The Experts below are selected from a list of 87 Experts worldwide ranked by ideXlab platform
Piotr Cysewski - One of the best experts on this subject based on the ideXlab platform.
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predicting value of binding constants of organic ligands to beta cyclodextrin application of Marsplines and descriptors encoded in smiles string
Symmetry, 2019Co-Authors: Piotr Cysewski, Maciej PrzybylekAbstract:The quantitative structure–activity relationship (QSPR) model was formulated to quantify values of the binding constant (lnK) of a series of ligands to beta–cyclodextrin (β-CD). For this purpose, the multivariate adaptive regression splines (Marsplines) methodology was adopted with molecular descriptors derived from the simplified molecular input line entry specification (SMILES) strings. This approach allows discovery of regression equations consisting of new non-linear components (basis functions) being combinations of molecular descriptors. The model was subjected to the standard internal and external validation procedures, which indicated its high predictive power. The appearance of polarity-related descriptors, such as XlogP, confirms the hydrophobic nature of the cyclodextrin cavity. The model can be used for predicting the affinity of new ligands to β-CD. However, a non-standard application was also proposed for classification into Biopharmaceutical Classification System (BCS) drug types. It was found that a single parameter, which is the estimated value of lnK, is sufficient to distinguish highly permeable drugs (BCS class I and II) from low permeable ones (BCS class II and IV). In general, it was found that drugs of the former group exhibit higher affinity to β-CD then the latter group (class III and IV).
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application of multivariate adaptive regression splines Marsplines methodology for screening of dicarboxylic acid cocrystal using 1d and 2d molecular descriptors
Crystal Growth & Design, 2019Co-Authors: Maciej Przybylek, Julia Slabuszewska, Dorota Ziolkowska, Karina Mroczynska, Tomasz Jelinski, Piotr CysewskiAbstract:Dicarboxylic acids (DiAs) are probably among of the most popular cocrystal formers. Due to the high hydrophilicity and nontoxicity, they are promising solubilizers of active pharmaceutical ingredients (APIs). Although DiAs appear to be highly capable of forming multicomponent crystals with various compounds, some systems reported in the literature are physical mixtures of the solid state without forming stable intermolecular complex. In this study, an accurate cocrystal screening model was developed based on the Marsplines (Multivariate Adaptive Regression Splines) methodology and easily computable descriptors driven simply from the SMILES codes. Additionally, the data set was enriched with several new mixtures of sulfamethazine. As demonstrated, this sulfonamide can form new multicomponent crystals with oxalic, malonic, and maleic acids. In the case of the latter system, a significant 10-fold solubility advantage was observed. The whole data set comprised 608 cocrystals and 104 systems hardy miscible in ...
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application of multivariate adaptive regression splines Marsplines for predicting hansen solubility parameters based on 1d and 2d molecular descriptors computed from smiles string
Journal of Chemistry, 2019Co-Authors: Maciej Przybylek, Tomasz Jelinski, Piotr CysewskiAbstract:A new method of Hansen solubility parameters (HSPs) prediction was developed by combining the multivariate adaptive regression splines (Marsplines) methodology with a simple multivariable regression involving 1D and 2D PaDEL molecular descriptors. In order to adopt the Marsplines approach to QSPR/QSAR problems, several optimization procedures were proposed and tested. The effectiveness of the obtained models was checked via standard QSPR/QSAR internal validation procedures provided by the QSARINS software and by predicting the solubility classification of polymers and drug-like solid solutes in collections of solvents. By utilizing information derived only from SMILES strings, the obtained models allow for computing all of the three Hansen solubility parameters including dispersion, polarization, and hydrogen bonding. Although several descriptors are required for proper parameters estimation, the proposed procedure is simple and straightforward and does not require a molecular geometry optimization. The obtained HSP values are highly correlated with experimental data, and their application for solving solubility problems leads to essentially the same quality as for the original parameters. Based on provided models, it is possible to characterize any solvent and liquid solute for which HSP data are unavailable.
Maciej Przybylek - One of the best experts on this subject based on the ideXlab platform.
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predicting value of binding constants of organic ligands to beta cyclodextrin application of Marsplines and descriptors encoded in smiles string
Symmetry, 2019Co-Authors: Piotr Cysewski, Maciej PrzybylekAbstract:The quantitative structure–activity relationship (QSPR) model was formulated to quantify values of the binding constant (lnK) of a series of ligands to beta–cyclodextrin (β-CD). For this purpose, the multivariate adaptive regression splines (Marsplines) methodology was adopted with molecular descriptors derived from the simplified molecular input line entry specification (SMILES) strings. This approach allows discovery of regression equations consisting of new non-linear components (basis functions) being combinations of molecular descriptors. The model was subjected to the standard internal and external validation procedures, which indicated its high predictive power. The appearance of polarity-related descriptors, such as XlogP, confirms the hydrophobic nature of the cyclodextrin cavity. The model can be used for predicting the affinity of new ligands to β-CD. However, a non-standard application was also proposed for classification into Biopharmaceutical Classification System (BCS) drug types. It was found that a single parameter, which is the estimated value of lnK, is sufficient to distinguish highly permeable drugs (BCS class I and II) from low permeable ones (BCS class II and IV). In general, it was found that drugs of the former group exhibit higher affinity to β-CD then the latter group (class III and IV).
