Typical in silico models for ecotoxicology focus on a few endpoints, but there is a need to increase the diversity of these models. This study proposes models using the NOEC for the harlequin fly (Chironomus riparius) and EC50 for swollen duckweed (Lemna gibba) for the first time. The data were derived from the EFSA OpenFoodTox database. The models were based on the correlation weights of molecular features used to calculate the 2D descriptor in CORAL software. The Monte Carlo method was used to calculate the correlation weights of the algorithms. The determination coefficients of the best models for the external validation set were 0.74 (NOAEC) and 0.85 (EC50).

Monoamine oxidases are the enzymes involved in the management of brain homeostasis through oxidative deamination of monoamines such as neurotransmitters, tyramine etc. The excessive production of monoamine oxidase-B specifically results in numerous neurodegenerative disorders like Alzheimer\'s and Parkinson\'s diseases. Inhibitors of monoamine oxidase-B are applied in the management of these disorders. Here in this article we have developed robust hybrid descriptor based QSAR models related to 123 monoamine oxidase-B inhibitors through CORAL software by means of Monte Carlo optimization method. Three target functions were applied to prepare QSAR models and three splits were made for each target function. The most reliable, robust and better predictive QSAR models were developed with TF3 (correlation intensity index -index of ideality of correlation). Correlation intensity index showed positive effect on QSAR models. The structural features obtained from the QSAR modeling were incorporated in newly designed molecules and exhibited positive effect on their endpoint. Significant binding interactions were represented by these molecules in docking studies. Molecule B5 displayed prominent pIC50 (8.3) and binding affinity (-11.5 kcal mol-1) towards monoamine oxidase-B.

The assessment of cardiotoxicity is a persistent problem in medicinal chemistry. Quantitative structure-activity relationships (QSAR) are one possible way to build up models for cardiotoxicity. Here, we describe the results obtained with the Monte Carlo technique to develop hybrid optimal descriptors correlated with cardiotoxicity. The predictive potential of the cardiotoxicity models (pIC50, Ki in nM) of piperidine derivatives obtained using this approach provided quite good determination coefficients for the external validation set, in the range of 0.90-0.94. The results were best when applying the so-called correlation intensity index, which improves the predictive potential of a model.

Mutagenicity is one of the most dangerous properties from the point of view of medicine and ecology. Experimental determination of mutagenicity remains a costly process, which makes it attractive to identify new hazardous compounds based on available experimental data through in silico methods or quantitative structure-activity relationships (QSAR). A system for constructing groups of random models is proposed for comparing various molecular features extracted from SMILES and graphs. For mutagenicity (mutagenicity values were expressed by the logarithm of the number of revertants per nanomole assayed by Salmonella typhimurium TA98-S9 microsomal preparation) models, the Morgan connectivity values are more informative than the comparison of quality for different rings in molecules. The resulting models were tested with the previously proposed model self-consistency system. The average determination coefficient for the validation set is 0.8737 ± 0.0312.

Clinical studies show that the pyroglutamate alteration of amyloid-β (Aβ) catalysed by metalloenzyme glutaminyl cyclase results in the formation of the more neurotoxic pGlu-Aβ, and inhibition of glutaminyl cyclase can bring down the load of pGlu-Aβ in the brain and reduces Alzheimer\'s disease pathology with improvement in cognition. The present study involves the identification of activity-modulating structural features of 188 inhibitors of glutaminyl cyclase under the influence of index of ideality of correlation (IIC) and correlation intensity index (CII) as prediction parameters. The QSAR models developed employing IIC and CII were found to be statistically better and had better predictability than the models developed without them. The best model (split 4) showed r2 values of 0.8155 and 0.8218 for calibration and validation sets, respectively. The structural features classified from QSAR models were used to design some new glutaminyl cyclase inhibitors. Among the designed ligands, ligand 5 possesses the highest pIC50 value (6.30) as well as binding affinity (-6.2 kcal/mol) and creates hydrogen bonds with TRP 329, π-alkyl interactions with ILE 303 and TYR 299, π-π stacking interaction with PHE 325 and interactions with ZN 391. All novel designed ligands have better pIC50 values and binding affinities.

