A Stepwise Approach for Defining the Applicability Domain of SAR and QSAR Models

Abstract

A stepwise approach for determining the model applicability domain is proposed. Four stages are applied to account for the diversity and complexity of the current SAR/QSAR models, reflecting their mechanistic rationality (including metabolic activation of chemicals) and transparency. General parametric requirements are imposed in the first stage, specifying in the domain only those chemicals that fall in the range of variation of the physicochemical properties of the chemicals in the training set. The second stage defines the structural similarity between chemicals that are correctly predicted by the model. The structural neighborhood of atom-centered fragments is used to determine this similarity. The third stage in defining the domain is based on a mechanistic understanding of the modeled phenomenon. Here, the model domain combines the reliability of specific reactive groups hypothesized to cause the effect and the domain of explanatory variables determining the parametric requirements in order for functional groups to elicit their reactivity. Finally, the reliability of simulated metabolism (metabolites, pathways, and maps) is taken into account in assessing the reliability of predictions, if metabolic activation of chemicals is a part of the (Q)SAR model. Some of the stages of the proposed approach for defining the model domain can be eliminated depending on the availability and quality of the experimental data used to derive the model, the specificity of (Q)SARs, and the goals of their ultimate application. The performance of the proposed definition of the model domain is tested using several examples of (Q)SARs that have been externally validated, including models for predicting acute toxicity, skin sensitization, and biodegradation. The results clearly showed that credibility in predictions of QSAR models for chemicals belonging to their domain is much higher than for chemicals outside this domain.

Assessing Applicability Domains of Toxicological QSARs: Definition, Confidence in Predicted Values, and the Role of Mechanisms of Action

“…Skew within the descriptor space has led [19] to suggest that, in addition to a "general requirements" domain (which includes, but is not limited to, molecular properties) and a structural domain, a "mechanistic domain" is also required. This study indicates that an understanding of the mechanistic domain is the most critical criterion to understand the accuracy of, and hence the confidence that may be placed in, predictions.…”

Estimation of Compartmental Half‐lives of Organic Compounds – Structural Similarity versus EPI‐Suite

“…In this scheme, each non-hydrogen atom in a mole- [14,17], the first-order scheme with the simple atom-type discrimination as described has proven useful for the present purpose. The Dice similarity [18] has been used as the similarity measure between two compounds A and B, Sim(A, B), building on the ACF characterization of the chemical structures:…”

The One‐Class Classification Approach to Data Description and to Models Applicability Domain

“…The problem can be solved using the Data Description Analysis based on 1-SVM technique. Unlike some applicability domains techniques earlier described in the literature, this approach: (i) is not confined to any model-specific information (2) can handle large number of descriptors; (3) involves low (zero) variance descriptors; (4) can even be used in the framework of descriptor-less approaches based on chemical graph kernels. An application of 1-SVM improves performance of regression models by rejecting compounds (outliers) dissimilar to the training set.…”

“…The problem of defining the applicability domain (AD) of a QSAR/QSPR model is one of the hottest topics in chemoinformatics (see reviews [1][2][3] ). Surprisingly that this concept is currently used almost exclusively in chemoinformatics and still very little has been discussed in mathematical statistics and machine learning theory.…”