) integrase appears to be a crucial target for developing new anti-HIV-1 therapeutic agents. Different quantitative structure–activity relationships (QSARs) algorithms have been used in order to develop efficient model(s) to predict the activity of new pyridinone derivatives against HIV-1 integrase. Multiple linear regression (MLR) and combined principal component analysis (PCA) with MLR have been applied to build QSAR models for a set of new pyridinone derivatives as potent anti-HIV-1 therapeutic agents. Four different approaches based on MLR method including; concrete-MLR, stepwise-MLR, concrete PCA–MLR and stepwise PCA–MLR were utilized for this aim. Twenty two different sets of descriptors containing 1613 descriptors were constructed for each optimized molecule. Comparison between predictability of the “concrete” and “stepwise” procedure in two different algorithms of MLR and PCA models indicated the advantage of the stepwise procedure over that of the simple concrete method. Although the PCA was employed for dimension reduction, using stepwise PCA–MLR model showed that the method has higher ability to predict the compounds’ activity. The stepwise PCA–MLR model showed highly validated statistical results both in fitting and prediction processes ([Formula: see text] and [Formula: see text]). Therefore, using stepwise PCA approach is suitable to remove ineffective descriptors, which results in remaining efficient descriptors for building good predictability stepwise PCA–MLR. The stepwise hybrid approach of PCA–MLR may be useful in derivation of highly predictive and interpretable QSAR models.

Synthesis and biological evaluation of dihydropyrano-[2,3-c]pyrazoles as a new class of PPARγ partial agonists

Peroxisome proliferator-activated receptor γ (PPARγ) is a well-known target for thiazolidinedione antidiabetic drugs. In this paper, we present the synthesis and biological evaluation of a series of dihydropyrano[2,3-c]pyrazole derivatives as a novel family of PPARγ partial agonists. Two analogues were found to display high affinity for PPARγ with potencies in the micro molar range. Both of these hits were selective against PPARγ, since no activity was measured when tested against PPARα, PPARδ and RXRα. In addition, a novel modelling approach based on multiple individual flexible alignments was developed for the identification of ligand binding interactions in PPARγ. In combination with cell-based transactivation experiments, the flexible alignment model provides an excellent analytical tool to evaluate and visualize the effect of ligand chemical structure with respect to receptor binding mode and biological activity.

Online Molecular Docking Resources

This chapter aims to present the available online resources that are used for protein modeling with accent to online molecular docking resources. SwissDock, MTiAutoDock, and PatchDock online docking tools are described and a few illustrative examples concerning the molecular docking studies for the cytochrom P450 interactions with the fungicide difenoconazole. The results obtained using different servers based on distinct approaches are compared and the advantages and/or disadvantages of every server are illustrated.

Discovery of novel drugs for promising targets

BACKGROUND

Once a promising drug target is identified, the steps to actually discover and optimize a drug are diverse and challenging.

OBJECTIVE

The goal of this study was to provide a road map to navigate drug discovery.

METHODS

Review general steps for drug discovery and provide illustrating references.

RESULTS

A number of approaches are available to enhance and accelerate target identification and validation. Consideration of a variety of potential mechanisms of action of potential drugs can guide discovery efforts. The hit to lead stage may involve techniques such as high-throughput screening, fragment-based screening, and structure-based design, with informatics playing an ever-increasing role. Biologically relevant screening models are discussed, including cell lines, 3-dimensional culture, and in vivo screening. The process of enabling human studies for an investigational drug is also discussed.

CONCLUSIONS

Drug discovery is a complex process that has significantly evolved in recent years.

In silico-screening approaches for lead generation: identification of novel allosteric modulators of human-erythrocyte pyruvate kinase

Identification of allosteric binding site modulators have gained increased attention lately for their potential to be developed as selective agents with a novel chemotype and targeting perhaps a new and unique binding site with probable fewer side effects. Erythrocyte pyruvate kinase (R-PK) is an important glycolytic enzyme that can be pharmacologically modulated through its allosteric effectors for the treatment of hemolytic anemia, sickle-cell anemia, hypoxia-related diseases, and other disorders arising from erythrocyte PK malfunction. An in-silico screening approach was applied to identify novel allosteric modulators of pyruvate kinase. A small-molecules database of the National Cancer Institute (NCI), was virtually screened based on structure/ligand-based pharmacophore. The virtual screening campaign led to the identification of several compounds with similar pharmacophoric features as fructose-1,6-bisphosphate (FBP), the natural allosteric activator of the kinase. The compounds were subsequently docked into the FBP-binding site using the programs FlexX and GOLD, and their interactions with the protein were analyzed with the energy-scoring function of HINT. Seven promising candidates were obtained from the NCI and subjected to kinetics analysis, which revealed both activators and inhibitors of the R-isozyme of PK (R-PK).

