Literature DB >> 16846797

Can we estimate the accuracy of ADME-Tox predictions?

Igor V Tetko1, Pierre Bruneau, Hans-Werner Mewes, Douglas C Rohrer, Gennadiy I Poda.   

Abstract

There have recently been developments in the methods used to access the accuracy of the prediction and applicability domain of absorption, distribution, metabolism, excretion and toxicity models, and also in the methods used to predict the physicochemical properties of compounds in the early stages of drug development. The methods are classified into two main groups: those based on the analysis of similarity of molecules, and those based on the analysis of calculated properties. An analysis of octanol-water distribution coefficients is used to exemplify the consistency of estimated and calculated accuracy of the ALOGPS program (http://www.vcclab.org) to predict in-house and publicly available datasets.

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Year:  2006        PMID: 16846797     DOI: 10.1016/j.drudis.2006.06.013

Source DB:  PubMed          Journal:  Drug Discov Today        ISSN: 1359-6446            Impact factor:   7.851


  38 in total

1.  Estimating the domain of applicability for machine learning QSAR models: a study on aqueous solubility of drug discovery molecules.

Authors:  Timon Sebastian Schroeter; Anton Schwaighofer; Sebastian Mika; Antonius Ter Laak; Detlev Suelzle; Ursula Ganzer; Nikolaus Heinrich; Klaus-Robert Müller
Journal:  J Comput Aided Mol Des       Date:  2007-12-01       Impact factor: 3.686

2.  Estimating the domain of applicability for machine learning QSAR models: a study on aqueous solubility of drug discovery molecules.

Authors:  Timon Sebastian Schroeter; Anton Schwaighofer; Sebastian Mika; Antonius Ter Laak; Detlev Suelzle; Ursula Ganzer; Nikolaus Heinrich; Klaus-Robert Müller
Journal:  J Comput Aided Mol Des       Date:  2007-07-14       Impact factor: 3.686

Review 3.  Chemical predictive modelling to improve compound quality.

Authors:  John G Cumming; Andrew M Davis; Sorel Muresan; Markus Haeberlein; Hongming Chen
Journal:  Nat Rev Drug Discov       Date:  2013-12       Impact factor: 84.694

4.  Cardio-vascular safety beyond hERG: in silico modelling of a guinea pig right atrium assay.

Authors:  Luca A Fenu; Ard Teisman; Stefan S De Buck; Vikash K Sinha; Ron A H J Gilissen; Marjoleen J M A Nijsen; Claire E Mackie; Wendy E Sanderson
Journal:  J Comput Aided Mol Des       Date:  2009-11-05       Impact factor: 3.686

5.  How "drug-like" are naturally occurring anti-cancer compounds?

Authors:  Fidele Ntie-Kang; Lydia L Lifongo; Philip N Judson; Wolfgang Sippl; Simon M N Efange
Journal:  J Mol Model       Date:  2014-01-24       Impact factor: 1.810

6.  Reliably assessing prediction reliability for high dimensional QSAR data.

Authors:  Jianping Huang; Xiaohui Fan
Journal:  Mol Divers       Date:  2012-12-19       Impact factor: 2.943

7.  Prediction of drug distribution in rat and humans using an artificial neural networks ensemble and a PBPK model.

Authors:  Paulo Paixão; Natália Aniceto; Luís F Gouveia; José A G Morais
Journal:  Pharm Res       Date:  2014-05-28       Impact factor: 4.200

8.  Deficiencies in the reporting of VD and t(1/2) in the FDA approved chemotherapy drug inserts.

Authors:  Malcolm J D'Souza; Ghada J Alabed
Journal:  Pharm Rev       Date:  2010-02-03

9.  Evaluation of computational docking to identify pregnane X receptor agonists in the ToxCast database.

Authors:  Sandhya Kortagere; Matthew D Krasowski; Erica J Reschly; Madhukumar Venkatesh; Sridhar Mani; Sean Ekins
Journal:  Environ Health Perspect       Date:  2010-06-17       Impact factor: 9.031

10.  Challenges predicting ligand-receptor interactions of promiscuous proteins: the nuclear receptor PXR.

Authors:  Sean Ekins; Sandhya Kortagere; Manisha Iyer; Erica J Reschly; Markus A Lill; Matthew R Redinbo; Matthew D Krasowski
Journal:  PLoS Comput Biol       Date:  2009-12-11       Impact factor: 4.475

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