Enhancing Acute Oral Toxicity Predictions by using Consensus Modeling and Algebraic Form-Based 0D-to-2D Molecular Encodes

Quantitative structure–activity
relationships (QSAR) are
introduced to predict acute oral toxicity (AOT), by using the QuBiLS-MAS
(acronym for quadratic, bilinear and N-Linear maps based on graph-theoretic
electronic-density matrices and atomic weightings) framework for the
molecular encoding. Three training sets were employed to build the
models: EPA training set (5931 compounds), EPA-full training set (7413
compounds), and Zhu training set (10 152 compounds). Additionally,
the EPA test set (1482 compounds) was used for the validation of the
QSAR models built on the EPA training set, while the ProTox (425 compounds)
and T3DB (284 compounds) external sets were employed for the assessment
of all the models. The k-nearest neighbor, multilayer perceptron,
random forest, and support vector machine procedures were employed
to build several base (individual) models. The base models with REPA–training ≥ 0.75 (R = correlation coefficient) and MAEEPA–training ≤ 0.5 (MAE = mean absolute error) were retained to build
consensus models. As a result, two consensus models based on the minimum
operator and denoted as M19 and M22, as well as a consensus model
based on the weighted average operator and denoted as M24, were selected
as the best ones for each training set considered. According to the
applicability domain (AD) analysis performed, model M19 (built on
the EPA training set) has MAEtest–AD = 0.4044, MAEProTox–AD = 0.4067 and MAET3DB–AD =
0.2586 on the EPA test set, ProTox external set, and T3DB external
set, respectively; whereas model M22 (built on the EPA-full set) and
model M24 (built on the Zhu set) present MAEProTox–AD = 0.3992 and MAET3DB–AD = 0.2286, and MAEProTox–AD = 0.3773 and MAET3DB–AD =
0.2471 on the two external sets accounted for, respectively. These
outcomes were compared and statistically validated with respect to
14 QSAR methods (e.g., admetSAR, ProTox-II) from the literature. As
a result, model M22 presents the best overall performance. In addition,
a retrospective study on 261 withdrawn drugs due to their toxic/side
effects was performed, to assess the usefulness of prospectively using
the QSAR models proposed in the labeling of chemicals. A comparison
with regard to the methods from the literature was also made. As a
result, model M22 has the best ability of labeling a compound as toxic
according to the globally harmonized system of classification and
labeling of chemicals. Therefore, it can be concluded that the models
proposed, especially model M22, constitute prominent tools for studying
AOT, at providing the best results among all the methods examined.
A freely available software was also developed to be used in virtual
screening tasks (http://tomocomd.com/apps/ptoxra).