Do crystal structures obviate the need for theoretical models of GPCRs for structure-based virtual screening?

Recent highly expected structural characterizations of agonist-bound and antagonist-bound beta-2 adrenoreceptor (β2AR) by X-ray crystallography have been widely regarded as critical advances to enable more effective structure-based discovery of GPCRs ligands. It appears that this very important development may have undermined many previous efforts to develop 3D theoretical models of GPCRs. To address this question directly, we have compared several historical β2AR models versus the inactive state and nanobody-stabilized active state of β2AR crystal structures in terms of their structural similarity and effectiveness of use in virtual screening for β2AR specific agonists and antagonists. Theoretical models, incluing both homology and de novo types, were collected from five different groups who have published extensively in the field of GPCRs modeling. All models were built before X-ray structures became available. In general, β2AR theoretical models differ significantly from the crystal structure in terms of TMH definition and the global packing. Nevertheless, surprisingly, several models afforded hit rates resulting from virtual screening of large chemical library enriched by known β2AR ligands that exceeded those using X-ray structures. The hit rates were particularly higher for agonists. Furthemore, the screening performance of models is associated with local structural quality, such as the RMSDs for binding pocket residues and the ability to capture accurately, most if not all critical protein/ligand interactions. These results suggest that carefully built models of GPCRs could capture critical chemical and structural features of the binding pocket, and thus may be even more useful for practical structure-based drug discovery than X-ray structures.

Predictive cheminformatics in drug discovery: statistical modeling for analysis of micro-array and gene expression data

The vast amounts of chemical and biological data available through robotic high-throughput assays and micro-array technologies require computational techniques for visualization, analysis, and predictive -modeling. Predictive cheminformatics and bioinformatics employ statistical methods to mine this data for hidden correlations and to retrieve molecules or genes with desirable biological activity from large databases, for the purpose of drug development. While many statistical methods are commonly employed and widely accessible, their proper use involves due consideration to data representation and preprocessing, model validation and domain of applicability estimation, similarity assessment, the nature of the structure-activity landscape, and model interpretation. This chapter seeks to review these considerations in light of the current state of the art in statistical modeling and to summarize the best practices in predictive cheminformatics.

Application of Machine Leaning in Drug Discovery and Development

Machine learning techniques have been widely used in drug discovery and development, particularly in the areas of cheminformatics, bioinformatics and other types of pharmaceutical research. It has been demonstrated they are suitable for large high dimensional data, and the models built with these methods can be used for robust external predictions. However, various problems and challenges still exist, and new approaches are in great need. In this Chapter, the authors will review the current development of machine learning techniques, and especially focus on several machine learning techniques they developed as well as their application to model building, lead discovery via virtual screening, integration with molecular docking, and prediction of off-target properties. The authors will suggest some potential different avenues to unify different disciplines, such as cheminformatics, bioinformatics and systems biology, for the purpose of developing integrated in silico drug discovery and development approaches.

LOCAL KERNEL CANONICAL CORRELATION ANALYSIS WITH APPLICATION TO VIRTUAL DRUG SCREENING

Drug discovery is the process of identifying compounds which have potentially meaningful biological activity. A major challenge that arises is that the number of compounds to search over can be quite large, sometimes numbering in the millions, making experimental testing intractable. For this reason computational methods are employed to filter out those compounds which do not exhibit strong biological activity. This filtering step, also called virtual screening reduces the search space, allowing for the remaining compounds to be experimentally tested.In this paper we propose several novel approaches to the problem of virtual screening based on Canonical Correlation Analysis (CCA) and on a kernel-based extension. Spectral learning ideas motivate our proposed new method called Indefinite Kernel CCA (IKCCA). We show the strong performance of this approach both for a toy problem as well as using real world data with dramatic improvements in predictive accuracy of virtual screening over an existing methodology.

