Whether the Validation of the Predictive Potential of Toxicity Models is a Solved Task?

Construction of reliable QSPR models for predicting the impact sensitivity of nitroenergetic compounds using correlation weights of the fragments of molecular structures

Impact sensitivity is a critical property of energetic molecules, indicating their tendency to react when subjected to mechanical stimuli such as impact. Nitro compounds are widely used as explosives across industrial, military, and civilian applications, making their safe handling a significant concern for engineers and scientists working with these materials. Predicting whether a molecule has the potential to pose safety risks is therefore of great importance. This study aimed to develop a QSPR model for predicting the impact sensitivity of 404 nitro compounds using the Monte Carlo algorithm implemented in CORAL-2023 software. The Simplified Molecular Input Line Entry System (SMILES) was employed to represent the molecular structures, while correlation weight descriptors were computed. Four target functions (TF0, TF1, TF2, and TF3) were used to generate robust models. The first model applied Monte Carlo optimization without the inclusion of IIC (information index of correlation) or CII (correlation index of information); the second incorporated IIC; the third incorporated CII; and the fourth applied both IIC and CII. Comparative statistical analyses indicated that the model integrating both IIC and CII demonstrated superior predictive performance, with the best results observed in split 2 (R2Validation = 0.7821, IICValidation = 0.6529, CIIValidation=0.8766, Q2Validation = 0.7715, and [Formula: see text] = 0.7464).

The system of self-consistent models for pesticide toxicity to Daphnia magna

Quantitative structure-property/activity relationships (QSPRs/QSARs) are a tool of modern theoretical and computational chemistry. The self-consistent model system is both a method to build up a group of QSPR/QSAR models and an approach to checking the reliability of these models. Here, a group of models of pesticide toxicity toward Daphnia magna for different distributions into training and test sub-sets is compared. This comparison is the basis for formulating the system of self-consistent models. The so-called index of the ideality of correlation (IIC) has been used to improve the above models' predictive potential of pesticide toxicity. The predictive potential of the suggested models should be classified as high since the average value of the determination coefficient for the validation sets is 0.841, and the dispersion is 0.033 (on all five models). The best model (number 4) has an average determination coefficient of 0.89 for the external validation sets (related to all five splits).

A regression-based QSAR-model to predict acute toxicity of aromatic chemicals in tadpoles of the Japanese brown frog (Rana japonica): Calibration, validation, and future developments to support risk assessment of chemicals in amphibians

Amphibian populations are undergoing a global decline worldwide. Such decline has been attributed to their unique physiology, ecology, and exposure to multiple stressors including chemicals, temperature, and biological hazards such as fungi of the Batrachochytrium genus, viruses such as Ranavirus, and habitat reduction. There are limited toxicity data for chemicals available for amphibians and few quantitative structure-activity relationship (QSAR) models have been developed and are publicly available. Such QSARs provide important tools to assess the toxicity of chemicals particularly in a data poor context. QSARs provide important tools to assess the toxicity of chemicals particularly when no toxicological data are available. This manuscript provides a description and validation of a regression-based QSAR model to predict, in a quantitative manner, acute lethal toxicity of aromatic chemicals in tadpoles of the Japanese brown frog (Rana japonica). QSAR models for acute median lethal molar concentrations (LC50-12 h) of waterborne chemicals using the Monte Carlo method were developed. The statistical characteristics of the QSARs were described as average values obtained from five random distributions into training and validation sets. Predictions from the model gave satisfactory results for the overall training set (R² = 0.72 and RMSE = 0.33) and were even more robust for the validation set (R² = 0.96 and RMSE = 0.11). Further development of QSAR models in amphibians, particularly for other life stages and species, are discussed.