Applicability Domain for Trustable Predictions

In the rapidly evolving landscape of artificial intelligence (AI) and machine learning (ML), understanding and correctly applying the concept of the applicability domain (AD) has emerged as an essential part. This chapter begins with an introduction and background on the critical area of AD. It dives into the definition and different methodologies associated with the applicability domain, laying a solid foundation for further exploration. A detailed examination of AD’s role within the framework of AI and ML is undertaken, supported by in-depth theoretical foundations. The paper then proceeds to delineate the various measures of AD in AI and ML, offering insights into methods like DA index (κ, γ, δ), class probability estimation, and techniques involving local vicinity, boosting, classification neural networks, and subgroup discovery (SGD), among others. We also discussed a series of AD methods employed in Quantitative Structure-Activity Relationship (QSAR) studies. Lastly, the diverse applications of AD are addressed, underlining its widespread influence across different sectors. This chapter is intended to offer a thorough understanding of AD and its applications, particularly in AI and ML, leading to more informed research and decision-making in these fields as a good amount of literature already exists regarding AD of QSAR modeling.