Journal: Foods

Article Title: Extraction of Ursolic Acid from Apple Peel with Hydrophobic Deep Eutectic Solvents: Comparison between Response Surface Methodology and Artificial Neural Networks

doi: 10.3390/foods12020310

Figure Lengend Snippet: Results of the Box–Behnken design (BBD) for the extraction yield of UA and ANN calculated.

Article Snippet: Therefore, it can be proved that the ANN model can better calculate the experimental results than the RSM model, which is related to its general ability to approximate nonlinear systems [ ].

Techniques: Extraction

Journal: Pharmaceutical Research

Article Title: Computational Prediction of Drug Solubility in Fasted Simulated and Aspirated Human Intestinal Fluid

doi: 10.1007/s11095-014-1487-z

Figure Lengend Snippet: Physicochemical properties and S app of the investigated compounds a

Article Snippet: The combination of the (ANN) FaSSIF model results (obtained from ADMET Predictor) with the PLS predictions increased the predictive power, as identified from the lowered RMSE te (Table ).

Techniques:

Journal: Pharmaceutical Research

Article Title: Computational Prediction of Drug Solubility in Fasted Simulated and Aspirated Human Intestinal Fluid

doi: 10.1007/s11095-014-1487-z

Figure Lengend Snippet: Training- and testset S app ranges for FaSSIF. The literature training (Tr) and test sets (Te) are shown with blue and yellow circles respectively and the discovery test set is denoted with green circles.

Article Snippet: The combination of the (ANN) FaSSIF model results (obtained from ADMET Predictor) with the PLS predictions increased the predictive power, as identified from the lowered RMSE te (Table ).

Techniques:

Journal: Pharmaceutical Research

Article Title: Computational Prediction of Drug Solubility in Fasted Simulated and Aspirated Human Intestinal Fluid

doi: 10.1007/s11095-014-1487-z

Figure Lengend Snippet: Correlation between HIF, FaSSIF and buffer pH 6.5 Sapp correlations. a FaSSIF and PhBpH6.5, b HIF and PhBpH6.5, and c HIF and FaSSIF. Light gray circles represent compounds with a predicted logD pH6.5 below 3. Gray circles denote compounds with logD pH6.5 between 3 and 4. Dark gray circles shows compounds with a logD pH6.5 above 4.

Article Snippet: The combination of the (ANN) FaSSIF model results (obtained from ADMET Predictor) with the PLS predictions increased the predictive power, as identified from the lowered RMSE te (Table ).

Techniques:

Journal: Pharmaceutical Research

Article Title: Computational Prediction of Drug Solubility in Fasted Simulated and Aspirated Human Intestinal Fluid

doi: 10.1007/s11095-014-1487-z

Figure Lengend Snippet: Model Summary.

Article Snippet: The combination of the (ANN) FaSSIF model results (obtained from ADMET Predictor) with the PLS predictions increased the predictive power, as identified from the lowered RMSE te (Table ).

Techniques:

Journal: Pharmaceutical Research

Article Title: Computational Prediction of Drug Solubility in Fasted Simulated and Aspirated Human Intestinal Fluid

doi: 10.1007/s11095-014-1487-z

Figure Lengend Snippet: Prediction results for solubility in FaSSIF. a FaSSIF model based on seven calculated descriptors and b the same but including measured buffer solubility. Light green circles represent the training set while green and dark green circles denote the literature test set and discovery test set, respectively.

Article Snippet: The combination of the (ANN) FaSSIF model results (obtained from ADMET Predictor) with the PLS predictions increased the predictive power, as identified from the lowered RMSE te (Table ).

Techniques: Solubility

Journal: Pharmaceutical Research

Article Title: Computational Prediction of Drug Solubility in Fasted Simulated and Aspirated Human Intestinal Fluid

doi: 10.1007/s11095-014-1487-z

Figure Lengend Snippet: Loading plots for the FaSSIF and HIF models. a FaSSIF model loading plot and b with measured PhBph6.5 Sapp as additional descriptor. c HIF model loading plot and d experimentally determined PhBph6.5 Sapp as a_descriptor.

Article Snippet: The combination of the (ANN) FaSSIF model results (obtained from ADMET Predictor) with the PLS predictions increased the predictive power, as identified from the lowered RMSE te (Table ).

Techniques:

Journal: Pharmaceutical Research

Article Title: Computational Prediction of Drug Solubility in Fasted Simulated and Aspirated Human Intestinal Fluid

doi: 10.1007/s11095-014-1487-z

Figure Lengend Snippet: Consensus model performance. a

Article Snippet: The combination of the (ANN) FaSSIF model results (obtained from ADMET Predictor) with the PLS predictions increased the predictive power, as identified from the lowered RMSE te (Table ).

Techniques: