Three-dimensional quantitative structure-activity relationships of cyclo-oxygenase-2 (COX-2) inhibitors: a comparative molecular field analysis

Electron Donor-Acceptor Complex Enabled Radical Cyclization of α-Diazodifluoroethyl Sulfonium Salt with Unactivated Alkynes

An α-diazodifluoroethane sulfonium reagent was developed in this study to undergo [3 + 2] radical cyclization with unactivated alkynes to give the corresponding 3-difluoromethyl pyrazoles under blue light irradiation conditions. The key to the success of this transformation lies in the formation of an electron donor-acceptor (EDA) complex between an electron-deficient α-diazo sulfonium salt and an electron-rich triaryl amine. This study circumvents a major substrate scope limitation in polar cycloaddition reactions of existent diazodifluoroethane reagents.

Biological Activity Predictions of Ligands Based on Hybrid Molecular Fingerprinting and Ensemble Learning

The biological activity predictions of ligands are an important research direction, which can improve the efficiency and success probability of drug screening. However, the traditional prediction method has the disadvantages of complex modeling and low screening efficiency. Machine learning is considered an important research direction to solve these traditional method problems in the near future. This paper proposes a machine learning model with high predictive accuracy and stable prediction ability, namely, the back propagation neural network cross-support vector regression model (BPCSVR). By comparing multiple molecular descriptors, MACCS fingerprint and ECFP6 fingerprint were selected as inputs, and the stable prediction ability of the model was improved by integrating multiple models and correcting similar samples. We used leave-one-out cross-validation on 3038 samples from six data sets. The coefficient of determination, root mean square error, and absolute error were used as the evaluation parameters. After comparing the multiclass models, the results show that the BPCSVR model has stable prediction ability in different data sets, and the prediction accuracy is higher than other comparison models.

Viscosine as a Potent and Safe Antipyretic Agent Evaluated by Yeast-Induced Pyrexia Model and Molecular Docking Studies

The antipyretic potential of viscosine, a natural product isolated from the medicinal plant Dodonaea viscosa, was investigated using yeast-induced pyrexia rat model, and its structure-activity relationship was investigated through molecular docking analyses with the target enzymes cyclooxygenase-1 (COX-1), cyclooxygenase-2 (COX-2), and microsomal prostaglandin E synthase-1 (mPGES-1). The in vivo antipyretic experiments showed a progressive dose-dependent reduction in body temperatures of the hyperthermic test animals when injected with viscosine. Comparison of docking analyses with target enzymes showed strongest bonding interactions (binding energy -17.34 kcal/mol) of viscosine with the active-site pocket of mPGES-1. These findings suggest that viscosine shows antipyretic properties by reducing the concentration of prostaglandin E₂ in brain through its mPGES-1 inhibitory action and make it a potential lead compound for developing effective and safer antipyretic drugs for treating fever and related pathological conditions.

Rational Use of Heterogeneous Data in Quantitative Structure-Activity Relationship (QSAR) Modeling of Cyclooxygenase/Lipoxygenase Inhibitors

Numerous studies have been published in recent years with acceptable quantitative structure-activity relationship (QSAR) modeling based on heterogeneous data. In many cases, the training sets for QSAR modeling were constructed from compounds tested by different biological assays, contradicting the opinion that QSAR modeling should be based on the data measured by a single protocol. We attempted to develop approaches that help to determine how heterogeneous data should be used for the creation of QSAR models on the basis of different sets of compounds tested by different experimental methods for the same target and the same endpoint. To this end, more than 100 QSAR models for the IC50 values of ligands interacting with cyclooxygenase 1,2 (COX) and seed lipoxygenase (LOX), obtained from ChEMBL database were created using the GUSAR software. The QSAR models were tested on the external set, including 26 new thiazolidinone derivatives, which were experimentally tested for COX-1,2/LOX inhibition. The IC50 values of the derivatives varied from 89 μM to 26 μM for LOX, from 200 μM to 0.018 μM for COX-1, and from 210 μM to 1 μM for COX-2. This study showed that the accuracy of the models is dependent on the distribution of IC50 values of low activity compounds in the training sets. In the most cases, QSAR models created based on the combined training sets had advantages in comparison with QSAR models, based on a single publication. We introduced a new method of combination of quantitative data from different experimental studies based on the data of reference compounds, which was called "scaling".

