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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.
pkCSM a machine-learning platform to predict small-molecule pharmacokinetic properties, which relies on distance/pharmacophore patterns encoded as graph-based signatures.
Oct 16, 2021 · pkCSM 67 presents graph-based modeling for the prediction of pharmacokinetic and toxicological properties. In its implementation, models were constructed to predict thirty ADME properties, presenting 14 regression and 16 classification models.
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 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...
Oct 1, 2021 · The pkCSM is an authentic source (collaboratively developed by Instituto Rene Rachou Fiocruz Minas, The University of Melbourne and University of Cambridge) to predict small-molecule pharmacokinetics using graph-based signatures.
