article Open AccessTop 1% cited
pkCSM: Predicting Small-Molecule Pharmacokinetic and Toxicity Properties Using Graph-Based Signatures
Journal of Medicinal Chemistry · 2015 · Vol. 58(9) · pp. 4066–4072
Abstract
Drug development has a high attrition rate, with poor pharmacokinetic and safety properties a significant hurdle. Computational approaches may help minimize these risks. We have developed a novel approach (pkCSM) which uses graph-based signatures to develop predictive models of central ADMET properties for drug development. pkCSM performs as well or better than current methods. A freely accessible web server (http://structure.bioc.cam.ac.uk/pkcsm), which retains no information submitted to it, provides an integrated platform to rapidly evaluate pharmacokinetic and toxicity properties.
Computational Drug Discovery MethodsMachine Learning in Materials ScienceClick Chemistry and ApplicationsPharmacokineticsChemistryToxicityAttritionGraphComputer sciencePharmacologyTheoretical computer science
MeSH terms
AnimalsComputer GraphicsCyprinidaeHumansModels, TheoreticalPharmacokineticsSoftwareTetrahymena pyriformisUser-Computer InterfaceDrug DesignToxicity TestsCaco-2 CellsRatsSmall Molecule LibrariesDatabases, Pharmaceutical
Funding
- Wellcome
- Wellcome Trust
- Research Councils UK
- University of Cambridge
- Conselho Nacional de Desenvolvimento Científico e Tecnológico
- Fundação de Amparo à Pesquisa do Estado de Minas Gerais
- Fundação Oswaldo Cruz
- Conselho Nacional das Fundações Estaduais de Amparo à Pesquisa
- Medical Research Council
- National Health and Medical Research Council
Citations
5,075
FWCI
44.14
field-weighted impact
References
65
Percentile
100%
vs. same field & year
Citations per year
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Journal of Medicinal Chemistry · 2010 · 3,863 citations
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ADMET in silico modelling: towards prediction paradise?
Nature Reviews Drug Discovery · 2003 · 1,819 citations
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