In this work we explore the corrosion inhibition of steel in acidic medium for a diverse set of o... more In this work we explore the corrosion inhibition of steel in acidic medium for a diverse set of organic compounds by developing a KNIME workflow including the newly introduced Enalos KNIME nodes. We have integrated in a single database 186 corrosion inhibition data of steel in acidic medium including 55 organic inhibitors in different concentrations and investigated the structural characteristics that influence the corrosion inhibition effect. We introduce the custom made Enalos KNIME nodes that are made publicly available by Novamechanics Ltd, as key – nodes to develop robust and validated quantitative structure–property models (QSPRs). Tasks such as assessing the structural characteristics of compounds, validating the model and defining the domain of its applicability are easily addressed using the Enalos family nodes. We have concluded in an accurate kNN model that can reliably predict the corrosion inhibition of a given compound.
OpenTox provides an interoperable, standards-based Framework for the support of predictive toxico... more 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.
Quantitative Structure Activity Relationships (QSARs) are mathematical models that correlate stru... more Quantitative Structure Activity Relationships (QSARs) are mathematical models that correlate structural or property descriptions of compounds (hydrophobicity, topology, electronic properties etc.) with activities, such as chemical measurements and biological assays. In this paper we propose a modeling methodology suitable for QSAR studies which selects the proper descriptors based on evolutionary computing and finally produces Radial Basis Function (RBF) neural network
In this work, a linear quantitative structure-property relationship (QSPR) model is presented for... more 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. q
A linear quantitative structureeactivity relationship (QSAR) model is presented for modeling and ... more A linear quantitative structureeactivity 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 3 k, 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.
A linear quantitative structure-activity relationship (QSAR) model is presented for modeling and ... more 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.
In this study, we present a new model that has been developed for the prediction of θ (lower crit... more In this study, we present a new model that has been developed for the prediction of θ (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.
Virtual Screening (VS) has experienced increased attention into the recent years due to the large... more 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.
In this study, quantitative structure-activity ⁄ property models are developed for modeling and p... more 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.
A linear Quantitative Structure–Activity Relationship (QSAR) is developed in this work for modeli... more 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.
A linear quantitative–structure activity relationship model is developed in this work using Multi... more 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.
In the present work a series of novel coumarin-3-carboxamides and their hybrids with the alpha-li... more 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 structureactivity relationships in order to define the structural features required for activity. diamine. Yield: 71%; 1 H NMR (CDCl 3 /CF 3 COOD): d ¼ 1.24-1.45 (m, 8H, (CH 2 ) 2 (CH 2 ) 4 (CH 2 ) 2 ), 1.67-1.72 (m, 4H, CH 2 (CH 2 ) 4 CH 2 ), 3.14 (t, J ¼ 6.7 Hz, 2H, CH 2 NH 2 ), 3.46 (t, J ¼ 6.9 Hz, 2H, NHCH 2 ), 7.37 (dd,
A linear quantitative structure-activity relationship has been developed for a series of para-sub... more 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.
This paper presents the results of an optimization study on biaryl piperidine and 4-amino-2-biary... more 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.
A novel QSPR model is developed and evaluated for the prediction of diamagnetic susceptibility. T... more 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 (R 2 ¼ 0.88) is illustrated using various evaluation techniques, such as leave-one-out procedure (Q 2 ¼ 0.87) and validation through an external test set (R 2 pred ¼ 0.89). 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), and Diameter (Diam).
A series of N-substituted-quinolinone-3-aminoamides and their hybrids containing the R-lipoic aci... more A series of N-substituted-quinolinone-3-aminoamides and their hybrids containing the R-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.
A novel short-step methodology for the synthesis in good yields of functionalized coumarins has b... more 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.
A novel QSAR workflow is constructed that combines MLR with LS-SVM classification techniques for ... more 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 IC50 inhibition values. The accuracy of the QSAR model (R 2 = 0.80) is illustrated using various evaluation techniques, such as leave-one-out procedure ${(R^{2}_{\rm LOO} =0.67)}$ and validation through an external test set ${(R^{2}_{\rm pred} =0.78)}$ . 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 (1κ), demonstrate discriminatory and pharmacophore abilities.
In this work we explore the corrosion inhibition of steel in acidic medium for a diverse set of o... more In this work we explore the corrosion inhibition of steel in acidic medium for a diverse set of organic compounds by developing a KNIME workflow including the newly introduced Enalos KNIME nodes. We have integrated in a single database 186 corrosion inhibition data of steel in acidic medium including 55 organic inhibitors in different concentrations and investigated the structural characteristics that influence the corrosion inhibition effect. We introduce the custom made Enalos KNIME nodes that are made publicly available by Novamechanics Ltd, as key – nodes to develop robust and validated quantitative structure–property models (QSPRs). Tasks such as assessing the structural characteristics of compounds, validating the model and defining the domain of its applicability are easily addressed using the Enalos family nodes. We have concluded in an accurate kNN model that can reliably predict the corrosion inhibition of a given compound.
