Antreas Afantitis

Abstract: A novel short-step methodology for the synthesis in good yields of functionalized coumarins has been developed starting from an activated precursor, the N-hydroxysuccinimide ester of O-acetylsalicylic acid. The procedure is based on a tandem C-acylation-cyclization process under mild reaction conditions. The structure of 3-methoxycarbonyl-4-hydroxy coumarin has been established by X-ray diffraction analysis and its geometry was compared with optimized parameters by means of DFT calculations.

Abstract: In this work we have developed an in silico model to predict the inhibition of β-amyloid aggregation by small organic molecules. In particular we have explored the inhibitory activity of a series of 62 N-phenylanthranilic acids using Kohonen maps and Counterpropagation Artificial Neural Networks. The effects of various structural modifications on biological activity are investigated and novel structures are designed using the developed in silico model. More specifically a search for optimized pharmacophore patterns by insertions, substitutions, and ring fusions of pharmacophoric substituents of the main building block scaffolds is described. The detection of the domain of applicability defines compounds whose estimations can be accepted with confidence.

Abstract: Virtual Screening (VS) has experienced increased attention into the recent years due to the large datasets made available, the development of advanced VS techniques and the encouraging fact that VS has contributed to the discovery of several compounds that have either reached the market or entered clinical trials. Hepatitis C Virus (HCV) nonstructural protein 5B (NS5B) has become an attractive target for the development of antiviral drugs and many small molecules have been explored as possible HCV NS5B inhibitors. In parallel with experimental practices, VS can serve as a valuable tool in the identification of novel effective inhibitors. Different techniques and workflows have been reported in literature with the goal to prioritize possible potent hits. In this context, different virtual screening strategies have been deployed for the identification of novel Hepatitis C Virus (HCV) inhibitors. This work reviews recent applications of virtual screening in an effort to identify novel potent HCV inhibitors.

Abstract: ABSTRACT: OpenTox provides an interoperable, standards-based Framework for the support of predictive toxicology data management, algorithms, modelling, validation and reporting. It is relevant to satisfying the chemical safety assessment requirements of the REACH legislation as it supports access to experimental data, (Quantitative) Structure-Activity Relationship models, and toxicological information through an integrating platform that adheres to regulatory requirements and OECD validation principles. Initial research defined the essential components of the Framework including the approach to data access, schema and management, use of controlled vocabularies and ontologies, architecture, web service and communications protocols, and selection and integration of algorithms for predictive modelling. OpenTox provides end-user oriented tools to non-computational specialists, risk assessors, and toxicological experts in addition to Application Programming Interfaces (APIs) for developers of new applications. OpenTox actively supports public standards for data representation, interfaces, vocabularies and ontologies, Open Source approaches to core platform components, and community-based collaboration approaches, so as to progress system interoperability goals.The OpenTox Framework includes APIs and services for compounds, datasets, features, algorithms, models, ontologies, tasks, validation, and reporting which may be combined into multiple applications satisfying a variety of different user needs. OpenTox applications are based on a set of distributed, interoperable OpenTox API-compliant REST web services. The OpenTox approach to ontology allows for efficient mapping of complementary data coming from different datasets into a unifying structure having a shared terminology and representation.Two initial OpenTox applications are presented as an illustration of the potential impact of OpenTox for high-quality and consistent structure-activity relationship modelling of REACH-relevant endpoints: ToxPredict which predicts and reports on toxicities for endpoints for an input chemical structure, and ToxCreate which builds and validates a predictive toxicity model based on an input toxicology dataset. Because of the extensible nature of the standardised Framework design, barriers of interoperability between applications and content are removed, as the user may combine data, models and validation from multiple sources in a dependable and time-effective way.

Abstract: A novel QSAR workflow is constructed that combines MLR with LS-SVM classification techniques for the identification of quinazolinone analogs as "active" or "non-active" CXCR3 antagonists. The accuracy of the LS-SVM classification technique for the training set and test was 100% and 90%, respectively. For the "active" analogs a validated MLR QSAR model estimates accurately their I-IP10 IC(50) inhibition values. The accuracy of the QSAR model (R (2) = 0.80) is illustrated using various evaluation techniques, such as leave-one-out procedure [Formula: see text] and validation through an external test set [Formula: see text]. The key conclusion of this study is that the selected molecular descriptors, Highest Occupied Molecular Orbital energy (HOMO), Principal Moment of Inertia along X and Y axes PMIX and PMIZ, Polar Surface Area (PSA), Presence of triple bond (PTrplBnd), and Kier shape descriptor ((1) kappa), demonstrate discriminatory and pharmacophore abilities.

Abstract: In this study, quantitative structure-activity/property models are developed for modeling and predicting both MEK inhibitory activity and oral bioavailability of novel isothiazole-4-carboxamidines. The models developed are thoroughly discussed to identify the key components that influence the inhibitory activity and oral bioavailability of the selected compounds. These selected descriptors serve as a first guideline for the design of novel and potent MEK inhibitors with desired ADME properties.

Abstract: A linear quantitative structure-activity relationship (QSAR) model is presented for modeling and predicting the inhibition of CXCR3 receptor. The model was produced by using the multiple linear regression (MLR) technique on a database that consists of 32 recently discovered 4-N-aryl-[1,4] diazepane ureas. The key conclusion of this study is that 3k, ChiInf8, ChiInf0, AtomCompTotal and ClogP affect significantly the inhibition of CXCR3 receptor by diazepane ureas. The selected physicochemical descriptors serve as a first guideline for the design of novel and potent antagonists of CXCR3.

Abstract: In the present work a series of novel coumarin-3-carboxamides and their hybrids with the alpha-lipoic acid were designed, synthesized and tested as potent antioxidant and anti-inflammatory agents. The new compounds were evaluated for their antioxidant activity, their activity to inhibit in vitro lipoxygenase and their in vivo anti-inflammatory activity. In general, the derivatives were generally found to present antioxidant and anti-inflammatory activities. Discussion is made based on the results for the structure-activity relationships in order to define the structural features required for activity.

Abstract: A linear Quantitative Structure-Activity Relationship (QSAR) is developed in this work for modeling and predicting HDAC inhibition by 5-pyridin-2-yl-thiophene-2-hydroxamic acids. In particular, a five-variable model is produced by using the Multiple Linear Regression (MLR) technique and the Elimination Selection-Stepwise Regression Method (ES-SWR) on a database that consists of 58 recently discovered 5-pyridin-2-yl-thiophene-2-hydroxamic acids and 69 descriptors. The physical meaning of the selected descriptors is discussed in detail. The validity of the proposed MLR model is established using the following techniques: cross validation, validation through an external test set and Y-randomization. Furthermore, the domain of applicability which indicates the area of reliable predictions is defined. Based on the produced model, an in silico-screening study explores novel structural patterns and suggests new potent lead compounds.

Abstract: A novel QSPR model is developed and evaluated for the prediction of diamagnetic susceptibility. The model was produced using the Multiple Linear Regression (MLR) technique on a database that consists of 406 organic compounds involving a diverse set of chemical structures. The accuracy of the QSPR model (R2¼0.88) is illustratedusing various evaluation techniques, such as leave-one-out procedure (Q2¼0.87) andvalid ation through an external test set (R2 pred¼0.89). The study leads to the conclusion that three physical � topological descriptors affect significantly the diamagnetic susceptibility: Polar Surface Area (PSAr), Principal Moment of Inertia X (PMIX), andDiameter (Diam).

Abstract: This paper presents the results of a ligand-based virtual screening optimized procedure on 98 compounds which have been recently evaluated as inhibitors of genotype 1 HCV polymerase. First, quantitative structure-activity patterns are investigated for the selected compounds and then structural modifications are proposed to afford novel active patterns. An accurate and reliable QSAR model involving five descriptors that is able to predict successfully the HCV inhibitory potency against genotype 1 HCV polymerase is presented. Furthermore, the effects of various structural modifications on biological activity are investigated and biological activities of novel structures are estimated using the developed QSAR model. More specifically a search for optimized pharmacophore patterns by insertions, substitutions, and ring fusions of pharmacophoric substituents of the main building block scaffolds is described. The detection of the domain of applicability defines compounds whose estimations can be accepted with confidence.

