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pkCSM: Predicting Small-Molecule Pharmacokinetic and Toxicity Properties Using Graph-Based Signatures

[Image: see text] 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 cen...

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Detalles Bibliográficos
Autores principales: Pires, Douglas E. V., Blundell, Tom L., Ascher, David B.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: American Chemical Society 2015
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4434528/
https://www.ncbi.nlm.nih.gov/pubmed/25860834
http://dx.doi.org/10.1021/acs.jmedchem.5b00104
Descripción
Sumario:[Image: see text] 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.