## 1 Introduction
**A condition for a point to belong to AD in leverage method:**  
\[h\leq h^{*}=3(p+1)/n \tag{1}\]  
where \(h\) is the leverage of a considered point, \(n\) is the number of molecules in the training set and \(p\) is the number of descriptors. The notion of AD is closely related to the notion of an \"outlier\" or \"anomaly\". Indeed, defining the AD frontier between \"normal\" and \"abnormal\" points is identical to detecting outliers. Many definitions of an outlier can be found in reported studies [9; 10; 11; 12]; however, a common consensus is that the term \"outlier\" generally refers to a point of an ensemble that is \"significantly different\" from the remaining points of the same ensemble. Due to their equivalence, the terms \"AD definition\" and \"outlier detection\" are therefore used interchangeably in this study. Additionally, three types of outliers can be distinguished in QSPR/QSAR studies, namely X-outliers, y-outliers and outliers towards the model (Model-outliers) [13]. X-outliers and y-outliers are outliers in the descriptor space X and the response space y, respectively. As for Model-outliers, they correspond to the molecules for which the properties are poorly predicted by the QSPR/QSAR model and can therefore be detected only after model construction. Note that a point can belong to different categories of outliers simultaneously [14]. Note also that for a new query molecule (i.e., unknown response), only X-outlier detection methods are applicable. More generally, even if the AD has an obvious role during the deployment of QSPR/QSAR models on new molecules, its definition remains essential at the different stages of the modeling procedure in which it fulfills different functions [1]. First, it is recommended to define the AD and eliminate the outliers as early as possible in a machine learning (ML) project, as some methods (e.g., dimensionality reduction, data scaling) are very sensitive to outliers. This initial AD definition step can be viewed as a data preprocessing step that removes outliers in order to improve the performance of the models. Then, during model construction, the AD definition can serve as a way to check whether the validation and test data are indeed within the AD defined by the training data, which is necessary as ML models are generally not reliable in case of extrapolation due to their data-driven nature. Finally, during model deployment, the AD can be used to judge the reliability of the prediction made by the model for a new query molecule. In this last case, as outliers are identified within new data, the outliers can also be referred to as novelties. This article is the second of a series of articles aiming to construct QSPR models on the basis of ML techniques, ML-QSPR, while examining the different choices that are offered to the developer along the way and analysing the effects of each one. In the first article of the series, two ML-QSPR models were developed to predict two thermodynamic properties, namely the enthalpy of formation (H) and the absolute entropy (S) for the ideal gas state of molecules at 298.15 K and 1 bar [15]. The molecular descriptors were employed as features of the models to describe the structural and atomic characteristics of the considered molecules of the DIPPR (Design Institute for Physical Properties) database. Within an objective of discovering new chemicals for various applications, a wide range of chemical structures were considered in the training data as means to increase the AD of the developed models. The retained models were two Lasso (Least Absolute Shrinkage and Selection Operator) linear models with 100 descriptors, selected from an initial ensemble of approximately 2500 descriptors through a feature selection step based on a genetic algorithm (GA). While the first article focused on the development of the QSPR approach from data collection to model construction, this second article aims at defining the AD of the developed models, characterized by their high dimensionality.