Abstract: In this study, we present a new model that has been developed for the prediction of theta (lower critical solution temperature) using a database of 169 data points that include 12 polymers and 67 solvents. For the characterization of polymer and solvent molecules, a number of molecular descriptors (topological, physicochemical, steric and electronic) were examined. The best subset of descriptors was selected using the elimination selection-stepwise regression method. Multiple linear regression (MLR) served as the statistical tool to explore the potential correlation among the molecular descriptors and the experimental data. The prediction accuracy of the MLR model was tested using the leave-one-out cross-validation procedure, validation through an external test set and the Y-randomization evaluation technique. The domain of applicability was finally determined to identify the reliable predictions.

Abstract: A series of N-substituted-quinolinone-3-aminoamides and their hybrids containing the alpha-lipoic acid functionality were designed and synthesized as potential bifunctional agents combining antioxidant and anti-inflammatory activity. The new compounds were evaluated for their antioxidant activity and for their ability to inhibit in vitro lipoxygenase as well as for their anti-inflammatory activity in vivo. In general, the derivatives were found to be potent antioxidant or anti-inflammatory agents. The results are discussed in terms of structure-activity relationships and an attempt is made to define the structural features required for activity.

Abstract: This paper presents the results of an optimization study on biaryl piperidine and 4-amino-2-biarylurea MCH1 receptor antagonists, which was accomplished by using quantitative-structure activity relationships (QSARs), classification and virtual screening techniques. First, a linear QSAR model was developed using Multiple Linear Regression (MLR) Analysis, while the Elimination Selection-Stepwise Regression (ES-SWR) method was adopted for selecting the most suitable input variables. The predictive activity of the model was evaluated using an external validation set and the Y-randomization technique. Based on the selected descriptors, the Support Vector Machines (SVM) classification technique was utilized to classify data into two categories: "actives" or "non-actives". Several attempts were made to optimize the scaffold of most potent compounds by inducing various structural modifications. Potential derivatives with improved activities were identified, as they were classified "actives" by the SVM classifier. Their activities were estimated using the produced MLR model. A detailed analysis on the model applicability domain defined the compounds, whose estimations can be accepted with confidence.

Abstract: A linear quantitative structure activity relationship model is obtained using Multiple Linear Regression (MLR) analysis as applied to a series of 49 dipeptidyl aspartyl fluoromethylketone derivatives with inhibitory activity of the caspase enzyme. For the selection of the best descriptors, the elimination selection stepwise regression method is utilized. The accuracy of the proposedMLR model is illustratedusing the following evaluation techniques: cross validation, validation through an external test set, and Y-randomization. Furthermore, the domain of applicability which indicates the area of reliable predictions is defined.

Abstract: A neural network methodology based on the radial basis function (RBF) architecture is introduced in order to establish quantitative structure-toxicity relationship models for the prediction of toxicity. The dataset used consists of 221 phenols and their corresponding toxicity values to Tetrahymena pyriformis. Physicochemical parameters and molecular descriptors are used to provide input information to the models. The performance and predictive abilities of the RBF models are compared to standard multiple linear regression (MLR) models. The leave-one-out cross validation procedure and validation through an external test set produce statistically significant R2 and RMS values for the RBF models, which prove considerably more accurate than the MLR models. [Figure: see text].

Abstract: A linear quantitative structure-activity relationship has been developed for a series of para-substituted aromatic sulfonamides by using topological index methodologies. The compounds were studied for their carbonic anhydrase II (CAII) inhibitory activity. A large series of topological indices were calculated and the stepwise regression method was used to derive the most significant model. Very good results were obtained using multi-parametric regressions and showed that the information approach used in the present work is quite useful for modeling carbonic anhydrase inhibition.

Abstract: This work introduces a neural network methodology for developing QSTR predictors of toxicity to Vibrio fischeri. The method adopts the Radial Basis Function (RBF) architecture and the fuzzy means training strategy, which is fast and repetitive, in contrast to most traditional training techniques. The data set that was utilized consisted of 39 organic compounds and their corresponding toxicity values to Vibrio fischeri, while lipophilicity, equalized electronegativity and one topological index were used to provide input information to the models. The performance and predictive ability of the RBF model were illustrated through external validation and various statistical tests. The proposed methodology can be used to successfully model toxicity to Vibrio fischeri for a heterogeneous set of compounds.

