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. 2015 May 14;58(9):4066-72.
doi: 10.1021/acs.jmedchem.5b00104. Epub 2015 Apr 22.

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

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

Douglas E V Pires et al. J Med Chem. .

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.

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Figures

Figure 1
Figure 1
pkCSM workflow. Given an input molecule, two main sources of information are used to train and test machine learning-based predictors: compound general properties (including molecular properties, toxicophores and pharmacophore) and distance-based graph signatures.
Figure 2
Figure 2
Regression analysis for absorption predictors considering cross-validation schemes. Pearson’s correlation coefficients and standard error are also shown at the top-left corner. The left graph shows the correlation between experimental and predicted values for Caco2 permeability, while the graph on the right for water solubility.

References

    1. Kola I.; Landis J. Can the pharmaceutical industry reduce attrition rates?. Nature Rev. Drug Discovery 2004, 3, 711–715. - PubMed
    1. Merlot C. Computational toxicology—a tool for early safety evaluation. Drug Discovery Today 2010, 15, 16–22. - PubMed
    1. Eddershaw P. J.; Beresford A. P.; Bayliss M. K. ADME/PK as part of a rational approach to drug discovery. Drug Discovery Today 2000, 5, 409–414. - PubMed
    1. Li A. P. Screening for human ADME/Tox drug properties in drug discovery. Drug Discovery Today 2001, 6, 357–366. - PubMed
    1. Lin J.; Sahakian D. C.; de Morais S. M.; Xu J. J.; Polzer R. J.; Winter S. M. The role of absorption, distribution, metabolism, excretion and toxicity in drug discovery. Curr. Top. Med. Chem. 2003, 3, 1125–1154. - PubMed

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