Quasi-SMILES is an extension of the traditional SMILES. The classic SMILES is a way to represent the molecular structure. The quasi-SMILES is a way to describe all eclectic conditions that are able to affect the activity of a substance or a mixture. Nano-QSAR for prediction of toxicity of Nano-mixtures built up using the database on the corresponding experimental data. Models built up for five random splits of available data in training and validation sets are suggested. The Monte Carlo method of optimization is applied to calculate so-called optimal descriptors. The optimization was carried out with two criteria of predictive potential. These are the so-called index of ideality of correlation (IIC) and correlation intensity index (CII). Applying CII gives the better statistical quality of models for toxicity Nano-mixtures towards Daphnia Magna. The statistical quality of the best model follows the determination coefficients 0.987 (training set) and 0.980 (validation set).

Azo dyes are broadly used in different industries through their chemical stability and ease of synthesis. However, these dyes are usually identified as critical environmental pollutants. Hence, a mathematical model for the adsorption affinity of azo dyes can be applied for solving tasks of medicine and ecology. Quantitative structure-property relationships for the adsorption affinity of azo dyes to a substrate (DAF, kJ/mol) were established using the Monte Carlo method by generating optimal SMILES-based descriptors. The index of ideality of correlation (IIC) and the correlation intensity index (CII) improved the model\'s predictive potential, especially when they were used simultaneously. The statistical quality of the best model on the validation set was characterized by n = 18, r2 = 0.9468, and RMSE = 1.26 kJ/mol.

Simplified molecular input-line entry system (SMILES) is a format for representing of the molecular structure. Quasi-SMILES is an extended format for representing molecular structure data and some eclectic data, which in principle could be applied to improve a model\'s predictive potential. Nano-quantitative structure-property relationships (nano-QSPRs) for energy gap (Eg, eV) of the metals oxide nanoparticles based on the quasi-SMILES give a predictive model for Eg, characterized by the following statistical quality for external validation set n = 22, R2 = 0.83, RMSE = 0.267.

Robust quantitative structure-activity relationships (QSARs) for hBACE-1 inhibitors (pIC50) for a large database (n = 1706) are established. New statistical criteria of the predictive potential of models are suggested and tested. These criteria are the index of ideality of correlation (IIC) and the correlation intensity index (CII). The system of self-consistent models is a new approach to validate the predictive potential of QSAR-models. The statistical quality of models obtained using the CORAL software (http://www.insilico.eu/coral) for the validation sets is characterized by the average determination coefficient R2v= 0.923, and RMSE = 0.345. Three new promising molecular structures which can become inhibitors hBACE-1 are suggested.

Some new products, which include common personal-care products, drugs, household items, can be hazardous in aspect personal care products/cosmetics and their ingredients (i.e. the above can effect human skin). International organizations (e.g. the Organisation for Economic Co-operation and Development-OECD) recommend evaluating individual ingredients when assessing the safety of personal care or cosmetic products. Thus, checking up that \"popular at the market\" substances are non-toxic, do not penetrate into or through normal or compromised human skin, and therefore, pose no risk to human health is an essential element of modern toxicology. The development of reliable models of toxicological endpoints is a tool to carry out the above checking up via quantitative structure-activity relationships (QSARs). The reliability of the QSAR is the current task of mathematical statistics. Recently, the index of ideality of correlation (IIC) and correlation intensity index (CII) were suggested as criteria of predictive potential (i.e. reliability) of QSAR-models. Here, the abilities of these criteria were studied for the case of building up models for skin sensitivity (LLNA, local lymph node assay). Computational experiments have confirmed that the IIC demonstrates an obvious ability to improve the predictive potential of models of skin sensitization. The applying of the CII for the case of skin sensitization also improves the quality of the model. However, the best models for skin sensitization were observed if the above-mentioned criteria are applied jointly (n = 268; R2 = 0.60; RMSE = 0.63).