Exploring the QSAR's predictive truthfulness of the novel N-tuple discrete derivative indices on benchmark datasets

Graph derivative indices (GDIs) have recently been defined over N-atoms (N = 2, 3 and 4) simultaneously, which are based on the concept of derivatives in discrete mathematics (finite difference), metaphorical to the derivative concept in classical mathematical analysis. These molecular descriptors (MDs) codify topo-chemical and topo-structural information based on the concept of the derivative of a molecular graph with respect to a given event (S) over duplex, triplex and quadruplex relations of atoms (vertices). These GDIs have been successfully applied in the description of physicochemical properties like reactivity, solubility and chemical shift, among others, and in several comparative quantitative structure activity/property relationship (QSAR/QSPR) studies. Although satisfactory results have been obtained in previous modelling studies with the aforementioned indices, it is necessary to develop new, more rigorous analysis to assess the true predictive performance of the novel structure codification. So, in the present paper, an assessment and statistical validation of the performance of these novel approaches in QSAR studies are executed, as well as a comparison with those of other QSAR procedures reported in the literature. To achieve the main aim of this research, QSARs were developed on eight chemical datasets widely used as benchmarks in the evaluation/validation of several QSAR methods and/or many different MDs (fundamentally 3D MDs). Three to seven variable QSAR models were built for each chemical dataset, according to the original dissection into training/test sets. The models were developed by using multiple linear regression (MLR) coupled with a genetic algorithm as the feature wrapper selection technique in the MobyDigs software. Each family of GDIs (for duplex, triplex and quadruplex) behaves similarly in all modelling, although there were some exceptions. However, when all families were used in combination, the results achieved were quantitatively higher than those reported by other authors in similar experiments. Comparisons with respect to external correlation coefficients (q²_ext) revealed that the models based on GDIs possess superior predictive ability in seven of the eight datasets analysed, outperforming methodologies based on similar or more complex techniques and confirming the good predictive power of the obtained models. For the q²_ext values, the non-parametric comparison revealed significantly different results to those reported so far, which demonstrated that the models based on DIVATI's indices presented the best global performance and yielded significantly better predictions than the 12 0-3D QSAR procedures used in the comparison. Therefore, GDIs are suitable for structure codification of the molecules and constitute a good alternative to build QSARs for the prediction of physicochemical, biological and environmental endpoints.

Synthesis of Pyrazole-Thiobarbituric Acid Derivatives: Antimicrobial Activity and Docking Studies

A one-pot reaction was described that results in various pyrazole-thiobarbituric acid derivatives as new pharmacophore agents. These new heterocycles were synthesized in high yields with a broad substrate scope under mild reaction conditions in water mediated by NHEt₂. The molecular structures of the synthesized compounds were assigned based on different spectroscopic techniques. The new compounds were evaluated for their antibacterial and antifungal activity. Compounds 4h and 4l were the most active compounds against C. albicans with MIC = 4 µg/L. Compound 4c exhibited the best activity against S. aureus and E. faecalis with MIC = 16 µg/L. However, compounds 4l and 4o were the most active against B. subtilis with MIC = 16 µg/L. Molecular docking studies for the final compounds and standard drugs were performed using the OpenEye program.

Extrapolative prediction using physically-based QSAR

Surflex-QMOD integrates chemical structure and activity data to produce physically-realistic models for binding affinity prediction . Here, we apply QMOD to a 3D-QSAR benchmark dataset and show broad applicability to a diverse set of targets. Testing new ligands within the QMOD model employs automated flexible molecular alignment, with the model itself defining the optimal pose for each ligand. QMOD performance was compared to that of four approaches that depended on manual alignments (CoMFA, two variations of CoMSIA, and CMF). QMOD showed comparable performance to the other methods on a challenging, but structurally limited, test set. The QMOD models were also applied to test a large and structurally diverse dataset of ligands from ChEMBL, nearly all of which were synthesized years after those used for model construction. Extrapolation across diverse chemical structures was possible because the method addresses the ligand pose problem and provides structural and geometric means to quantitatively identify ligands within a model’s applicability domain. Predictions for such ligands for the four tested targets were highly statistically significant based on rank correlation. Those molecules predicted to be highly active (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\hbox {pK}_i \ge 7.5$$\end{document}pKi≥7.5) had a mean experimental \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\hbox {pK}_i$$\end{document}pKi of 7.5, with potent and structurally novel ligands being identified by QMOD for each target.

