The Experts below are selected from a list of 453 Experts worldwide ranked by ideXlab platform
Johannes Ranke - One of the best experts on this subject based on the ideXlab platform.
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qualitative and quantitative structure activity relationships for the inhibitory effects of cationic head groups functionalised side chains and anions of ionic liquids on acetylcholinesterase
Green Chemistry, 2008Co-Authors: Jiirgen Arning, Stefan Stolte, Andrea Boschen, Frauke Stock, Williamrobert Pitner, Urs Welzbiermann, Bernd Jastorff, Johannes RankeAbstract:To contribute to a deeper insight into the hazard potential of ionic liquids to humans and the environment, an acetylcholinesterase (AchE) inhibition screening assay was used to identify Toxicophore substructures and interaction potentials mediating enzyme inhibition.The positively charged nitrogen atom, a widely delocalised aromatic system, and the lipophilicity of the side chains connected to the cationic head groups can be identified as the key structural elements in binding to the enzymes active site. With respect to this, the dimethylaminopyridinium, the quinolinium and the pyridinium head groups exhibit a very strong inhibitory potential to the enzyme with IC50 values around 10 µM. In contrast, the polar and non-aromatic morpholinium head group is found to be only weakly inhibiting to the enzyme activity, with IC50 values > 500 µM.The introduction of polar hydroxy, ether or nitrile functions into the alkyl side chain is shown to be a potent structural alteration to shift the corresponding ionic liquids to a lower inhibitory potential. Supporting this fact, for a series of imidazolium cations, a QSAR correlation was set up by the linear regression of the log IC50versus the logarithm of the HPLC-derived lipophilicity parameter k0.Additionally, a broad set of anion species (inorganic, organic and complex borate anions), commonly used as ionic liquid counterions, was tested and the vast majority exhibited no effect on AchE. Only the fluoride and fluoride containing anion species which readily undergo hydrolytic cleavage can be identified to act as AchE inhibitors.
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qualitative and quantitative structure activity relationships for the inhibitory effects of cationic head groups functionalised side chains and anions of ionic liquids on acetylcholinesterase
Green Chemistry, 2008Co-Authors: Jiirgen Arning, Stefan Stolte, Andrea Boschen, Frauke Stock, Williamrobert Pitner, Urs Welzbiermann, Bernd Jastorff, Johannes RankeAbstract:To contribute to a deeper insight into the hazard potential of ionic liquids to humans and the environment, an acetylcholinesterase (AchE) inhibition screening assay was used to identify Toxicophore substructures and interaction potentials mediating enzyme inhibition.The positively charged nitrogen atom, a widely delocalised aromatic system, and the lipophilicity of the side chains connected to the cationic head groups can be identified as the key structural elements in binding to the enzymes active site. With respect to this, the dimethylaminopyridinium, the quinolinium and the pyridinium head groups exhibit a very strong inhibitory potential to the enzyme with IC50 values around 10 µM. In contrast, the polar and non-aromatic morpholinium head group is found to be only weakly inhibiting to the enzyme activity, with IC50 values > 500 µM.The introduction of polar hydroxy, ether or nitrile functions into the alkyl side chain is shown to be a potent structural alteration to shift the corresponding ionic liquids to a lower inhibitory potential. Supporting this fact, for a series of imidazolium cations, a QSAR correlation was set up by the linear regression of the log IC50versus the logarithm of the HPLC-derived lipophilicity parameter k0.Additionally, a broad set of anion species (inorganic, organic and complex borate anions), commonly used as ionic liquid counterions, was tested and the vast majority exhibited no effect on AchE. Only the fluoride and fluoride containing anion species which readily undergo hydrolytic cleavage can be identified to act as AchE inhibitors.
Jiirgen Arning - One of the best experts on this subject based on the ideXlab platform.
