Comparison of Different Approaches to Define the Applicability Domain of QSAR Models

By F. Sahigara, Kamel Mansouri, Davide Ballabio, A. Mauri, Viviana Consonni, Roberto Todeschini. Molecules, 2012

542 citations recorded by the source corpus.

One of the OECD principles for model validation requires defining the Applicability Domain (AD) for the QSAR models. This is important since the reliable predictions are generally limited to query chemicals structurally similar to the training compounds used to build the model. Therefore, characterization of interpolation space is significant in defining the AD and in this study some existing descriptor-based approaches performing this task are discussed and compared by implementing them on existing validated datasets from the literature. Algorithms adopted by different approaches allow defining the interpolation space in several ways, while defined thresholds contribute significantly to the extrapolations. For each dataset and approach implemented for this study, the comparison analysis was carried out by considering the model statistics and relative position of test set with respect to the training space.

Subfields: Analytical Chemistry, Computational Theory and Mathematics, Materials Chemistry

View Paper: https://www.semanticscholar.org/paper/e955330fec7945e84fe365dbf16f7aa833421ecd

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Citations in the corpus

Software Tools and Approaches for Compound Identification of LC-MS/MS Data in Metabolomics (2018, background): “An additional important consideration for retention time models is the applicability domain or structural space used in model building [110].”
Drug discovery with explainable artificial intelligence (2020, background): “Most of the deep learning models in drug discovery do not consider applicability domain issues, 198 i.e. , the region of chemical space where statistical learning assumptions are met.”