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The pkCSM signatures were successfully used across five main different pharmacokinetic properties classes to develop predictive regression and classification models. We show that pkCSM performs as well or better across different pharmacokinetic properties than other freely available methods.
Apr 10, 2015 · 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.
May 14, 2015 · 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.
How to use pkCSM About pkCSM pkCSM a machine-learning platform to predict small-molecule pharmacokinetic properties, which relies on distance/pharmacophore patterns encoded as graph-based signatures. The platform is composed of 22 regression and classification models, trained and tested on different experimental data sets encompassing a diverse and
pkCSM: predicting small-molecule pharmacokinetic and toxicity. properties using graph-based signatures. Douglas E. V. Pires1,2,‡,*, Tom L. Blundell1, David B. Ascher1,‡,* Table of Contents. Predictive models implemented by pkCSM. Experimental Methods. Toxicophore SMARTS Queries. Macrocycles SMARTS Query. Case Studies.
Jan 20, 2015 · 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.
Mar 29, 2018 · In silico approaches offer a cost and time-effective approach to rapidly screen and optimize pharmacokinetic and toxicity properties. Here we demonstrate the use of the comprehensive analysis system pkCSM, to allow early identification of potential problems, prioritization of hits, and optimization of leads.
pkCSM: predicting small-molecule pharmacokinetic properties using graph-based signatures. Douglas E. V. Pires*, Tom L. Blundell and David B. Ascher*. *Correspondence: douglas.pires@cpqrr.fiocruz.br , dascher@svi.edu.au.
Apr 18, 2024 · We collected the experimental ADMET data from four pharmacokinetic prediction methods, namely ADMETlab 2.0 , Interpretable-ADMET , toxCSM and pkCSM .
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