Consensus hologram QSAR modeling for the prediction of human intestinal absorption.

Consistent in silico models for ADME properties are useful tools in early drug discovery. Here, we report the hologram QSAR modeling of human intestinal absorption using a dataset of 638 compounds with experimental data associated. The final validated models are consistent and robust for the consensus prediction of this important pharmacokinetic property and are suitable for virtual screening applications.

[1]  R. Yunes,et al.  Synthesis, biological evaluation, and molecular modeling of chalcone derivatives as potent inhibitors of Mycobacterium tuberculosis protein tyrosine phosphatases (PtpA and PtpB). , 2012, Journal of medicinal chemistry.

[2]  Adriano D. Andricopulo,et al.  PK/DB: database for pharmacokinetic properties and predictive in silico ADME models , 2008, Bioinform..

[3]  John L. LaMattina,et al.  Book Review: Drug Truths: Dispelling the Myths About Pharma R&D , 2008 .

[4]  Marcelo Santos Castilho,et al.  Discovery of New Inhibitors of Schistosoma mansoni PNP by Pharmacophore-Based Virtual Screening , 2010, J. Chem. Inf. Model..

[5]  B. Munos Lessons from 60 years of pharmaceutical innovation , 2009, Nature Reviews Drug Discovery.

[6]  Adriano D. Andricopulo,et al.  Fragment-based QSAR: perspectives in drug design , 2009, Molecular Diversity.

[7]  Sean Ekins,et al.  Evolving molecules using multi-objective optimization: applying to ADME/Tox. , 2010, Drug discovery today.

[8]  Peter Ertl,et al.  The graphical representation of ADME-related molecule properties for medicinal chemists. , 2011, Drug discovery today.

[9]  John G. Topliss,et al.  QSAR Model for Drug Human Oral Bioavailability1 , 2000 .

[10]  Adriano D Andricopulo,et al.  Hologram QSAR model for the prediction of human oral bioavailability. , 2007, Bioorganic & medicinal chemistry.

[11]  Carlos A. Montanari,et al.  In Silico Prediction of Human Plasma Protein Binding Using Hologram QSAR , 2007 .

[12]  Alexander Golbraikh,et al.  Predictive QSAR modeling workflow, model applicability domains, and virtual screening. , 2007, Current pharmaceutical design.

[13]  Alexander Golbraikh,et al.  Combinatorial QSAR Modeling of Specificity and Subtype Selectivity of Ligands Binding to Serotonin Receptors 5HT1E and 5HT1F , 2008, J. Chem. Inf. Model..

[14]  H. van de Waterbeemd,et al.  ADMET in silico modelling: towards prediction paradise? , 2003, Nature reviews. Drug discovery.

[15]  Gisbert Schneider,et al.  Virtual screening: an endless staircase? , 2010, Nature Reviews Drug Discovery.

[16]  John P. Overington,et al.  Probing the links between in vitro potency, ADMET and physicochemical parameters , 2011, Nature Reviews Drug Discovery.

[17]  Igor V. Tetko,et al.  Combinatorial QSAR Modeling of Chemical Toxicants Tested against Tetrahymena pyriformis , 2008, J. Chem. Inf. Model..

[18]  H. Waterbeemd Improving compound quality through in vitro and in silico physicochemical profiling. , 2009 .

[19]  Stefano Moro,et al.  Pharmaceutical Perspectives of Nonlinear QSAR Strategies , 2010, J. Chem. Inf. Model..