Using Cheminformatics in Drug Discovery

This chapter illustrates how cheminformatics can be applied to designing novel compounds that are active at the primary target and have good predicted ADMET properties. Examples of various cheminformatics techniques are illustrated in the process of designing inhibitors that inhibit both cyclooxygenase isoforms but are more potent toward COX-2. The first step in the process is to create a knowledge database of cyclooxygenase inhibitors in the public domain. This data was analyzed to find activity cliffs - small structural changes that result in drastic changes in potency. Additional cyclooxygenase potency and selectivity trends were obtained using matched molecular pair analysis. QSAR models were then developed to predict cyclooxygenase potency and selectivity. Next, computational algorithms were used to generate novel scaffolds starting from known cyclooxygenase inhibitors. Nine virtual libraries containing 240 compounds each were constructed. Predictions from the cyclooxygenase QSAR models were used to eliminate molecules with undesirable potency or selectivity. Additionally, the compounds were screened in silico for undesirable ADMET properties, e.g., low solubility, permeability, metabolic stability, or high toxicity, using a liability scoring system known as ADMET Risk™. Eight synthetic candidates were identified from this process after incorporating knowledge gained from activity cliff analysis. Four of the compounds were synthesized and tested to measure their COX-1 and COX-2 IC(50) values as well as several ADME properties. The best compound, SLP0020, had a COX-1 IC(50) of 770 nM and COX-2 IC(50) of 130 nM.

Ligand biological activity predictions using fingerprint-based artificial neural networks (FANN-QSAR)

This chapter focuses on the fingerprint-based artificial neural networks QSAR (FANN-QSAR) approach to predict biological activities of structurally diverse compounds. Three types of fingerprints, namely ECFP6, FP2, and MACCS, were used as inputs to train the FANN-QSAR models. The results were benchmarked against known 2D and 3D QSAR methods, and the derived models were used to predict cannabinoid (CB) ligand binding activities as a case study. In addition, the FANN-QSAR model was used as a virtual screening tool to search a large NCI compound database for lead cannabinoid compounds. We discovered several compounds with good CB2 binding affinities ranging from 6.70 nM to 3.75 μM. The studies proved that the FANN-QSAR method is a useful approach to predict bioactivities or properties of ligands and to find novel lead compounds for drug discovery research.

Benchmarking study of parameter variation when using signature fingerprints together with support vector machines

QSAR modeling using molecular signatures and support vector machines with a radial basis function is increasingly used for virtual screening in the drug discovery field. This method has three free parameters: C, γ, and signature height. C is a penalty parameter that limits overfitting, γ controls the width of the radial basis function kernel, and the signature height determines how much of the molecule is described by each atom signature. Determination of optimal values for these parameters is time-consuming. Good default values could therefore save considerable computational cost. The goal of this project was to investigate whether such default values could be found by using seven public QSAR data sets spanning a wide range of end points and using both a bit version and a count version of the molecular signatures. On the basis of the experiments performed, we recommend a parameter set of heights 0 to 2 for the count version of the signature fingerprints and heights 0 to 3 for the bit version. These are in combination with a support vector machine using C in the range of 1 to 100 and γ in the range of 0.001 to 0.1. When data sets are small or longer run times are not a problem, then there is reason to consider the addition of height 3 to the count fingerprint and a wider grid search. However, marked improvements should not be expected.

FT-IR, FT-Raman spectra, and DFT computations of the vibrational spectra and molecular geometry of chlorzoxazone

Far-IR, mid-IR, and FT-Raman spectra of the chlorzoxazone (CZX) were recorded. The observed vibrational wavenumbers were analyzed and assigned to different normal modes of vibration of the molecule. Density functional calculations were performed to support wavenumber assignments of the observed bands. The equilibrium geometry and harmonic wavenumbers of CZX were calculated by the DFT B3LYP method. All tautomeric forms and dimer form of CZX were determined and optimized. Additionally, experimental FT-IR spectrum in ethanol solution was recorded and compared with solid phase experimental data for the first time. The combination of the DFT B3LYP with polarized continuum model (PCM) was employed to characterize the solvent effects in ethanol solution.