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application of multivariate adaptive regression splines Marsplines methodology for screening of dicarboxylic acid cocrystal using 1d and 2d molecular descriptors
Crystal Growth & Design, 2019Co-Authors: Maciej Przybylek, Julia Slabuszewska, Dorota Ziolkowska, Karina Mroczynska, Tomasz Jelinski, Piotr CysewskiAbstract:Dicarboxylic acids (DiAs) are probably among of the most popular cocrystal formers. Due to the high hydrophilicity and nontoxicity, they are promising solubilizers of active pharmaceutical ingredients (APIs). Although DiAs appear to be highly capable of forming multicomponent crystals with various compounds, some systems reported in the literature are physical mixtures of the solid state without forming stable intermolecular complex. In this study, an accurate cocrystal screening model was developed based on the Marsplines (Multivariate Adaptive Regression Splines) methodology and easily computable descriptors driven simply from the SMILES codes. Additionally, the data set was enriched with several new mixtures of sulfamethazine. As demonstrated, this sulfonamide can form new multicomponent crystals with oxalic, malonic, and maleic acids. In the case of the latter system, a significant 10-fold solubility advantage was observed. The whole data set comprised 608 cocrystals and 104 systems hardy miscible in ...
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application of multivariate adaptive regression splines Marsplines for predicting hansen solubility parameters based on 1d and 2d molecular descriptors computed from smiles string
Journal of Chemistry, 2019Co-Authors: Maciej Przybylek, Tomasz Jelinski, Piotr CysewskiAbstract:A new method of Hansen solubility parameters (HSPs) prediction was developed by combining the multivariate adaptive regression splines (Marsplines) methodology with a simple multivariable regression involving 1D and 2D PaDEL molecular descriptors. In order to adopt the Marsplines approach to QSPR/QSAR problems, several optimization procedures were proposed and tested. The effectiveness of the obtained models was checked via standard QSPR/QSAR internal validation procedures provided by the QSARINS software and by predicting the solubility classification of polymers and drug-like solid solutes in collections of solvents. By utilizing information derived only from SMILES strings, the obtained models allow for computing all of the three Hansen solubility parameters including dispersion, polarization, and hydrogen bonding. Although several descriptors are required for proper parameters estimation, the proposed procedure is simple and straightforward and does not require a molecular geometry optimization. The obtained HSP values are highly correlated with experimental data, and their application for solving solubility problems leads to essentially the same quality as for the original parameters. Based on provided models, it is possible to characterize any solvent and liquid solute for which HSP data are unavailable.
Shihab Asfour - One of the best experts on this subject based on the ideXlab platform.
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Short-term electrical peak demand forecasting in a large government building using artificial neural networks
Energies, 2014Co-Authors: Jason Grant, Moataz Eltoukhy, Shihab AsfourAbstract:The power output capacity of a local electrical utility is dictated by its customers’ cumulative peak-demand electrical consumption. Most electrical utilities in the United States maintain peak-power generation capacity by charging for end-use peak electrical demand; thirty to seventy percent of an electric utility’s bill. To reduce peak demand, a real-time energy monitoring system was designed, developed, and implemented for a large government building. Data logging, combined with an application of artificial neural networks (ANNs), provides short-term electrical load forecasting data for controlled peak demand. The ANN model was tested against other forecasting methods including simple moving average (SMA), linear regression, and multivariate adaptive regression splines (Marsplines) and was effective at forecasting peak building electrical demand in a large government building sixty minutes into the future. The ANN model presented here outperformed the other forecasting methods tested with a mean absolute percentage error (MAPE) of 3.9% as compared to the SMA, linear regression, and Marsplines MAPEs of 7.7%, 17.3%, and 7.0% respectively. Additionally, the ANN model realized an absolute maximum error (AME) of 8.2% as compared to the SMA, linear regression, and Marsplines AMEs of 26.2%, 45.1%, and 22.5% respectively.
Mats Soderstrom - One of the best experts on this subject based on the ideXlab platform.
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digital soil mapping of arable land in sweden validation of performance at multiple scales
Geoderma, 2017Co-Authors: Kristin Piikki, Mats SoderstromAbstract:Abstract In this study, we produced a detailed digital soil map of topsoil texture and soil organic matter (SOM) content for 2.4 million ha of arable land in Sweden (DSMS). Three spatially exhaustive datasets (a laser-scanned digital elevation model, airborne gamma radiation scanning data and a legacy Quaternary deposit map) were calibrated against topsoil texture and SOM content in around 13,500 soil samples, using multivariate adaptive regression splines (Marsplines) modelling. We then deployed the Marsplines models to produce raster maps (50 m × 50 m) of clay, sand and SOM content. The modelling procedure was validated by an independent dataset of about 24,000 samples clustered on 544 farms (with a local sample density of one per 3 ha). The error in clay content was
Jason Grant - One of the best experts on this subject based on the ideXlab platform.
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Short-term electrical peak demand forecasting in a large government building using artificial neural networks
Energies, 2014Co-Authors: Jason Grant, Moataz Eltoukhy, Shihab AsfourAbstract:The power output capacity of a local electrical utility is dictated by its customers’ cumulative peak-demand electrical consumption. Most electrical utilities in the United States maintain peak-power generation capacity by charging for end-use peak electrical demand; thirty to seventy percent of an electric utility’s bill. To reduce peak demand, a real-time energy monitoring system was designed, developed, and implemented for a large government building. Data logging, combined with an application of artificial neural networks (ANNs), provides short-term electrical load forecasting data for controlled peak demand. The ANN model was tested against other forecasting methods including simple moving average (SMA), linear regression, and multivariate adaptive regression splines (Marsplines) and was effective at forecasting peak building electrical demand in a large government building sixty minutes into the future. The ANN model presented here outperformed the other forecasting methods tested with a mean absolute percentage error (MAPE) of 3.9% as compared to the SMA, linear regression, and Marsplines MAPEs of 7.7%, 17.3%, and 7.0% respectively. Additionally, the ANN model realized an absolute maximum error (AME) of 8.2% as compared to the SMA, linear regression, and Marsplines AMEs of 26.2%, 45.1%, and 22.5% respectively.