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qualitative and quantitative structure activity relationships for the inhibitory effects of cationic head groups functionalised side chains and anions of ionic liquids on acetylcholinesterase
Green Chemistry, 2008Co-Authors: Jiirgen Arning, Stefan Stolte, Andrea Boschen, Frauke Stock, Williamrobert Pitner, Urs Welzbiermann, Bernd Jastorff, Johannes RankeAbstract:To contribute to a deeper insight into the hazard potential of ionic liquids to humans and the environment, an acetylcholinesterase (AchE) inhibition screening assay was used to identify Toxicophore substructures and interaction potentials mediating enzyme inhibition.The positively charged nitrogen atom, a widely delocalised aromatic system, and the lipophilicity of the side chains connected to the cationic head groups can be identified as the key structural elements in binding to the enzymes active site. With respect to this, the dimethylaminopyridinium, the quinolinium and the pyridinium head groups exhibit a very strong inhibitory potential to the enzyme with IC50 values around 10 µM. In contrast, the polar and non-aromatic morpholinium head group is found to be only weakly inhibiting to the enzyme activity, with IC50 values > 500 µM.The introduction of polar hydroxy, ether or nitrile functions into the alkyl side chain is shown to be a potent structural alteration to shift the corresponding ionic liquids to a lower inhibitory potential. Supporting this fact, for a series of imidazolium cations, a QSAR correlation was set up by the linear regression of the log IC50versus the logarithm of the HPLC-derived lipophilicity parameter k0.Additionally, a broad set of anion species (inorganic, organic and complex borate anions), commonly used as ionic liquid counterions, was tested and the vast majority exhibited no effect on AchE. Only the fluoride and fluoride containing anion species which readily undergo hydrolytic cleavage can be identified to act as AchE inhibitors.
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qualitative and quantitative structure activity relationships for the inhibitory effects of cationic head groups functionalised side chains and anions of ionic liquids on acetylcholinesterase
Green Chemistry, 2008Co-Authors: Jiirgen Arning, Stefan Stolte, Andrea Boschen, Frauke Stock, Williamrobert Pitner, Urs Welzbiermann, Bernd Jastorff, Johannes RankeAbstract:To contribute to a deeper insight into the hazard potential of ionic liquids to humans and the environment, an acetylcholinesterase (AchE) inhibition screening assay was used to identify Toxicophore substructures and interaction potentials mediating enzyme inhibition.The positively charged nitrogen atom, a widely delocalised aromatic system, and the lipophilicity of the side chains connected to the cationic head groups can be identified as the key structural elements in binding to the enzymes active site. With respect to this, the dimethylaminopyridinium, the quinolinium and the pyridinium head groups exhibit a very strong inhibitory potential to the enzyme with IC50 values around 10 µM. In contrast, the polar and non-aromatic morpholinium head group is found to be only weakly inhibiting to the enzyme activity, with IC50 values > 500 µM.The introduction of polar hydroxy, ether or nitrile functions into the alkyl side chain is shown to be a potent structural alteration to shift the corresponding ionic liquids to a lower inhibitory potential. Supporting this fact, for a series of imidazolium cations, a QSAR correlation was set up by the linear regression of the log IC50versus the logarithm of the HPLC-derived lipophilicity parameter k0.Additionally, a broad set of anion species (inorganic, organic and complex borate anions), commonly used as ionic liquid counterions, was tested and the vast majority exhibited no effect on AchE. Only the fluoride and fluoride containing anion species which readily undergo hydrolytic cleavage can be identified to act as AchE inhibitors.
Gilles Klopman - One of the best experts on this subject based on the ideXlab platform.
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Multiple computer‐automated structure evaluation study of aquatic toxicity. III. Vibrio fischeri
Environmental toxicology and chemistry, 2003Co-Authors: Gilles Klopman, Scott E. StuartAbstract:An acute toxicity model was constructed on the basis of 901 chemicals tested for toxicity against the luminescent bacteria Vibrio fischeri (formerly Photobacterium phosphoreum, the Microtox® test). The model was created using the Multiple Computer-Automated Structure Evaluation (M-CASE) program. The model can correctly predict acute toxicity for 92% of the compounds with an error averaging 0.55 log units per median effect concentration (EC50). The main Toxicophores, corresponding to polar and nonpolar narcosis, and other types of reactive chemicals were identified.