Using matched molecular series as a predictive tool to optimize biological activity

A matched molecular series is the general form of a matched molecular pair and refers to a set of two or more molecules with the same scaffold but different R groups at the same position. We describe Matsy, a knowledge-based method that uses matched series to predict R groups likely to improve activity given an observed activity order for some R groups. We compare the Matsy predictions based on activity data from ChEMBLdb to the recommendations of the Topliss tree and carry out a large scale retrospective test to measure performance. We show that the basis for predictive success is preferred orders in matched series and that this preference is stronger for longer series. The Matsy algorithm allows medicinal chemists to integrate activity trends from diverse medicinal chemistry programs and apply them to problems of interest as a Topliss-like recommendation or as a hypothesis generator to aid compound design.

Prediction of new bioactive molecules using a Bayesian belief network

Natural products and synthetic compounds are a valuable source of new small molecules leading to novel drugs to cure diseases. However identifying new biologically active small molecules is still a challenge. In this paper, we introduce a new activity prediction approach using Bayesian belief network for classification (BBNC). The roots of the network are the fragments composing a compound. The leaves are, on one side, the activities to predict and, on another side, the unknown compound. The activities are represented by sets of known compounds, and sets of inactive compounds are also used. We calculated a similarity between an unknown compound and each activity class. The more similar activity is assigned to the unknown compound. We applied this new approach on eight well-known data sets extracted from the literature and compared its performance to three classical machine learning algorithms. Experiments showed that BBNC provides interesting prediction rates (from 79% accuracy for high diverse data sets to 99% for low diverse ones) with a short time calculation. Experiments also showed that BBNC is particularly effective for homogeneous data sets but has been found to perform less well with structurally heterogeneous sets. However, it is important to stress that we believe that using several approaches whenever possible for activity prediction can often give a broader understanding of the data than using only one approach alone. Thus, BBNC is a useful addition to the computational chemist's toolbox.

Molecular structure and spectroscopic analysis of 1,4-Bis(1-methyl-2-benzimidazolyl)benzene; XRD, FT-IR, dispersive-Raman, NMR and DFT studies

This study reports the structural characterization of a bis-benzimidazole derivative, 1,4-Bis(1-methyl-2-benzimidazolyl)benzene (BMBB), by using spectroscopic and quantum chemical methods. The BMBB molecule was synthesized under microwave conditions and was characterized by using single-crystal X-ray diffraction, FT-IR, dispersive Raman and NMR spectroscopies. The potential energy surface scan study was carried out for the conformation of the theoretical structure. Quantum chemical calculations of relative energies, molecular geometry, vibrational wavenumbers, frontier molecular orbitals, atomic charges and gauge including atomic orbital (GIAO) (1)H and (13)C-NMR chemical shifts of the compound were carried out by using density functional method (DFT) at B3LYP/6-311++G(d,p) theory level. The complete assignments of the vibrational modes were performed with DFT calculations combined with scaled quantum mechanics force field (SQMFF) methodology. A satisfactory consistency between the experimental and theoretical findings was obtained. On account of the relative energies, population analysis and XRD results, the most stable conformational form of the molecule was also determined.

Molecular fingerprint-based artificial neural networks QSAR for ligand biological activity predictions

In this manuscript, we have reported a novel 2D fingerprint-based artificial neural network QSAR (FANN-QSAR) method in order to effectively predict biological activities of structurally diverse chemical ligands. Three different types of fingerprints, namely, ECFP6, FP2 and MACCS, were used in FANN-QSAR algorithm development, and FANN-QSAR models were compared to known 3D and 2D QSAR methods using five data sets previously reported. In addition, the derived models were used to predict GPCR cannabinoid ligand binding affinities using our manually curated cannabinoid ligand database containing 1699 structurally diverse compounds with reported cannabinoid receptor subtype CB(2) activities. To demonstrate its useful applications, the established FANN-QSAR algorithm was used as a virtual screening tool to search a large NCI compound database for lead cannabinoid compounds, and we have discovered several compounds with good CB(2) binding affinities ranging from 6.70 nM to 3.75 μM. To the best of our knowledge, this is the first report for a fingerprint-based neural network approach validated with a successful virtual screening application in identifying lead compounds. The studies proved that the FANN-QSAR method is a useful approach to predict bioactivities or properties of ligands and to find novel lead compounds for drug discovery research.

