In silico Prediction of Aqueous Solubility: a Comparative Study of Local and Global Predictive Models

Raevsky, Oleg A.; Polianczyk, Daniel E.; Va, Grigor'ev; Raevskaja, Olga E.; Dearden, John C.

doi:10.1002/minf.201400144

Cited by 28 publications

(11 citation statements)

References 58 publications

(37 reference statements)

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“…This is completely consistent with the fact that customized ML-based scoring functions perform better than general linear scoring functions42. Furthermore, SVM performs better than other ML-based schemes, namely ANN, GFA, and RF56116.…”

Section: Discussionsupporting

confidence: 83%

Prediction of N-Methyl-D-Aspartate Receptor GluN1-Ligand Binding Affinity by a Novel SVM-Pose/SVM-Score Combinatorial Ensemble Docking Scheme

Leong

Syu

Ding

et al. 2017

Sci Rep

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The glycine-binding site of the N-methyl-D-aspartate receptor (NMDAR) subunit GluN1 is a potential pharmacological target for neurodegenerative disorders. A novel combinatorial ensemble docking scheme using ligand and protein conformation ensembles and customized support vector machine (SVM)-based models to select the docked pose and to predict the docking score was generated for predicting the NMDAR GluN1-ligand binding affinity. The predicted root mean square deviation (RMSD) values in pose by SVM-Pose models were found to be in good agreement with the observed values (n = 30, r2 = 0.928–0.988, = 0.894–0.954, RMSE = 0.002–0.412, s = 0.001–0.214), and the predicted pKi values by SVM-Score were found to be in good agreement with the observed values for the training samples (n = 24, r2 = 0.967, = 0.899, RMSE = 0.295, s = 0.170) and test samples (n = 13, q2 = 0.894, RMSE = 0.437, s = 0.202). When subjected to various statistical validations, the developed SVM-Pose and SVM-Score models consistently met the most stringent criteria. A mock test asserted the predictivity of this novel docking scheme. Collectively, this accurate novel combinatorial ensemble docking scheme can be used to predict the NMDAR GluN1-ligand binding affinity for facilitating drug discovery.

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Section: Discussionsupporting

confidence: 83%

Prediction of N-Methyl-D-Aspartate Receptor GluN1-Ligand Binding Affinity by a Novel SVM-Pose/SVM-Score Combinatorial Ensemble Docking Scheme

Leong

Syu

Ding

et al. 2017

Sci Rep

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“…They report that the multimodel approach can enlarge the applicability domain given that more accurate results for solubility prediction were obtained in comparison to using individual models. This approach agrees with other reports that consensus of local QSAR models can generate predictive workflows, especially for datasets with large structural diversity [58,59]. It is worth noting that Lipinski himself recently revisited his own rules [60], in vision of new potential classes of drugs, such as natural products, peptide-like, and fragments, which, despite the validated effect, would defy the original Ro5 limits.…”

Section: Aqueous Solubility and Lipophilicitysupporting

confidence: 87%

ADME Profiling in Drug Discovery and a New Path Paved on Silica

Krüger¹,

Wrenger²,

Kronenberger³

2020

Drug Discovery and Development - New Advances

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The drug discovery and development pipeline have more and more relied on in vitro testing and in silico predictions to reduce investments and optimize lead compounds. A comprehensive set of in vitro assays is available to determine key parameters of absorption, distribution, metabolism, and excretion, for example, lipophilicity, solubility, and plasma stability. Such test systems aid the evaluation of the pharmacological properties of a compound and serve as surrogates before entering in vivo testing and clinical trials. Nowadays, computer-aided techniques are employed not just in the discovery of new lead compounds but embedded as part of the entire drug development process where the ADME profiling and big data analyses add a new layer of complexity to those systems. Herein, we give a short overview of the history of the drug development pipeline presenting state-of-theart ADME in vitro assays as established in academia and industry. We will further introduce the underlying good practices and give an example of the compound development pipeline. In the next step, recent advances at in silico techniques will be highlighted with special emphasis on how pharmacogenomics and in silico PK profiling can enhance drug monitoring and individualization of drug therapy.

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“…Many of these solvation/hydration methodologies have been summarized in a recent review by Skyner et al 19 Commonly, QSPR methods are employed due to their speed, convenience and accuracy, when provided with a suitable training dataset. 10,[20][21][22]23 These methods represent the current state-of-the-art in practical solubility prediction. However, QSPR methods lack the theoretical basis of a fundamental physical theory, hence limiting their interpretability and the understanding that can be gained from their use.…”

Section: Introductionmentioning

confidence: 99%

Are the Sublimation Thermodynamics of Organic Molecules Predictable?

McDonagh

Palmer

Mourik

et al. 2016

J. Chem. Inf. Model.

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We compare a range of computational methods for the prediction of sublimation thermodynamics (enthalpy, entropy and free energy of sublimation). These include a model from theoretical chemistry that utilizes crystal lattice energy minimization (with the DMACRYS program) and QSPR models generated by both machine learning (Random Forest and Support Vector Machines) and regression (Partial Least Squares) methods. Using these methods we investigate the predictability of the enthalpy, entropy and free energy of sublimation, with consideration of whether such a method may be able to improve solubility prediction schemes. Previous work has suggested that the major source of error in solubility prediction schemes involving a thermodynamic cycle via the solid state is in the modeling of the free energy change away from the solid state. Yet contrary to this conclusion other work has found that the inclusion of terms such as the enthalpy of sublimation in QSPR methods 2 does not improve the predictions of solubility. We suggest the use of theoretical chemistry terms, detailed explicitly in the methods section, as descriptors for the prediction of the enthalpy and free energy of sublimation. A dataset of 158 molecules with experimental sublimation thermodynamics values and some CSD refcodes has been collected from the literature and is provided with their original source references.

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In silico Prediction of Aqueous Solubility: a Comparative Study of Local and Global Predictive Models

Cited by 28 publications

References 58 publications

Prediction of N-Methyl-D-Aspartate Receptor GluN1-Ligand Binding Affinity by a Novel SVM-Pose/SVM-Score Combinatorial Ensemble Docking Scheme

Prediction of N-Methyl-D-Aspartate Receptor GluN1-Ligand Binding Affinity by a Novel SVM-Pose/SVM-Score Combinatorial Ensemble Docking Scheme

ADME Profiling in Drug Discovery and a New Path Paved on Silica

Are the Sublimation Thermodynamics of Organic Molecules Predictable?

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