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Multiple computer‐automated structure evaluation study of aquatic toxicity II. Fathead minnow
Environmental Toxicology and Chemistry, 2000Co-Authors: Gilles Klopman, Roustem D. Saiakhov, Herbert S. RosenkranzAbstract:An acute toxicity model was constructed on the basis of experimental data for 685 chemicals tested for toxicity for fathead minnow. The multiple computer-automated structure evaluation (M-CASE) program was used for the construction of the model. Based on a comparison between our results and published results, we found that the methodology is able to describe acute toxicity for the fathead minnow with high accuracy. The model incorporates the concept of a baseline activity as one of the parameters for the correlation as well as others parameters such as the presence of biophores, hardness-softness parameters, and other characteristics determined from quantum mechanical calculations. By using its artificial intelligence algorithm, M-CASE chooses automatically the most suitable set of parameters for evaluating the minnow toxicity of any organic molecule. We found that M-CASE can correctly predict acute toxicity for minnow for 80% of organic compounds with an average error of only 0.4 log units of lethal dose. The main Toxicophores, corresponding to polar narcosis and to other types of reactive chemicals, were identified.
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Development, Characterization and Application of Predictive-Toxicology Models
SAR and QSAR in environmental research, 1999Co-Authors: Herbert S. Rosenkranz, Albert R. Cunningham, Ying Ping Zhang, H.g. Claycamp, Orest T. Macina, Nancy B. Sussman, Stephen G. Grant, Gilles KlopmanAbstract:Abstract The adoption of SAR techniques for risk assessment purposes requires that the predictive performance of models be characterized and optimized. The development of such methods with respect to CASE/MULTICASE are described. Moreover the effects of size, informational content, ratio of actives/inactives in the model on predictivity must be determined. Characterized models can provide mechanistic insights: nature of Toxicophore, reactivity, receptor binding. Comparison of Toxicophores among SAR models allows a determination of mechanistic overlaps (e.g., mutagenicity, toxicity, inhibition of gap junctional intercellular communication vs. carcinogenicity). Methods have been developed to combine SAR submodels and thereby improve predictive performance. Now that predictive toxicology methods are gaining acceptance, the development of Good Laboratory Practices is a further priority, as is the development of graduate programs in Computational Toxicology to adequately train the needed professional.
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Multiple Computer‐Automated structure evaluation program study of aquatic toxicity 1: Guppy
Environmental Toxicology and Chemistry, 1999Co-Authors: Gilles Klopman, Herbert S. Rosenkranz, Roustem D. Saiakhov, Joop L. M. HermensAbstract:An acute fish toxicity model was constructed on the basis of a wide series of experimental data for guppy. The Multiple Computer-Automated Structure Evaluation program was used to construct the model. The created model possesses very good predictive ability. It can correctly predict acute toxicity for guppy for 80% of compounds with an average error of only 0.63 log unit per median lethal concentration. The importance of the narcosis effect was demonstrated. The main Toxicophores, corresponding to polar narcosis and to the reactive chemicals, were identified.
Alban Lepailleur - One of the best experts on this subject based on the ideXlab platform.
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Mining (Soft-) Skypatterns Using Constraint Programming
2016Co-Authors: Willy Ugarte, Patrice Boizumault, Samir Loudni, Bruno Cremilleux, Alban LepailleurAbstract:Within the pattern mining area, skypatterns enable to express a user-preference point of view according to a dominance relation. In this paper, we deal with the introduction of softness in the skypattern mining problem. First, we show how softness can provide convenient patterns that would be missed otherwise. Then, thanks to Constraint Programming, we propose a generic and efficient method to mine skypatterns as well as soft ones. Finally, we show the relevance and the effectiveness of our approach through experiments on UCI benchmarks and a case study in chemoinformatics for discovering Toxicophores.
