Predicting the solubility of pesticide compounds in water using QSPR methods

The using of the Index of Ideality of Correlation (IIC) to improve predictive potential of models of water solubility for pesticides

Models for water solubility of pesticides suggested in this manuscript are important data from point of view of ecologic engineering. The Index of Ideality of Correlation (IIC) of groups of quantitative structure-property relationships (QSPRs) for water solubility of pesticides related to the calibration sets was used to identify good in silico models. This comparison confirmed the high IIC set provides better statistical quality of the model for the validation set. Though there are large databases on solubility, the reliable prediction of the endpoint for new substances which are potential pesticides is an important ecologic task. Unfortunately, predictive models for various endpoints suffer overtraining, and the IIC serves to avoid or at least reduce this. Thus, the approach suggested has both theoretical and economic effects for ecology.

QSPR studies on water solubility, octanol-water partition coefficient and vapour pressure of pesticides

The assessment of the environmental fate and (eco)toxicological effects of pesticide compounds is of crucial importance. The present review is focused on Quantitative Structure-Property Relationships (QSPR) applications on three environmentally relevant physicochemical properties of pesticides, which can be used for assessing their environmental partition and transport, as well as exposure potential namely water solubility, octanol-water partition coefficient and vapour pressure. This article revises various interesting QSPR applications with special emphasis on studies developed during the 2009-2019 period.

Prediction on the mutagenicity of nitroaromatic compounds using quantum chemistry descriptors based QSAR and machine learning derived classification methods

Nitroaromatic compounds (NACs) are an important type of environmental organic pollutants. However, it is lack of sufficient information relating to their potential adverse effects on human health and the environment due to the limited resources. Thus, using in silico technologies to assess their potential hazardous effects is urgent and promising. In this study, quantitative structure activity relationship (QSAR) and classification models were constructed using a set of NACs based on their mutagenicity against Salmonella typhimurium TA100 strain. For QSAR studies, DRAGON descriptors together with quantum chemistry descriptors were calculated for characterizing the detailed molecular information. Based on genetic algorithm (GA) and multiple linear regression (MLR) analyses, we screened descriptors and developed QSAR models. For classification studies, seven machine learning methods along with six molecular fingerprints were applied to develop qualitative classification models. The goodness of fitting, reliability, robustness and predictive performance of all developed models were measured by rigorous statistical validation criteria, then the best QSAR and classification models were chosen. Moreover, the QSAR models with quantum chemistry descriptors were compared to that without quantum chemistry descriptors and previously reported models. Notably, we also obtained some specific molecular properties or privileged substructures responsible for the high mutagenicity of NACs. Overall, the developed QSAR and classification models can be utilized as potential tools for rapidly predicting the mutagenicity of new or untested NACs for environmental hazard assessment and regulatory purposes, and may provide insights into the in vivo toxicity mechanisms of NACs and related compounds.

QSAR/QSPR as an application of artificial neural networks

Quantitative Structure-Activity Relationships (QSARs) and Quantitative Structure-Property Relationships (QSPRs) are mathematical models used to describe and predict a particular activity/property of compounds. On the other hand, the Artificial Neural Network (ANN) is a tool that emulates the human brain to solve very complex problems. The exponential need for new compounds in the drug industry requires alternatives for experimental methods to decrease development time and costs. This is where chemical computational methods have a great relevance, especially QSAR/QSPR-ANN. This chapter shows the importance of QSAR/QSPR-ANN and provides examples of its use.

QSTR with extended topochemical atom (ETA) indices. 15. Development of predictive models for toxicity of organic chemicals against fathead minnow using second-generation ETA indices

Modern industrialisation has led to the production of millions of toxic chemicals having hazardous effects on the ecosystem. It is impracticable to determine the toxic potential of a large number of chemicals in animal models, making the use of quantitative structure-toxicity relationship (QSTR) models an alternative strategy for toxicity prediction. Recently we introduced a set of second-generation extended topochemical atom (ETA) indices for predictive modelling. Here we have developed predictive toxicity models on a large dataset of 459 diverse chemicals against fathead minnow (Pimephales promelas) using the second-generation ETA indices. These descriptors can be easily calculated from two-dimensional molecular representation without the need of time-consuming conformational analysis and alignment, making the developed models easily reproducible. Considering the importance of hydrophobicity for toxicity prediction, AlogP98 was used as an additional predictor in all the models, which were validated rigorously using multiple strategies. The ETA models were comparable in predictability to those involving various non-ETA topological parameters and those previously reported using various descriptors including computationally demanding quantum-chemical ones.

Analysis and environmental concentrations of the herbicide dichlobenil and its main metabolite 2,6-dichlorobenzamide (BAM): a review

Dichlobenil is an herbicide which has been applied in many countries for weed control in non-agricultural areas such as railroads, car parks and private gardens. In the aquatic environment it has been used for control of floating aquatic weeds. Dichlobenil is relatively persistent in the environment, and primarily bound to solid matrices. Of great concern is its main degradation product 2,6-dichlorobenzamide which is water soluble and therefore transported downward in aquifers, contaminating groundwater resources. It is often found in concentrations exceeding 0.1 μg/L, which is the maximum allowed concentration of pesticides in groundwater set by the European Commission. In many countries, the usage of dichlobenil and the problems associated with groundwater contamination by 2,6-dichlorobenzamide have resulted in intensive research and monitoring of these compounds. This review gives the first overview of analytical strategies available for determining dichlobenil and 2,6-dichlorobenzamide in environmental matrices. It also summarizes studies presenting measured environmental concentrations of dichlobenil and 2,6-dichlorobenzamide identified in the literature during the past two decades. Thereby a preliminary picture of the distribution of dichlobenil and 2,6-dichlorobenzamide in the environment can be outlined for the first time.

QSPR Modeling of Bioconcentration Factors of Nonionic Organic Compounds

The terms bioaccumulation and bioconcentration refer to the uptake and build-up of chemicals that can occur in living organisms. Experimental measurement of bioconcentration is time-consuming and expensive, and is not feasible for a large number of chemicals of potential regulatory concern. A highly effective tool depending on a quantitative structure-property relationship (QSPR) can be utilized to describe the tendency of chemical concentration organisms represented by, the important ecotoxicological parameter, the logarithm of Bio Concentration Factor (log BCF) with molecular descriptors for a large set of non-ionic organic compounds. QSPR models were developed using multiple linear regression, partial least squares and neural networks analyses. Linear and non-linear QSPR models to predict log BCF of the compounds developed for the relevant descriptors. The results obtained offer good regression models having good prediction ability. The descriptors used in these models depend on the volume, connectivity, molar refractivity, surface tension and the presence of atoms accepting H-bonds.