FT-IR and FT-Raman spectroscopic and DFT theoretical studies on 4-azabenzimidazole

The Becke, three-parameter, Lee-Yang-Parr exchange-correlation functional have been used to study the geometry, relative energy, frequency and intensity of the vibrational bands of the 4-azabenzimidazole (4AB) tautomers and the most stable tautomer's homodimers. FT-IR and Raman spectra of the 4AB have been measured in the regions 4000-100 cm(-1) and 3500-100 cm(-1), respectively for the first time. The stability of 4AB tautomers were reported. All vibrational frequencies assigned in detail with the help of total energy distribution (TED) and isotopic shifts. The energy and atomic charges were discussed. NH⋯N type intermolecular hydrogen bonding interactions were suggested for 4AB dimeric forms. 1H and 13C NMR properties have been calculated for the most stable two tautomeric forms using the gauge independent atomic orbital (GIAO) method.

Rank order entropy: why one metric is not enough

The use of Quantitative Structure-Activity Relationship models to address problems in drug discovery has a mixed history, generally resulting from the misapplication of QSAR models that were either poorly constructed or used outside of their domains of applicability. This situation has motivated the development of a variety of model performance metrics (r(2), PRESS r(2), F-tests, etc.) designed to increase user confidence in the validity of QSAR predictions. In a typical workflow scenario, QSAR models are created and validated on training sets of molecules using metrics such as Leave-One-Out or many-fold cross-validation methods that attempt to assess their internal consistency. However, few current validation methods are designed to directly address the stability of QSAR predictions in response to changes in the information content of the training set. Since the main purpose of QSAR is to quickly and accurately estimate a property of interest for an untested set of molecules, it makes sense to have a means at hand to correctly set user expectations of model performance. In fact, the numerical value of a molecular prediction is often less important to the end user than knowing the rank order of that set of molecules according to their predicted end point values. Consequently, a means for characterizing the stability of predicted rank order is an important component of predictive QSAR. Unfortunately, none of the many validation metrics currently available directly measure the stability of rank order prediction, making the development of an additional metric that can quantify model stability a high priority. To address this need, this work examines the stabilities of QSAR rank order models created from representative data sets, descriptor sets, and modeling methods that were then assessed using Kendall Tau as a rank order metric, upon which the Shannon entropy was evaluated as a means of quantifying rank-order stability. Random removal of data from the training set, also known as Data Truncation Analysis (DTA), was used as a means for systematically reducing the information content of each training set while examining both rank order performance and rank order stability in the face of training set data loss. The premise for DTA ROE model evaluation is that the response of a model to incremental loss of training information will be indicative of the quality and sufficiency of its training set, learning method, and descriptor types to cover a particular domain of applicability. This process is termed a "rank order entropy" evaluation or ROE. By analogy with information theory, an unstable rank order model displays a high level of implicit entropy, while a QSAR rank order model which remains nearly unchanged during training set reductions would show low entropy. In this work, the ROE metric was applied to 71 data sets of different sizes and was found to reveal more information about the behavior of the models than traditional metrics alone. Stable, or consistently performing models, did not necessarily predict rank order well. Models that performed well in rank order did not necessarily perform well in traditional metrics. In the end, it was shown that ROE metrics suggested that some QSAR models that are typically used should be discarded. ROE evaluation helps to discern which combinations of data set, descriptor set, and modeling methods lead to usable models in prioritization schemes and provides confidence in the use of a particular model within a specific domain of applicability.

Using kernel alignment to select features of molecular descriptors in a QSAR study

Quantitative structure-activity relationships (QSARs) correlate biological activities of chemical compounds with their physicochemical descriptors. By modeling the observed relationship seen between molecular descriptors and their corresponding biological activities, we may predict the behavior of other molecules with similar descriptors. In QSAR studies, it has been shown that the quality of the prediction model strongly depends on the selected features within molecular descriptors. Thus, methods capable of automatic selection of relevant features are very desirable. In this paper, we present a new feature selection algorithm for a QSAR study based on kernel alignment which has been used as a measure of similarity between two kernel functions. In our algorithm, we deploy kernel alignment as an evaluation tool, using recursive feature elimination to compute a molecular descriptor containing the most important features needed for a classification application. Empirical results show that the algorithm works well for the computation of descriptors for various applications involving different QSAR data sets. The prediction accuracies are substantially increased and are comparable to those from earlier studies.