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EGC (best of volume) - Mining (Soft-) Skypatterns Using Constraint Programming
Advances in Knowledge Discovery and Management, 2015Co-Authors: Willy Ugarte, Patrice Boizumault, Samir Loudni, Bruno Cremilleux, Alban LepailleurAbstract:Within the pattern mining area, skypatterns enable to express a user-preference point of view according to a dominance relation. In this paper, we deal with the introduction of softness in the skypattern mining problem. First, we show how softness can provide convenient patterns that would be missed otherwise. Then, thanks to Constraint Programming, we propose a generic and efficient method to mine skypatterns as well as soft ones. Finally, we show the relevance and the effectiveness of our approach through experiments on UCI benchmarks and a case study in chemoinformatics for discovering Toxicophores.
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Soft constraints for pattern mining
Journal of Intelligent Information Systems, 2015Co-Authors: Willy Ugarte, Patrice Boizumault, Samir Loudni, Bruno Cremilleux, Alban LepailleurAbstract:Constraint-based pattern discovery is at the core of numerous data mining tasks. Patterns are extracted with respect to a given set of constraints (frequency, closedness, size, etc). In practice, many constraints require threshold values whose choice is often arbitrary. This difficulty is even harder when several thresholds are required and have to be combined. Moreover, patterns barely missing a threshold will not be extracted even if they may be relevant. The paper advocates the introduction of softness into the pattern discovery process. By using Constraint Programming, we propose efficient methods to relax threshold constraints as well as constraints involved in patterns such as the top- k patterns and the skypatterns. We show the relevance and the efficiency of our approach through a case study in chemoinformatics for discovering Toxicophores.
Kunal Roy - One of the best experts on this subject based on the ideXlab platform.
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Predictive toxicity modelling of benzodiazepine drugs using multiple in silico approaches: descriptor-based QSTR, group-based QSTR and 3D-Toxicophore mapping
Molecular Simulation, 2014Co-Authors: Supratik Kar, Kunal RoyAbstract:Benzodiazepines have been widely used therapeutically for their ability to act as tranquilizers, sedative-hypnotics, antiepileptics and frequently prescribed to women during pregnancy for managing preeclampsia or eclampsia. The present report deals with quantitative structure–toxicity relationship (QSTR) modelling of a series of benzodiazepines, in the context of the 3R concept, to provide an insight into the main structural fragments that impart toxicity to these molecules. Three different in silico techniques, namely descriptor-based QSTR, group-based QSTR and 3D-Toxicophore mapping, were employed to obtain statistically significant models. Multiple in silico models made it possible to reach a unified conclusion regarding the structural fragments and features responsible for the toxicity and provide consensus predictions which can be effectively utilised to design and predict less toxic new benzodiazepines.
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Predictive Chemometric Modeling and Three-Dimensional Toxicophore Mapping of Diverse Organic Chemicals Causing Bioluminescent Repression of the Bacterium Genus Pseudomonas
Industrial & Engineering Chemistry Research, 2013Co-Authors: Supratik Kar, Kunal RoyAbstract:Classification and regression-based quantitative structure–activity relationship (QSAR) as well as three-dimensional (3D) Toxicophore models were developed for toxicity prediction of 104 organic chemicals causing bioluminescent repression of the bacterium genus Pseudomonas isolated from industrial wastewater. Statistically significant and interpretable in silico models were obtained using linear discriminant analysis (classification), genetic partial least-squares (regression), and 3D Toxicophore models. The QSAR and Toxicophore models were scrupulously validated internally as well as externally along with the randomization test to avoid the possibilities of chance correlation. Features such as octanol–water partition coefficient, third-order branching, ═CH2 fragment or unsaturation, and the presence of a higher number of electronegative atoms (specifically halogen atoms) and their contribution toward hydrophobicity have been identified as major responsible structural attributes for higher toxicity from t...
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Exploring QSTR modeling and Toxicophore mapping for identification of important molecular features contributing to the chemical toxicity in Escherichia coli.
