Abstract
The ability of accurate predictions of biological response (biological activity/property/toxicity) of a given chemical makes the quantitative structure‐activity/property/toxicity relationship (QSAR/QSPR/QSTR) models unique among the in silico tools. In addition, experimental data of selected species can also be used as an independent variable along with other structural as well as physicochemical variables to predict the response for different species formulating quantitative activity–activity relationship (QAAR)/quantitative structure–activity–activity relationship (QSAAR) approach. Irrespective of the models' type, the developed model's quality, and reliability need to be checked through multiple classical stringent validation metrics. Among the validation metrics, error-based metrics are more significant as the basic idea of a good predictive model is to improve the predictions' quality by lowering the predicted residuals for new query compounds. Following the concept, we have checked the predictive quality of the QSAR and QSAAR models employing kernel-weighted local polynomial regression (KwLPR) approach over the traditional linear and non-linear regression-based approaches tools such as multiple linear regression (MLR) and k nearest neighbors (kNN). Five datasets which were previously modeled using linear and non-linear regression method were considered to implement the KwPLR approach, followed by comparison of their validation metrics outcomes. For all five cases, the KwLPR based models reported better results over the traditional approaches. The present study's focus is not to develop a better or improved QSAR/QSAAR model over the previous ones, but to demonstrate the advantage, prediction power, and reliability of the KwLPR algorithm and establishing it as a novel, powerful cheminformatic tool. To facilitate the use of the KwLPR algorithm for QSAR/QSPR/QSTR/QSAAR modeling, the authors provide an in-house developed KwLPR.RMD script under the open-source R programming language.
Highlights
The biological response, physicochemical properties as well as intrinsic toxicity of a chemical have a strong relationship with its structural representation
R‐script To facilitate the use of the kernel-weighted local polynomial regression approach for QSAR/quantitative structure–activity–activity relationship (QSAAR) modeling, we provide an in-house developed script as Supplementary information (Additional file 6)
We demonstrate its advantages compared to the traditional linear and non-linear regression-based techniques
Summary
The biological response, physicochemical properties as well as intrinsic toxicity of a chemical have a strong relationship with its structural representation. The interspecies quantitative activity–activity relationship (QAAR)/quantitative structure–activity–activity relationship (QSAAR) model offers to predict an endpoint (which is a dependent variable) for specific species employing the same endpoint (response in the form of activity, property, or toxicity) for another species along with selected structural and physicochemical features as a predictor or explanatory or independent variables (descriptors) [13, 14]. The endpoint acts as a predictor variable, it can highlight the mechanism of action (MOA) of a series of chemicals to some extent as they are derived from experimental bioassay along with structural and physicochemical features This specific feature strengthens the reliability and precision of the QSAAR model over the simple QSAR models. Extrapolating data from one species to another helps fill the data gaps without wasting time, money, and animal study maintaining the 3R’s approach intended to a replacement, reduction, and refinement of animals
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