OpenTox provides an interoperable, standards-based Framework for the support of predictive toxico... more 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.
Quantitative Structure Activity Relationships (QSARs) are mathematical models that correlate stru... more Quantitative Structure Activity Relationships (QSARs) are mathematical models that correlate structural or property descriptions of compounds (hydrophobicity, topology, electronic properties etc.) with activities, such as chemical measurements and biological assays. In this paper we propose a modeling methodology suitable for QSAR studies which selects the proper descriptors based on evolutionary computing and finally produces Radial Basis Function (RBF) neural network
In this work, a linear quantitative structure-property relationship (QSPR) model is presented for... more 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. q
A linear quantitative structureeactivity relationship (QSAR) model is presented for modeling and ... more A linear quantitative structureeactivity 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 3 k, 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.
A linear quantitative structure-activity relationship (QSAR) model is presented for modeling and ... more 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.
In this study, we present a new model that has been developed for the prediction of θ (lower crit... more In this study, we present a new model that has been developed for the prediction of θ (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.
Virtual Screening (VS) has experienced increased attention into the recent years due to the large... more 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.
In this study, quantitative structure-activity ⁄ property models are developed for modeling and p... more 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.
A linear Quantitative Structure–Activity Relationship (QSAR) is developed in this work for modeli... more 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.
A linear quantitative–structure activity relationship model is developed in this work using Multi... more 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.
In the present work a series of novel coumarin-3-carboxamides and their hybrids with the alpha-li... more 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 structureactivity relationships in order to define the structural features required for activity. diamine. Yield: 71%; 1 H NMR (CDCl 3 /CF 3 COOD): d ¼ 1.24-1.45 (m, 8H, (CH 2 ) 2 (CH 2 ) 4 (CH 2 ) 2 ), 1.67-1.72 (m, 4H, CH 2 (CH 2 ) 4 CH 2 ), 3.14 (t, J ¼ 6.7 Hz, 2H, CH 2 NH 2 ), 3.46 (t, J ¼ 6.9 Hz, 2H, NHCH 2 ), 7.37 (dd,
A linear quantitative structure-activity relationship has been developed for a series of para-sub... more 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.
This paper presents the results of an optimization study on biaryl piperidine and 4-amino-2-biary... more 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.
A novel QSPR model is developed and evaluated for the prediction of diamagnetic susceptibility. T... more 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 (R 2 ¼ 0.88) is illustrated using various evaluation techniques, such as leave-one-out procedure (Q 2 ¼ 0.87) and validation through an external test set (R 2 pred ¼ 0.89). 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), and Diameter (Diam).
A series of N-substituted-quinolinone-3-aminoamides and their hybrids containing the R-lipoic aci... more A series of N-substituted-quinolinone-3-aminoamides and their hybrids containing the R-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.
A novel short-step methodology for the synthesis in good yields of functionalized coumarins has b... more 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.
A novel QSAR workflow is constructed that combines MLR with LS-SVM classification techniques for ... more 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 IC50 inhibition values. The accuracy of the QSAR model (R 2 = 0.80) is illustrated using various evaluation techniques, such as leave-one-out procedure ${(R^{2}_{\rm LOO} =0.67)}$ and validation through an external test set ${(R^{2}_{\rm pred} =0.78)}$ . 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 (1κ), demonstrate discriminatory and pharmacophore abilities.
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Papers by A Afantitis
by developing a KNIME workflow including the newly introduced Enalos KNIME nodes. We have integrated in a
single database 186 corrosion inhibition data of steel in acidic medium including 55 organic inhibitors in different
concentrations and investigated the structural characteristics that influence the corrosion inhibition effect. We
introduce the custom made Enalos KNIME nodes that are made publicly available by Novamechanics Ltd, as
key – nodes to develop robust and validated quantitative structure–property models (QSPRs). Tasks such as
assessing the structural characteristics of compounds, validating the model and defining the domain of its applicability
are easily addressed using the Enalos family nodes. We have concluded in an accurate kNN model that
can reliably predict the corrosion inhibition of a given compound.
by developing a KNIME workflow including the newly introduced Enalos KNIME nodes. We have integrated in a
single database 186 corrosion inhibition data of steel in acidic medium including 55 organic inhibitors in different
concentrations and investigated the structural characteristics that influence the corrosion inhibition effect. We
introduce the custom made Enalos KNIME nodes that are made publicly available by Novamechanics Ltd, as
key – nodes to develop robust and validated quantitative structure–property models (QSPRs). Tasks such as
assessing the structural characteristics of compounds, validating the model and defining the domain of its applicability
are easily addressed using the Enalos family nodes. We have concluded in an accurate kNN model that
can reliably predict the corrosion inhibition of a given compound.