Toxicology in vitro : an international journal published in association with BIBRA, 2013Co-Authors: Subrata Pramanik, Kunal RoyAbstract:Abstract Biodiversity deprivation can affect functions and services of the ecosystem. Changes in biodiversity alter ecosystem processes and change the resilience of ecosystems to ecological changes. Bacterial communities are the main form of biomass in the ecosystem and one of largest populations on the planet. Bacterial communities provide important services to biodiversity. They break down pollutants, municipal waste and ingested food, and they are the primary route for recycling of organic matter to plants and other autotrophs, conversion of inorganic matter into new biological tissue using sunlight, management of energy crisis through use of biofuel. In the present study, computational chemistry and statistical modeling have been used to develop mathematical equations which can be applied to calculate toxicity of new/unknown chemicals/biofuels/metabolites in Escherichia coli. 2D and 3D descriptors were generated from molecular structure of compounds and mathematical models have been developed using genetic function approximation followed by multiple linear regression (GFA-MLR) method. Model validity was checked through defined internal (R2 = 0.751 and Q2 = 0.711), and external ( R pred 2 = 0.773 ) statistical parameters. Molecular features responsible for toxicity were also assessed through 3D Toxicophore study. The Toxicophore-based model was validated (R = 0.785) using qualitative statistical metrics and randomization test (Fischer validation).
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Predictive Chemometric Modeling and Three-Dimensional Toxicophore Mapping of Diverse Organic Chemicals Causing Bioluminescent Repression of the Bacterium Genus Pseudomonas
2013Co-Authors: Supratik Kar, Kunal RoyAbstract:Classification and regression-based quantitative structure–activity relationship (QSAR) as well as three-dimensional (3D) Toxicophore models were developed for toxicity prediction of 104 organic chemicals causing bioluminescent repression of the bacterium genus Pseudomonas isolated from industrial wastewater. Statistically significant and interpretable in silico models were obtained using linear discriminant analysis (classification), genetic partial least-squares (regression), and 3D Toxicophore models. The QSAR and Toxicophore models were scrupulously validated internally as well as externally along with the randomization test to avoid the possibilities of chance correlation. Features such as octanol–water partition coefficient, third-order branching, CH2 fragment or unsaturation, and the presence of a higher number of electronegative atoms (specifically halogen atoms) and their contribution toward hydrophobicity have been identified as major responsible structural attributes for higher toxicity from the developed in silico models. The present approaches can provide rich information in the context of virtual screening of relevant chemical libraries for aquatic toxicity prediction
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First report on predictive chemometric modeling, 3D-Toxicophore mapping and in silico screening of in vitro basal cytotoxicity of diverse organic chemicals.
Toxicology in vitro : an international journal published in association with BIBRA, 2012Co-Authors: Supratik Kar, Kunal RoyAbstract:Classification and regression based quantitative structure-toxicity relationship (QSTR) as well as Toxicophore models were developed for the first time on basal cytotoxicity data (in vitro 3T3 neutral red uptake data) of a diverse series of chemicals (including drugs and environmental pollutants) collected from the ACuteTox database (http://www.acutetox.eu/). Statistically significant QSTR models were obtained using linear discriminant analysis (classification) and partial least squares (regression) methodologies. Generated Toxicophore models showed four important features responsible for basal cytotoxicity: (i) two hydrophobic aliphatic groups (HYD Aliphatic), (ii) ring aromatic group (RA) and (iii) hydrogen bond donor (HBD). The most predictive hypothesis (Hypo 1) had a correlation coefficient of 0.932 for the training set, a low rms deviation of 1.105, and an acceptable cost difference of 62.8 bits, which represents a true correlation and a good predictivity. QSTR and Toxicophore models were rigorously validated internally as well as externally along with the randomization test to nullify the possibilities of chance correlation. Our in silico models enable to identify the essential structural attributes and quantify the prime molecular pre-requisites which were chiefly responsible for in vitro basal cytotoxicity. The developed models were also implemented to screen basal cytotoxicity for huge number DrugBank database (http://www.drugbank.ca/) compounds.