A systematic investigation of quaternary ammonium ions as asymmetric phase-transfer catalysts. Application of quantitative structure activity/selectivity relationships

Although the synthetic utility of asymmetric phase-transfer catalysis continues to expand, the number of proven catalyst types and design criteria remains limited. At the origin of this scarcity is a lack in understanding of how catalyst structural features affect the rate and enantioselectivity of phase transfer catalyzed reactions. Described in this paper is the development of quantitative structure-activity relationships (QSAR) and -selectivity relationships (QSSR) for the alkylation of a protected glycine imine with libraries of quaternary ammonium ion catalysts. Catalyst descriptors including ammonium ion accessibility, interfacial adsorption affinity, and partition coefficient were found to correlate meaningfully with catalyst activity. The physical nature of the descriptors was rationalized through differing contributions of the interfacial and extraction mechanisms to the reaction under study. The variation in the observed enantioselectivity was rationalized employing a comparative molecular field analysis (CoMFA) using both the steric and electrostatic fields of the catalysts. A qualitative analysis of the developed model reveals preferred regions for catalyst binding to afford both configurations of the alkylated product.

Recent development of anticancer therapeutics targeting Akt

The serine/threonine kinase Akt has proven to be a significant signaling target, involved in various biological functions. Because of its cardinal role in numerous cellular responses, Akt has been implicated in many human diseases, particularly cancer. It has been established that Akt is a viable and feasible target for anticancer therapeutics. Analysis of all Akt kinases reveals conserved homology for an N-terminal regulatory domain, which contains a pleckstrin-homology (PH) domain for cellular translocation, a kinase domain with serine/threonine specificity, and a C-terminal extension domain. These well defined regions have been targeted, and various approaches, including in silico methods, have been implemented to develop Akt inhibitors. In spite of unique techniques and a prolific body of knowledge surrounding Akt, no targeted Akt therapeutics have reached the market yet. Here we will highlight successes and challenges to date on the development of anticancer agents modulating the Akt pathway in recent patents as well as discuss the methods employed for this task. Special attention will be given to patents with focus on those discoveries using computer-aided drug design approaches.

Chemoinformatics as a Theoretical Chemistry Discipline

Here, chemoinformatics is considered as a theoretical chemistry discipline complementary to quantum chemistry and force-field molecular modeling. These three fields are compared with respect to molecular representation, inference mechanisms, basic concepts and application areas. A chemical space, a fundamental concept of chemoinformatics, is considered with respect to complex relations between chemical objects (graphs or descriptor vectors). Statistical Learning Theory, one of the main mathematical approaches in structure-property modeling, is briefly reviewed. Links between chemoinformatics and its "sister" fields - machine learning, chemometrics and bioinformatics are discussed.

Computer-aided drug discovery and development

Computer-aided approaches have been widely used in pharmaceutical research to improve the efficiency of the drug discovery and development pipeline. To identify and design small molecules as clinically effective therapeutics, various computational methods have been evaluated as promising strategies, depending on the purpose and systems of interest. Both ligand and structure-based drug design approaches are powerful technologies, which can be applied to virtual screening for lead identification and optimization. Here, we review the progress in this field and summarize the application of some new technologies we developed. These state-of-the-art tools have been used for the discovery and development of active agents for various diseases, in particular for cancer therapies. The described protocols are appropriate for all drug discovery stages, but expertise is still needed to perform the studies based on the targets of interest.

Mapping drug-target interaction networks

Molecular polypharmacological studies have gained more and more attention as they are important in predicting drug off-target properties and potential toxicity/side effect. The explosive growth of biomedical data provides us an opportunity to develop novel strategies to conduct such studies by analyzing molecular interaction networks. In this paper, we present an integrated web application that is implemented based on more than 5,000 drugs and 56,000 biological macromolecule structures. With efficient search of drug information (biological targets, pharmacology, side effect, etc.) and chemical similarity, molecular maps can be constructed to demonstrate the relationships among multiple drugs and receptors. In addition, receptor information can also be employed to map the interaction network. The 3D structures of available drug-receptor complexes can be visualized via our web server, and the query results will be used to identify similar structures for any given drugs as well as their cross interactions with other biological targets. Our implementation provides an efficient way to evaluate the safety and polypharmacological properties of chemical compounds.

