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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.
pkCSM a machine-learning platform to predict small-molecule pharmacokinetic properties, which relies on distance/pharmacophore patterns encoded as graph-based signatures.
Table of Contents. Predictive models implemented by pkCSM. Experimental Methods. Toxicophore SMARTS Queries. Macrocycles SMARTS Query. Case Studies. able S1: List of molecular prop. training the predictive models. . Table S2: Description of datasets used on building the pkCSM platform and validation protocols. employed.
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...
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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.
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 5, 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.
