Applicability domain: towards a more formal definition^$

^$Presented at the 17th International Conference on QSAR in Environmental and Health Sciences (QSAR 2016), 13–17 June 2016, Miami Beach, FL, USA.

Abstract

In recent years the applicability domain (AD) of a prediction system has become an important concern in (Q)SAR modelling, especially in the context of human safety assessment. Today AD is an active research topic, and many methods have been designed to estimate the adequacy of a model and the confidence in its outcome for a given prediction task. Unfortunately, the wide spectrum of techniques developed for this purpose is based on various definitions of the concept of AD, often taking into account different types of information. This variety of methodologies confuses the end users and makes the comparison of the AD for different models almost impossible. In this article, we demonstrate that AD is not a monolithic concept and can be broken down into three well-defined sub-domains assessing confidence at the model, prediction and decision levels, respectively. By leveraging this separation of concerns we have an opportunity to clarify, formalize and extend the definition of AD. We propose a framework that captures this new vision with the aim to initiate a global effort to converge towards a common AD definition within the (Q)SAR community.

Keywords:

• Applicability domain
• decision domain
• TARDIS
• QSAR
• machine learning
• confidence modelling

Notes

^$ Presented at the 17th International Conference on QSAR in Environmental and Health Sciences (QSAR 2016), 13–17 June 2016, Miami Beach, FL, USA.