Recent methodologies toward the synthesis of valdecoxib: a potential 3,4-diarylisoxazolyl COX-II inhibitor

Non-steroidal anti-inflammatory drugs are widely used therapeutic agents in the treatment of inflammation, pain and fever. Cyclooxygenase catalyzes the initial step of biotransformation of arachidonic acid to prostanoids, and exist as three distinct isozymes; COX-I, COX-II and COX-III. Selective COX-II inhibitors are a class of potential anti-inflammatory, analgesic, and antipyretic drugs with reduced gastrointestinal (GI) side effects compared to nonselective inhibitors. 3,4-Diarylisoxazole scaffold is recurrently found in a wide variety of NSAIDs, protein kinase inhibitors, hypertensive agents, and estrogen receptor (ER) modulators. In the present review, we document on the recent synthetic strategies of 3,4-diarylisoxazolyl scaffolds of valdecoxib and its relevant structural analogues.

Modeling the binding modes of stilbene analogs to cyclooxygenase-2: a molecular docking study

Stilbene analogs are a new class of anti-inflammatory compounds that effectively inhibit COX-2, which is the major target in the treatment of inflammation and pain. In this study, docking simulations were conducted using AutoDock 4 software that focused on the binding of this class of compounds to COX-2 protein. Our aim was to better understand the structural and chemical features responsible for the recognition mechanism of these compounds, and to explore their binding modes of interaction at the active site by comparing them with COX-2 co-crystallized with SC-558. The docking results allowed us to provide a plausible explanation for the different binding affinities observed experimentally. These results show that important conserved residues, in particular Arg513, Phe518, Trp387, Leu352, Leu531 and Arg120, could be essential for the binding of the ligands to COX-2 protein. The quality of the docking model was estimated based on the binding energies of the studied compounds. A good correlation was obtained between experimental logAr values and the predicted binding energies of the studied compounds.

A comparison of different electrostatic potentials on prediction accuracy in CoMFA and CoMSIA studies

Computational chemistry is playing an increasingly important role in drug design and discovery, structural biology, and quantitative structure-activity relationship (QSAR) studies. For QSAR work, selecting an appropriate and accurate method to assign the electrostatic potentials of each atom in a molecule is a critical first step. So far several commonly used methods are available to assign charges. However, no systematic comparison of the effects of electrostatic potentials on QSAR quality has been made. In this study, twelve semi-empirical and empirical charge-assigning methods, AM1, AM1-BCC, CFF, Del-Re, Formal, Gasteiger, Gasteiger-Hückel, Hückel, MMFF, PRODRG, Pullman, and VC2003 charges, have been compared for their performances in CoMFA and CoMSIA modeling using several standard datasets. Some charge assignment models, such as Del-Re, PRODRG, and Pullman, are limited to specific atom and bond types, and, therefore, were excluded from this study. Among the remaining nine methods, the Gasteiger-Hückel charge, though commonly used, performed poorly in prediction accuracy. The AM1-BCC method was better than most charge-assigning methods based on prediction accuracy, though it was not successful in yielding overall higher cross-validation correlation coefficient (q(2)) values than others. The CFF charge model worked the best in prediction accuracy when q(2) was used as the evaluation criterion. The results presented should help the selection of electrostatic potential models in CoMFA and CoMSIA studies.

Phenylacetic acid regioisomers possessing a N-difluoromethyl-1,2-dihydropyrid-2-one pharmacophore: evaluation as dual inhibitors of cyclooxygenases and 5-lipoxygenase with anti-inflammatory activity

