In Silico Predictions of Human Skin Permeability using Nonlinear Quantitative Structure–Property Relationship Models

Baba, Hiromi; Takahara, Jun-ichi; Mamitsuka, Hiroshi

doi:10.1007/s11095-015-1629-y

Cited by 46 publications

(48 citation statements)

References 53 publications

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“…Of various ML schemes51, support vector machine (SVM), which was invented by Vapnik et al . in 199552 and has been extensively applied to a broad range of studies535455, performs better than any other ML techniques, such as ANN, genetic algorithm (GA), and random forest (RF) as demonstrated by empirical studies5657, suggesting that an SVM-based model can actually perform better than any other ML-based schemes in selecting/ranking docked poses.…”

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confidence: 99%

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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confidence: 99%

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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“…As the major data sources, our previous dataset (Zhang et al, 2012) and the dataset of Baba et al (2015).…”

Section: Resultsmentioning

confidence: 99%

“…6 compare favorably with those reported for equations that deal only with neutral species. One of the most recent, and successful, models for skin permeation is that of Baba et al (2015) who used an SVM-Gaussian method in which 2732 descriptors per compound were initially computed.…”

Section: Resultsmentioning

confidence: 99%

“…Various kinds of empirical and mathematical models for the correlation and prediction of human skin permeability, as log Kp, have been reported (Baba et al, 2015;Mitragotri, 2003), and there have been a number of reviews of these models (Chen et al, 2013;Geinoz et al, 2004;Mitragotri et al, 2011;Moss et al, 2011;Neely et al, 2009). It has been known for years that the permeability of ionizable compounds depends on the pH of the donor solution, attributed to the slower rate of permeation of ionic species compared to the corresponding neutral species (Roy and Flynn, 1990;Swarbrick et al, 1984;Waters and Bhuiyan, 2016).…”

Section: Introductionmentioning

confidence: 99%

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An equation for the prediction of human skin permeability of neutral molecules, ions and ionic species

Zhang

Abraham

Liu

2017

International Journal of Pharmaceutics

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Accepted ManuscriptThis is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

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“…Based on a non-exhaustive assessment of several different end points relevant to pharmaceutical research, while it appears that most have seen utilization of Bayesian or SVM approaches to develop predictive models, few have so far utilized DL (Table 2). Recent examples of computational models appearing in this journal alone over the past 18 months include: modeling thermodynamic proxies (35), predicting mouse liver microsomal stability (36), predicting autooxidation (37), drug solubility in human intestinal fluid (38), site of metabolism prediction in CYP2C9 (39), human skin permeability prediction (40, 41), blood brain barrier penetration modeling (42), predicting clearance mechanism (41) and skin concentration due to dermal exposure (43). Many of these datasets could likely utilize and benefit from DL and it would be of interest to see for how many an improvement in predictions could be obtained.…”

Section: Pharmaceutical Applications Of Deep Learningmentioning

confidence: 99%

The Next Era: Deep Learning in Pharmaceutical Research

2016

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Over the past decade we have witnessed the increasing sophistication of machine learning algorithms applied in daily use from internet searches, voice recognition, social network software to machine vision software in cameras, phones, robots and self-driving cars. Pharmaceutical research has also seen its fair share of machine learning developments. For example, applying such methods to mine the growing datasets that are created in drug discovery not only enables us to learn from the past but to predict a molecule’s properties and behavior in future. The latest machine learning algorithm garnering significant attention is deep learning, which is an artificial neural network with multiple hidden layers. Publications over the last 3 years suggest that this algorithm may have advantages over previous machine learning methods and offer a slight but discernable edge in predictive performance. The time has come for a balanced review of this technique but also to apply machine learning methods such as deep learning across a wider array of endpoints relevant to pharmaceutical research for which the datasets are growing such as physicochemical property prediction, formulation prediction, absorption, distribution, metabolism, excretion and toxicity (ADME/Tox), target prediction and skin permeation, etc. We also show that there are many potential applications of deep learning beyond cheminformatics. It will be important to perform prospective testing (which has been carried out rarely to date) in order to convince skeptics that there will be benefits from investing in this technique.

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In Silico Predictions of Human Skin Permeability using Nonlinear Quantitative Structure–Property Relationship Models

Abstract: We provided one of the largest datasets with purely experimental log kp and developed reliable and accurate prediction models for screening active ingredients and seeking unsynthesized compounds of dermatological medicines and cosmetics.

Cited by 46 publications

References 53 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

An equation for the prediction of human skin permeability of neutral molecules, ions and ionic species

The Next Era: Deep Learning in Pharmaceutical Research

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