J
John C. Dearden
Researcher at Liverpool John Moores University
Publications - 120
Citations - 5981
John C. Dearden is an academic researcher from Liverpool John Moores University. The author has contributed to research in topics: Quantitative structure–activity relationship & Hydrogen bond. The author has an hindex of 39, co-authored 120 publications receiving 5375 citations. Previous affiliations of John C. Dearden include Aristotle University of Thessaloniki & Amirkabir University of Technology.
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Journal ArticleDOI
QSAR Modeling: Where have you been? Where are you going to?
Artem Cherkasov,Eugene N. Muratov,Eugene N. Muratov,Denis Fourches,Alexandre Varnek,Igor I. Baskin,Mark T. D. Cronin,John C. Dearden,Paola Gramatica,Yvonne C. Martin,Roberto Todeschini,Viviana Consonni,Victor E. Kuz’min,Richard D. Cramer,Romualdo Benigni,Chihae Yang,James F. Rathman,Lothar Terfloth,Johann Gasteiger,Ann M. Richard,Alexander Tropsha +20 more
TL;DR: In this paper, the authors provide guidelines for QSAR development, validation, and application, which are summarized in best practices for building rigorously validated and externally predictive quantitative structure-activity relationship models.
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How not to develop a quantitative structure–activity or structure–property relationship (QSAR/QSPR)
TL;DR: 21 types of error that continue to be perpetrated in the QSAR/QSPR literature are identified and each is discussed, with examples (including some of the authors' own).
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Quantitative structure-permeability relationships (QSPRs) for percutaneous absorption.
TL;DR: Current QSPR models provide a significant tool for assessing the percutaneous penetration of chemicals and may be important in determining the bioavailability of a range of topically applied exogenous chemicals, and in issues of dermal toxicology and risk assessment.
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The Measurement of Partition Coefficients
John C. Dearden,G. M. Bresnen +1 more
TL;DR: In this paper, the authors examined the factors that can affect the measurement of partition coefficient, and made recommendations as to good practice, and recommended that partitioning be carried out at constant temperature using either a stirred flask technique or the filter probe.
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In silico prediction of drug toxicity.
TL;DR: A number of expert systems are available for toxicity prediction, most of them covering a range of toxicity endpoints, and comparative tests of the ability of these systems to predict carcinogenicity show that improvement is still needed.