A novel class of phenylacetic acid regioisomers possessing a N-difluoromethyl-1,2-dihydropyrid-2-one pharmacophore attached to its C-2, C-3 or C-4 position was designed for evaluation as anti-inflammatory (AI) agents. A number of compounds exhibited a combination of potent in vitro cyclooxygenase-2 (COX-2) and 5-lipoxygenase (5-LOX) inhibitory activities. 2-(1-Difluoromethyl-2-oxo-1,2-dihydropyridin-4-yl)phenylacetic acid (9a) exerted the most potent AI activity among this group of compounds. Molecular modeling studies showed that the N-difluoromethyl-1,2-dihydropyridin-2-one moiety present in 9a inserts into the secondary pocket present in COX-2 to confer COX-2 selectivity, and that the N-difluoromethyl-1,2-dihydropyrid-2-one group (9a) binds close to the region of the 15-LOX enzyme containing catalytic iron (His361, His366). Accordingly, the N-difluoromethyl-1,2-dihyrdopyrid-2-one moiety possesses properties that make it an attractive pharmacophore suitable for the design of dual COX-2/5-LOX inhibitory AI drugs.

Optimal assignment methods for ligand-based virtual screening

BACKGROUND

Ligand-based virtual screening experiments are an important task in the early drug discovery stage. An ambitious aim in each experiment is to disclose active structures based on new scaffolds. To perform these "scaffold-hoppings" for individual problems and targets, a plethora of different similarity methods based on diverse techniques were published in the last years. The optimal assignment approach on molecular graphs, a successful method in the field of quantitative structure-activity relationships, has not been tested as a ligand-based virtual screening method so far.

RESULTS

We evaluated two already published and two new optimal assignment methods on various data sets. To emphasize the "scaffold-hopping" ability, we used the information of chemotype clustering analyses in our evaluation metrics. Comparisons with literature results show an improved early recognition performance and comparable results over the complete data set. A new method based on two different assignment steps shows an increased "scaffold-hopping" behavior together with a good early recognition performance.

CONCLUSION

The presented methods show a good combination of chemotype discovery and enrichment of active structures. Additionally, the optimal assignment on molecular graphs has the advantage to investigate and interpret the mappings, allowing precise modifications of internal parameters of the similarity measure for specific targets. All methods have low computation times which make them applicable to screen large data sets.

DPRESS: Localizing estimates of predictive uncertainty

BACKGROUND

The need to have a quantitative estimate of the uncertainty of prediction for QSAR models is steadily increasing, in part because such predictions are being widely distributed as tabulated values disconnected from the models used to generate them. Classical statistical theory assumes that the error in the population being modeled is independent and identically distributed (IID), but this is often not actually the case. Such inhomogeneous error (heteroskedasticity) can be addressed by providing an individualized estimate of predictive uncertainty for each particular new object u: the standard error of prediction su can be estimated as the non-cross-validated error st* for the closest object t* in the training set adjusted for its separation d from u in the descriptor space relative to the size of the training set.The predictive uncertainty factor gammat* is obtained by distributing the internal predictive error sum of squares across objects in the training set based on the distances between them, hence the acronym: Distributed PRedictive Error Sum of Squares (DPRESS). Note that st* and gammat*are characteristic of each training set compound contributing to the model of interest.

RESULTS

The method was applied to partial least-squares models built using 2D (molecular hologram) or 3D (molecular field) descriptors applied to mid-sized training sets (N = 75) drawn from a large (N = 304), well-characterized pool of cyclooxygenase inhibitors. The observed variation in predictive error for the external 229 compound test sets was compared with the uncertainty estimates from DPRESS. Good qualitative and quantitative agreement was seen between the distributions of predictive error observed and those predicted using DPRESS. Inclusion of the distance-dependent term was essential to getting good agreement between the estimated uncertainties and the observed distributions of predictive error. The uncertainty estimates derived by DPRESS were conservative even when the training set was biased, but not excessively so.

CONCLUSION

DPRESS is a straightforward and powerful way to reliably estimate individual predictive uncertainties for compounds outside the training set based on their distance to the training set and the internal predictive uncertainty associated with its nearest neighbor in that set. It represents a sample-based, a posteriori approach to defining applicability domains in terms of localized uncertainty.

Atomic local neighborhood flexibility incorporation into a structured similarity measure for QSAR

In this work, we introduce a new method to regard the geometry in a structural similarity measure by approximating the conformational space of a molecule. Our idea is to break down the molecular conformation into the local conformations of neighbor atoms with respect to core atoms. This local geometry can be implicitly accessed by the trajectories of the neighboring atoms, which are emerge by rotatable bonds. In our approach, the physicochemical atomic similarity, which can be used in structured similarity measures, is augmented by a local flexibility similarity, which gives a rough estimate of the similarity of the local conformational space. We incorporated this new type of encoding the flexibility into the optimal assignment molecular similarity approach, which can be used as a pseudokernel in support vector machines. The impact of the local flexibility was evaluated on several published QSAR data sets. This lead to an improvement of the model quality on 9 out of 10 data sets compared to the unmodified optimal assignment kernel.

