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Here we propose a novel approach to the prediction of pharmacokinetic properties, called pkCSM, which relies on graph-based signatures. These encode distance patterns between atoms and are used to represent the small molecule and to train predictive models.
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: predicting small-molecule pharmacokinetic properties using graph-based signatures. Douglas E. V. Pires*, Tom L. Blundell and David B. Ascher.
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 provides a user-friendly and quick web interface to generate ADMET predictions for up to 100 given chemical compounds at a time, developed using cutting edge frameworks (the front-end uses Bootstrap 2.0 and the back-end was implemented in Python, using Flask (0.10.1).
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Apr 22, 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.
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.
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.
