Advances in multi-parameter optimisation methods for de novo drug design
Summary This review article discusses recent developments in the methods and opinions around multi-parameter optimisation, focusing on applications to de novo drug…
The scoring profiles were developed in collaboration with expert DMPK scientists. It is based on their experience of successful drug discovery and the projects on which they have worked using StarDrop. You can find an explanation of the principles behind the property criteria in Section 16.7 of the StarDrop User Guide*.
You may also be interested in the review article that we published in Curr. Pharm. Des. (2012) 18(9) pp. 1292-1310 discussing methods for MPO. Several other publications and resources on the topic can also be found on our website at optibrium.com/topic/multi-parameter-optimisation/
The principles of the Probabilistic Scoring method for MPO in StarDrop are described in Chapter 2 of the StarDrop Reference Guide*.
*Both guides can be accessed from the Help menu in StarDrop or on our documentation page.
More MPO resources
Finding balance in drug discovery through multi-parameter optimisation
Successful drugs require a delicate balance of many properties, such as potency, ADME and toxicity, to meet a project’s therapeutic objective. To make decisions about compound progression and assay selection, the available data must be assessed against project-specific criteria. However, the data on which we base our decisions often come from different sources and can vary in quality, so how can we use this information to make confident decisions? In addition, how can we be sure that the criteria we’re using are the most appropriate?
Compound prioritisation and selection
In this demo we’re going to take a look at how StarDrop can guide the prioritisation and selections of compounds using a combination of in vitro and in silico data.
