Estimation of pKa Using Semiempirical Molecular Orbital Methods. Part 2: Application to Amines, Anilines and Various Nitrogen Containing Heterocyclic Compounds.

Tehan, Benjamin G.; Lloyd, Edward John; Wong, Margaret G.; Pitt, William R.; Gancia, Emanuela; Manallack, David T.

doi:10.1002/1521-3838(200211)21:5<473::aid-qsar473>3.0.co;2-d

Cited by 77 publications

(79 citation statements)

References 14 publications

(35 reference statements)

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“…Kernel-based machine learning was recently applied to pK a prediction models. For example, Rupp et al (2010) used graph kernels and kernel ridge regression to treat molecules based on their graph representation and developed a model showing an accuracy that was comparable with that of the semi-empirical models of Tehan et al (2002a, b). As partial atomic charge is an important descriptor for molecular pK a , Vařeková et al (2013) used an electronegativity equalization method (EEM) to develop QSPR models for pK a prediction (Jirouskova et al 2009).…”

Section: Ionization Constantmentioning

confidence: 99%

In silicoADME/T modelling for rational drug design

Wang

Xing

Yue

et al. 2015

Quart. Rev. Biophys.

247

155

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Abstract. In recent decades, in silico absorption, distribution, metabolism, excretion (ADME), and toxicity (T) modelling as a tool for rational drug design has received considerable attention from pharmaceutical scientists, and various ADME/T-related prediction models have been reported. The high-throughput and low-cost nature of these models permits a more streamlined drug development process in which the identification of hits or their structural optimization can be guided based on a parallel investigation of bioavailability and safety, along with activity. However, the effectiveness of these tools is highly dependent on their capacity to cope with needs at different stages, e.g. their use in candidate selection has been limited due to their lack of the required predictability. For some events or endpoints involving more complex mechanisms, the current in silico approaches still need further improvement. In this review, we will briefly introduce the development of in silico models for some physicochemical parameters, ADME properties and toxicity evaluation, with an emphasis on the modelling approaches thereof, their application in drug discovery, and the potential merits or deficiencies of these models. Finally, the outlook for future ADME/T modelling based on big data analysis and systems sciences will be discussed.

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Section: Ionization Constantmentioning

confidence: 99%

In silicoADME/T modelling for rational drug design

Wang

Xing

Yue

et al. 2015

Quart. Rev. Biophys.

247

155

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“…We use a published data set of structures and pK a measurements compiled from the literature by Tehan et al [6,21] (a curated subset of the PhysProp database [22]). The data set (Table 1) contains 698 compounds (416 acids, 282 bases), partitioned into 6 ?…”

Section: Data and Descriptorsmentioning

confidence: 99%

“…With regard to molecular representation, we therefore limit ourselves to an established pK a descriptor, electrophilic superdelocalizability (SE). This quantummechanical descriptor is based on frontier electron theory [23], and has been shown to be well-suited for pK a prediction [6,20]. It is defined as…”

Section: Data and Descriptorsmentioning

confidence: 99%

“…Structures and pK a values of the articles by Tehan et al [6,12] are available at the ''Online Chemical Modeling Environment'' [26] (http://www.ochem.eu, accessed 2012-05-08). Matlab (version 7.6.0, The MathWorks, http://www.mathworks.com) source code for STL and MTL Gaussian process regression can be downloaded from the authors web pages at http://homepages.inf.ed.ac.uk/ gsanguin/software.html, and, http://www.mrupp.info.…”

Section: Data and Descriptorsmentioning

confidence: 99%

“…Consider, e.g., orthosubstituted phenols. Division into two classes, those that can form internal hydrogen bonds, and those that can not, improved certain linear pK a models [6]. However, each class contains information about the other one that separate models don't use.…”

Section: Introductionmentioning

confidence: 99%

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Multi-task learning for pKa prediction

Skolidis

Hansen

Sanguinetti

et al. 2012

J Comput Aided Mol Des

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Many compound properties depend directly on the dissociation constants of its acidic and basic groups. Significant effort has been invested in computational models to predict these constants. For linear regression models, compounds are often divided into chemically motivated classes, with a separate model for each class. However, sometimes too few measurements are available for a class to build a reasonable model, e.g., when investigating a new compound series. If data for related classes are available, we show that multi-task learning can be used to improve predictions by utilizing data from these other classes. We investigate performance of linear Gaussian process regression models (single task, pooling, and multitask models) in the low sample size regime, using a published data set (n = 698, mostly monoprotic, in aqueous solution) divided beforehand into 15 classes. A multi-task regression model using the intrinsic model of co-regionalization and incomplete Cholesky decomposition performed best in 85 % of all experiments. The presented approach can be applied to estimate other molecular properties where few measurements are available.

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General and Theoretical Aspects of Anilines

Nguyen

2009

Patai's Chemistry of Functional Groups

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Estimation of pKa Using Semiempirical Molecular Orbital Methods. Part 2: Application to Amines, Anilines and Various Nitrogen Containing Heterocyclic Compounds.

Cited by 77 publications

References 14 publications

In silicoADME/T modelling for rational drug design

In silicoADME/T modelling for rational drug design

Multi-task learning for pKa prediction

General and Theoretical Aspects of Anilines

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