Computational modeling of novel inhibitors targeting the Akt pleckstrin homology domain

Computational modeling continues to play an important role in novel therapeutics discovery and development. In this study, we have investigated the use of in silico approaches to develop inhibitors of the pleckstrin homology (PH) domain of AKT (protein kinase B). Various docking/scoring schemes have been evaluated, and the best combination was selected to study the system. Using this strategy, two hits were identified and their binding behaviors were investigated. Robust and predictive QSAR models were built using the k nearest neighbor (kNN) method to study their cellular permeability. Based on our in silico results, long flexible aliphatic tails were proposed to improve the Caco-2 penetration without affecting the binding mode. The modifications enhanced the AKT inhibitory activity of the compounds in cell-based assays, and increased their activity as in vivo antitumor testing.

Inhibition of ATP synthase by chlorinated adenosine analogue

8-Chloroadenosine (8-Cl-Ado) is a ribonucleoside analogue that is currently in clinical trial for chronic lymphocytic leukemia. Based on the decline in cellular ATP pool following 8-Cl-Ado treatment, we hypothesized that 8-Cl-ADP and 8-Cl-ATP may interfere with ATP synthase, a key enzyme in ATP production. Mitochondrial ATP synthase is composed of two major parts; F(O) intermembrane base and F1 domain, containing alpha and beta subunits. Crystal structures of both alpha and beta subunits that bind to the substrate, ADP, are known in tight binding (alpha(dp)beta(dp)) and loose binding (alpha(tp)beta(tp)) states. Molecular docking demonstrated that 8-Cl-ADP/8-Cl-ATP occupied similar binding modes as ADP/ATP in the tight and loose binding sites of ATP synthase, respectively, suggesting that the chlorinated nucleotide metabolites may be functional substrates and inhibitors of the enzyme. The computational predictions were consistent with our whole cell biochemical results. Oligomycin, an established pharmacological inhibitor of ATP synthase, decreased both ATP and 8-Cl-ATP formation from exogenous substrates, however, did not affect pyrimidine nucleoside analogue triphosphate accumulation. Synthesis of ATP from ADP was inhibited in cells loaded with 8-Cl-ATP. These biochemical studies are in consent with the computational modeling; in the alpha(tp)beta(tp) state 8-Cl-ATP occupies similar binding as ANP, a non-hydrolyzable ATP mimic that is a known inhibitor. Similarly, in the substrate binding site (alpha(dp)beta(dp)) 8-Cl-ATP occupies a similar position as ATP mimic ADP-BeF(3)(-). Collectively, our current work suggests that 8-Cl-ADP may serve as a substrate and the 8-Cl-ATP may be an inhibitor of ATP synthase.

The importance of discerning shape in molecular pharmacology

Shape is a fundamentally important molecular feature that often determines the fate of a compound in terms of molecular interactions with preferred and non-preferred biological targets. Complementarity of binding in small-molecule-protein, peptide-receptor, antigen-antibody and protein-protein interactions is the key to life and survival and also to targeting molecules with bioactivity. We review the application of shape in various biological systems such as substrate recognition, ligand specificity or selectivity and antibody recognition in the context of computational methods such as docking, quantitative structure-activity relationships, classification models and similarity-search algorithms. These in silico pharmacology methods have recently demonstrated the importance and applicability of determining molecular shape in drug discovery, virtual screening and predictive toxicology. The results from recently published studies show that shape and shape-based descriptors are at least as useful as other traditional molecular descriptors.

HIV-1 protease function and structure studies with the simplicial neighborhood analysis of protein packing method

The Simplicial Neighborhood Analysis of Protein Packing (SNAPP) method was used to predict the effect of mutagenesis on the enzymatic activity of the HIV-1 protease (HIVP). SNAPP relies on a four-body statistical scoring function derived from the analysis of spatially nearest neighbor residue compositional preferences in a diverse and representative subset of protein structures from the Protein Data Bank. The method was applied to the analysis of HIVP mutants with residue substitutions in the hydrophobic core as well as at the interface between the two protease monomers. Both wild-type and tethered structures were employed in the calculations. We obtained a strong correlation, with R(2) as high as 0.96, between DeltaSNAPP score (i.e., the difference in SNAPP scores between wild-type and mutant proteins) and the protease catalytic activity for tethered structures. However, a weaker but significant correlation was obtained for nontethered structures. Our analysis identified residues both in the hydrophobic core and at the dimeric interface that are very important for the protease function. This study demonstrates a potential utility of the SNAPP method for rational design of mutagenesis studies and protein engineering.