A constructive approach for discovering new drug leads: Using a kernel methodology for the inverse-QSAR problem

Background

The inverse-QSAR problem seeks to find a new molecular descriptor from which one can recover the structure of a molecule that possess a desired activity or property. Surprisingly, there are very few papers providing solutions to this problem. It is a difficult problem because the molecular descriptors involved with the inverse-QSAR algorithm must adequately address the forward QSAR problem for a given biological activity if the subsequent recovery phase is to be meaningful. In addition, one should be able to construct a feasible molecule from such a descriptor. The difficulty of recovering the molecule from its descriptor is the major limitation of most inverse-QSAR methods.

Results

In this paper, we describe the reversibility of our previously reported descriptor, the vector space model molecular descriptor (VSMMD) based on a vector space model that is suitable for kernel studies in QSAR modeling. Our inverse-QSAR approach can be described using five steps: (1) generate the VSMMD for the compounds in the training set; (2) map the VSMMD in the input space to the kernel feature space using an appropriate kernel function; (3) design or generate a new point in the kernel feature space using a kernel feature space algorithm; (4) map the feature space point back to the input space of descriptors using a pre-image approximation algorithm; (5) build the molecular structure template using our VSMMD molecule recovery algorithm.

Conclusion

The empirical results reported in this paper show that our strategy of using kernel methodology for an inverse-Quantitative Structure-Activity Relationship is sufficiently powerful to find a meaningful solution for practical problems.

Electronic supplementary material

The online version of this article (doi:10.1186/1758-2946-1-4) contains supplementary material, which is available to authorized users.

Synthesis of celecoxib analogues possessing a N-difluoromethyl-1,2-dihydropyrid-2-one 5-lipoxygenase pharmacophore: biological evaluation as dual inhibitors of cyclooxygenases and 5-lipoxygenase with anti-inflammatory activity

A novel class of 1-(4-methanesulfonylphenyl and 4-aminosulfonylphenyl)-5-[4-(1-difluoromethyl-1,2-dihydropyrid-2-one)]-3-trifluoromethyl-1H-pyrazole hybrid cyclooxygenase-2 (COX-2)/5-lipoxygenase (5-LOX) inhibitory anti-inflammatory agents was designed. Replacement of the tolyl ring present in celecoxib by the N-difluoromethyl-1,2-dihydropyrid-2-one moiety provided compounds showing dual selective COX-2/5-LOX inhibitory activities. 1-(4-Aminosulfonylphenyl)-5-[4-(1-difluoromethyl-1,2-dihydropyrid-2-one)]-3-trifluoromethyl-1H-pyrazole exhibited good anti-inflammatory (AI) activity (ED(50) = 27.7 mg/kg po) that compares favorably with the reference drugs celecoxib (ED(50) = 10.8 mg/kg po) and ibuprofen (ED(50) = 67.4 mg/kg po). The N-difluoromethyl-1,2-dihydropyridin-2-one moiety provides a novel 5-LOX pharmacophore for the design of cyclic hydroxamic mimetics for exploitation in the development of COX-2/5-LOX inhibitory AI drugs.

A ligand's-eye view of protein binding

Docking tools created for structure-based design and virtual screening have also been used to automate ligand alignment for comparative molecular field analysis (CoMFA). Models based on such alignments have been compared with those obtained based solely on shared ligand substructures, but such comparisons have generally failed to distinguish between conformational specification (alignment in the internal coordinate space) and embedding in a shared external frame of reference (Cartesian alignment). Here, large sets of inhibitors were docked into two cyclooxygenase and two reverse transcriptase crystal structures, and the poses generated were evaluated in terms of the CoMFA models they produced. Realigning the conformers obtained by docking by rigid-body rotation and translation to overlay their common substructures improved model statistics and interpretability, provided the protein structure used for docking was reasonably appropriate to the ligands being considered.