Applicability Domains for Classification Problems: Benchmarking of Distance to Models for Ames Mutagenicity Set

TLDR

The estimation of accuracy and applicability of QSAR and QSPR models for biological and physicochemical properties represents a critical problem. The study introduces a distance‑to‑model (DM) metric that measures similarity between training and test compounds and applies it to 30 QSAR models for the Ames mutagenicity dataset from the 2009 QSAR challenge. Using ensemble‑based DM metrics, the authors show improved applicability assessment, identifying 30–60 % of Ames mutagenicity compounds with prediction accuracy comparable to the 90 % interlaboratory benchmark, thereby enabling cost‑effective in silico screening and providing the model publicly at http://ochem.eu/models/1.

Abstract

The estimation of accuracy and applicability of QSAR and QSPR models for biological and physicochemical properties represents a critical problem. The developed parameter of "distance to model" (DM) is defined as a metric of similarity between the training and test set compounds that have been subjected to QSAR/QSPR modeling. In our previous work, we demonstrated the utility and optimal performance of DM metrics that have been based on the standard deviation within an ensemble of QSAR models. The current study applies such analysis to 30 QSAR models for the Ames mutagenicity data set that were previously reported within the 2009 QSAR challenge. We demonstrate that the DMs based on an ensemble (consensus) model provide systematically better performance than other DMs. The presented approach identifies 30−60% of compounds having an accuracy of prediction similar to the interlaboratory accuracy of the Ames test, which is estimated to be 90%. Thus, the in silico predictions can be used to halve the cost of experimental measurements by providing a similar prediction accuracy. The developed model has been made publicly available at http://ochem.eu/models/1.