Computational analysis of ligand relationships within target families

Computational tools for the large-scale analysis and prediction of ligand-target interactions and the identification of small molecules having different selectivity profiles within target protein families complement research in chemical genetics and chemogenomics. For computational analysis and design, such tasks require a departure from the traditional focus on single targets, hit identification, and lead optimization. Recently, studies have been reported that profile compounds in silico against arrays of targets or systematically map ligand-target space. In order to identify small molecular probes that are suitable for chemical genetics applications, molecular diversity needs to be viewed in a way that partly differs from principles guiding conventional library design.

Chemogenomic approaches to rational drug design

Paradigms in drug design and discovery are changing at a significant pace. Concomitant to the sequencing of over 180 several genomes, the high-throughput miniaturization of chemical synthesis and biological evaluation of a multiple compounds on gene/protein expression and function opens the way to global drug-discovery approaches, no more focused on a single target but on an entire family of related proteins or on a full metabolic pathway. Chemogenomics is this emerging research field aimed at systematically studying the biological effect of a wide array of small molecular-weight ligands on a wide array of macromolecular targets. Since the quantity of existing data (compounds, targets and assays) and of produced information (gene/protein expression levels and binding constants) are too large for manual manipulation, information technologies play a crucial role in planning, analysing and predicting chemogenomic data. The present review will focus on predictive in silico chemogenomic approaches to foster rational drug design and derive information from the simultaneous biological evaluation of multiple compounds on multiple targets. State-of-the-art methods for navigating in either ligand or target space will be presented and concrete drug design applications will be mentioned.

eHiTS-to-VMD interface application. The search for tyrosine-tRNA ligase inhibitors

Owing to the recent development of virtual high-throughput screening (vHTS) and a vast number of compounds subjected to vHTS analyses, it has been essential to automate the processing of computational data required for the analysis and visualization of research results. Using the search for tyrosine-tRNA ligase inhibitors as an example, we present a computer application, an interface between eHiTS software for virtual high-throughput screening and VMD graphic software used to visualize calculation results.

A Cheminformatic Toolkit for Mining Biomedical Knowledge

PURPOSE

Cheminformatics can be broadly defined to encompass any activity related to the application of information technology to the study of properties, effects and uses of chemical agents. One of the most important current challenges in cheminformatics is to allow researchers to search databases of biomedical knowledge, using chemical structures as input.

MATERIALS AND METHODS

An important step towards this goal was the establishment of PubChem, an open, centralized database of small molecules accessible through the World Wide Web. While PubChem is primarily intended to serve as a repository for high throughput screening data from federally-funded screening centers and academic research laboratories, the major impact of PubChem could also reside in its ability to serve as a chemical gateway to biomedical databases such as PubMed.

CONCLUSION

This article will review cheminformatic tools that can be applied to facilitate annotation of PubChem through links to the scientific literature; to integrate PubChem with transcriptomic, proteomic, and metabolomic datasets; to incorporate results of numerical simulations of physiological systems into PubChem annotation; and ultimately, to translate data of chemical genomics screening efforts into information that will benefit biomedical researchers and physician scientists across all therapeutic areas.

Antitumor agents 252. Application of validated QSAR models to database mining: discovery of novel tylophorine derivatives as potential anticancer agents

A combined approach of validated QSAR modeling and virtual screening was successfully applied to the discovery of novel tylophrine derivatives as anticancer agents. QSAR models have been initially developed for 52 chemically diverse phenanthrine-based tylophrine derivatives (PBTs) with known experimental EC(50) using chemical topological descriptors (calculated with the MolConnZ program) and variable selection k nearest neighbor (kNN) method. Several validation protocols have been applied to achieve robust QSAR models. The original dataset was divided into multiple training and test sets, and the models were considered acceptable only if the leave-one-out cross-validated R (2) (q (2)) values were greater than 0.5 for the training sets and the correlation coefficient R (2) values were greater than 0.6 for the test sets. Furthermore, the q (2) values for the actual dataset were shown to be significantly higher than those obtained for the same dataset with randomized target properties (Y-randomization test), indicating that models were statistically significant. Ten best models were then employed to mine a commercially available ChemDiv Database (ca. 500 K compounds) resulting in 34 consensus hits with moderate to high predicted activities. Ten structurally diverse hits were experimentally tested and eight were confirmed active with the highest experimental EC(50) of 1.8 microM implying an exceptionally high hit rate (80%). The same ten models were further applied to predict EC50 for four new PBTs, and the correlation coefficient (R (2)) between the experimental and predicted EC(50) for these compounds plus eight active consensus hits was shown to be as high as 0.57. Our studies suggest that the approach combining validated QSAR modeling and virtual screening could be successfully used as a general tool for the discovery of novel biologically active compounds.

