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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%
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