Abstract: In this work, a linear quantitative structure�property relationship (QSPR) model is presented for the prediction of intrinsic viscosity in polymer solutions. The model was produced by using the multiple linear regression (MLR) technique on a database that consists of 65 polymer�solvent combinations involving 10 different polymer. Among the 30 different physicochemical, topological and structural descriptors that were considered as inputs to the model, only eight variables (four variables for the polymer and four descriptors for the solvent) were selected using the elimination selection stepwise regression method (ES-SWR). The physical meaning of each descriptor is discussed in details. The accuracy of the proposed MLR model is illustrated using various evaluation techniques: leave-one-out cross validation procedure, validation through an external test set and Y-randomization. Furthermore, the calculation of the domain of applicability defines the area of reliable predictions.

Abstract: A quantitative-structure activity relationship was obtained by applying Multiple Linear Regression Analysis to a series of 80 1-[2-hydroxyethoxy-methyl]-6-(phenylthio) thymine (HEPT) derivatives with significant anti-HIV activity. For the selection of the best among 37 different descriptors, the Elimination Selection Stepwise Regression Method (ES-SWR) was utilized. The resulting QSAR model (R (2) (CV) = 0.8160; S (PRESS) = 0.5680) proved to be very accurate both in training and predictive stages.

Abstract: A linear quantitative-structure activity relationship model is developed in this work using Multiple Linear Regression Analysis as applied to a series of 51 1-(3,3-diphenylpropyl)-piperidinyl phenylacetamides derivatives with CCR5 binding affinity. For the selection of the best variables the Elimination Selection-Stepwise Regression Method (ES-SWR) is utilized. The predictive ability of the model is evaluated against a set of 13 compounds. Based on the produced QSAR model and an analysis on the domain of its applicability, the effects of various structural modifications on biological activity are investigated. The study leads to a number of guanidine derivatives with significantly improved predicted activities.

Abstract: A novel and simple method for the synthesis of functionalized 2-amino-3-cyano-4-chromones is reported. The title compounds were isolated after acylation of malononitrile with Nhydroxybenzotriazolyl acetylsalicylates, generated in situ by treating acetylsalicylic acid derivatives with N-hydroxybenzotriazole, followed by cyclization. The described one-pot methodology is characterized by short reaction times, high yields (68 to 77%), no side-products and provides chromones with a variety of substituents on the aromatic ring. The structure of the isolated compounds has been determined by means of 1H/13C NMR and FT-IR Spectroscopy.

Abstract: A linear quantitative structure-activity relationship (QSAR) model is presented for modeling and predicting induction of apoptosis by 4-aryl-4H-chromenes. The model was produced by using the multiple linear regression (MLR) technique on a database that consists of 43 recently discovered 4-aryl-4H-chromenes. Among the 61 different physicochemical, topological, and structural descriptors that were considered as inputs to the model, seven variables were selected using the elimination selection-stepwise regression method (ES-SWR). The physical meaning of each descriptor is discussed. The accuracy of the proposed MLR model is illustrated using the following evaluation techniques: cross-validation, validation through an external test set, and Y-randomization. Furthermore, the domain of applicability which indicates the area of reliable predictions is defined.

Abstract: A novel approach to the prediction of the glass transition temperature (Tg) for high molecular polymers is presented. A new quantitative structure�property relationship (QSPR) model is obtained using Radial Basis Function (RBF) neural networks and a set of four-parameter descriptors, P MVðter�ðRter�, LF, DXSB and P PEI. The produced QSPR model (R2Z0.9269) proved to be considerably more accurate compared to a multiple linear regression model (R2Z0.8227).

Abstract: A novel method for the synthesis of functionalized 3- substituted 4-hydroxycoumarins is reported. C-Acylation compounds were derived from the reaction of the N-hydroxybenzotriazole ester of the functionalized acetyl salicylic acids and a variety of active methylene compounds and cyclized to the title compounds. The synthesis is simple and the compounds are produced in yields varying from 39 to 80%. The structure of the newly prepared C-acylation compounds was thoroughly studied through NMR spectroscopy for the first time in the literature.

Abstract: The synthesis of novel N-urethane-protected γ-methylanlino-β -oxo esters and their use as precursors for the preparation of N-methyltetramic acids is described. The presence of the bulky urethane protecting group on the nitrogen atom gives rise to rotational isomers detectable in the NMR spectra of the compounds, along with the keto/enol tautomerism. The mechanism of the cyclisation reaction of γ-amino-β-oxo esters to tetramic acids was studied theoretically by the B3LYP hybrid density functional method.