Parallel screening: a novel concept in pharmacophore modeling and virtual screening

Parallel screening comprises a novel in silico method to predict the potential biological activities of a compound by screening it with a multitude of pharmacophore models. Our aim is to provide a fast, large-scale system that allows for virtual activity profiling. In this proof of principle study, carried out with the software tools LigandScout and Catalyst, we present a model work for the application of parallel pharmacophore-based virtual screening on a set of 50 structure-based pharmacophore models built for various viral targets and 100 antiviral compounds. The latter were screened against all pharmacophore models in order to determine if their biological targets could be correctly predicted via an enrichment of corresponding pharmacophores matching these ligands. The results demonstrate that the desired enrichment, that is, successful virtual activity profiling, was achieved for approximately 90% of all input molecules. We discuss descriptors for output validation, as well as various aspects influencing the analysis of the obtained activity profiles, and the effect of the utilized search modus for screening.

A novel automated lazy learning QSAR (ALL-QSAR) approach: method development, applications, and virtual screening of chemical databases using validated ALL-QSAR models

A novel automated lazy learning quantitative structure-activity relationship (ALL-QSAR) modeling approach has been developed on the basis of the lazy learning theory. The activity of a test compound is predicted from a locally weighted linear regression model using chemical descriptors and the biological activity of the training set compounds most chemically similar to this test compound. The weights with which training set compounds are included in the regression depend on the similarity of those compounds to a test compound. We have applied the ALL-QSAR method to several experimental chemical data sets including 48 anticonvulsant agents with known ED50 values, 48 dopamine D1-receptor antagonists with known competitive binding affinities (Ki), and a Tetrahymena pyriformis data set containing 250 phenolic compounds with toxicity IGC50 values. When applied to database screening, models developed for anticonvulsant agents identified several known anticonvulsant compounds that were not only absent in the training set but highly chemically dissimilar to the training set compounds. This initial success indicates that ALL-QSAR can be further exploited as a general tool for accurate bioactivity prediction and database screening in drug design and discovery. Because of its local nature, the ALL-QSAR approach appears to be especially well-suited for the development of highly predictive models for the sparse or unevenly distributed data sets.

InSilico-Guided Target Identification of a Scaffold-Focused Library: 1,3,5-Triazepan-2,6-diones as Novel Phospholipase A2 Inhibitors

A collection of 2150 druggable active sites from the Protein Data Bank was screened by high-throughput docking to identify putative targets for five representative molecules of a combinatorial library sharing a 1,3,5-triazepan-2,6-dione scaffold. Five targets were prioritized for experimental evaluation by computing enrichment in individual protein entries among the top 2% scoring targets. Out of the five proposed proteins, secreted phospholipase A2 (sPLA2) was shown to be a true target for a panel of 1,3,5-triazepan-2,6-diones which exhibited micromolar affinities toward two human sPLA2 members.

High-throughput structure-based pharmacophore modelling as a basis for successful parallel virtual screening

In order to assess bioactivity profiles for small organic molecules we propose to use parallel pharmacophore-based virtual screening. Our aim is to provide a fast, reliable and scalable system that allows for rapid in silico activity profile prediction of virtual molecules. In this proof of principle study, carried out with the new structure-based pharmacophore modelling tool LigandScout and the high-performance database mining platform Catalyst, we present a model work for the application of parallel pharmacophore-based virtual screening on a set of 50 structure-based pharmacophore models built for various viral targets and 100 antiviral compounds. The latter were screened against all pharmacophore models in order to determine if their known biological targets could be correctly predicted via an enrichment of corresponding pharmaco-phores matching these ligands. The results demonstrate that the desired enrichment, i.e. a successful activity profiling, was achieved for approximately 90% of all input molecules. Additionally, we discuss descriptors for output validation, as well as various aspects influencing the analysis of the obtained activity profiles, and the effect of the searching mode utilized for screening. The results of the study presented here clearly indicate that pharmacophore-based parallel screening comprises a reliable in silico method to predict the potential biological activities of a compound or a compound library by screening it against a series of pharmacophore queries.