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ICSYNTH light Plug-in for StarDrop

Sunday, 17 March 2019 11:23

This script enables you to access ICSYNTH light to evaluate possible synthetic pathways for new molecules designed in StarDrop’s interactive designer or Nova module. ICSYNTH light uses a transform-based approach to generate synthesis pathways for target molecules, even if these are novel and/or unpublished. This script will display the possible synthetic pathways in a web browser.

To learn more about this plug-in watch our joint Webinar with InfoChem.

The installer files are provided in the following pages...


A Novel Scoring Profile for the Design of Antibacterials Active Against Gram-Negative Bacteria

Wednesday, 28 November 2018 16:08

In the poster “A Novel Scoring Profile for the Design of Antibacterials Active Against Gram-Negative Bacteria” by Bailey Montefiore, Franca Klingler and Nick Foster, the authors used the rule induction method to define a multi-parameter scoring profile to guide the generation of novel antibacterials active against gram-negative bacteria. The profile developed uses nine ADME QSAR predicted properties

  • Topological Polar surface area
  • Flexibility
  • logD
  • Molecular weight
  • Number of hydrogen bond acceptors
  • Protein Binding
  • Blood brain barrier penetration
  • Solubility
  • hERG affinity



Quantitative Estimate of Drug-likeness in StarDrop

Thursday, 21 February 2013 01:29

In the article, Bickerton et al. (2012) “The Chemical Beauty of Drugs” Nature Chemistry 4, 90–98, the authors proposed a measure of ‘drug-likeness’, the Quantitative Estimate of Drug-likeness (QED), that relates the similarity of a compound’s properties to those of oral drugs based on eight commonly used molecular properties:

  • Molecular weight (MW)
  • Lipophilicity (logP)
  • Number of hydrogen bond donors (HBD)
  • Number of hydrogen bond acceptors (HBA)
  • Polar surface area (PSA)
  • Number of rotatable bonds (ROTB)
  • Number of aromatic rings (AROM)
  • Count of alerts for undesirable substructures (ALERTS)

The QED is based on a method for multi-parameter optimisation known as ‘desirability functions’. A desirability function relates the value of a compound characteristic to the ‘desirability’ of that outcome. The desirability is a number between zero and one, where a value of one indicates that the outcome is ideal and a value of zero indicates that the outcome is completely unacceptable. Desirability functions are equivalent to the scoring functions used in StarDrop’s Probabilistic Scoring approach, making this approach very amenable to implementation in StarDrop.



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