This article discusses a critical issue that the community needs to address address in order to use the predictive models that we build to the greatest effect.
To understand how useful predictive models are in a practical context, we need to understand ‘prior probability distributions’ (or ‘priors’ for short). This article discusses the importance of ‘priors’ in assessing models in different contexts, including selecting or eliminating compounds, prioritising compounds for further investigation, and combining models for different properties to select compounds with a balance of properties.
In all cases, understanding prior probabilities of adverse events makes it easier to make good decisions in drug discovery, so we can more efficiently find high quality compounds to progress.
Matthew Segall and Andrew Chadwick, J. Comput.- Aided Mol. Des., 2010, 24, 957–960.
Find out more
Read the full article via the journal webpage below.
INTERESTED IN DRUG OPTIMIZATION?
With its comprehensive suite of integrated software, StarDrop™ delivers best-in-class in silico technologies within a highly visual and user-friendly interface. StarDrop™ enables a seamless flow from the latest data through predictive modelling to decision-making regarding the next round of synthesis and research, improving the speed, efficiency, and productivity of the drug optimisation and discovery process.