Criterion Of Predictive Potential Research Articles

Identifications of good and bad structural fragments of hydrazone/2,5-disubstituted-1,3,4-oxadiazole hybrids with correlation intensity index and consensus modelling using Monte Carlo based QSAR studies, their molecular docking and ADME analysis

ABSTRACT The application of QSAR along with other in silico tools like molecular docking, and molecular dynamics provide a lot of promise for finding new treatments for life-threatening diseases like Type 2 diabetes mellitus (T2DM). The present study is an attempt to develop Monte Carlo algorithm-based QSAR models using freely available CORAL software. The experimental data on the α-amylase inhibition by a series of benzothiazole-linked hydrazone/2,5-disubstituted-1,3,4-oxadiazole hybrids were selected as endpoint for the model generation. Initially, a total of eight QSAR models were built using correlation intensity index (CII) as a criterion of predictive potential. The model developed from split 6 using CII was the most reliable because of the highest numerical value of the determination coefficient of the validation set (r 2 VAL = 0.8739). The important structural fragments responsible for altering the endpoint were also extracted from the best-built model. With the goal of improved prediction quality and lower prediction errors, the validated models were used to build consensus models. Molecular docking was used to know the binding mode and pose of the selected derivatives. Further, to get insight into their metabolism by living beings, ADME studies were investigated using internet freeware, SwissADME.

Quantitative structure–toxicity relationship models for predication of toxicity of ionic liquids toward leukemia rat cell line IPC-81 based on index of ideality of correlation

The application of ionic liquids (ILs) as green solvents has attracted the attention of the scientific community. However, ILs may play the role of toxins. Even though ionic liquids may assist to minimize air pollution, but their discharge into aquatic ecosystems might result in significant water pollution due to their potential toxicity and inaccessibility to biodegradation. Recently, more attention has been paid to the toxicity of ILs on plants, bacteria, and humans. Here, a quantitative structure–toxicity relationship study (QSTR) based on the Monte Carlo method of CORAL software has been applied to estimate the logarithm of the half-maximal effective concentration of toxicity of ILs against leukemia rat cell line IPC-81 (logEC50). A hybrid optimal descriptor is used to build QSTR models for a large set of 304 diverse ILs including ammonium, imidazolium, morpholinium, phosphonium, piperidinium, pyridinium, pyrrolidinium, quinolinium, sulfonium, and protic ILs. The SMILES notations of ILs are utilized to compute the descriptor correlation weight (DCW). Four splits are made from the whole dataset and each split is randomly divided into four sets (training subsets and validation set). The index of ideality of correlation (IIC) is applied to evaluate the authenticity and robustness of the QSTR models. A QSTR model with statistical parameters R 2 = 0.85, CCC = 0.92, Q 2 = 0.84, and MAE = 0.25 for the validation set of the best split is considered as a prime model. The outliers and promoters of increase/decrease of logEC50 are extracted and the mechanistic interpretation of effective descriptors for the model is also offered. Highlights Global SMILES-based QSAR model was developed to predict the toxicity of ILs. The CORAL software is used to model the ILs toxicity on IPC-81 leukemia rat cell line. IIC is tested as a criterion of predictive potential. The toxicological effects of ILs are discussed based on the proposed model.

Prediction of No Observed Adverse Effect Concentration for inhalation toxicity using Monte Carlo approach

ABSTRACT Ideal correlation is one variable model based on so-called optimal descriptors calculated with simplified molecular input-line entry systems (SMILES). The optimal descriptor is calculated according to the index of ideality of correlation, a new criterion of predictive potential of quantitative structure–property/activity relationships (QSPRs/QSARs). The aim of the present study was the building and estimation of models for inhalation toxicity as No Observed Adverse Effect Concentration (NOAEC) based on the OECD guidelines 413. Three random distributions into the training set and validation set were examined. In practice, a structured training set that contains active training set, passive training set and calibration set is used as the training set. The statistical characteristics of the best model for negative logarithm of NOAEC (pNOAEC) are for training set n = 108, average r 2 = 0.52 + 0.62 + 0.76/3 = 0.63 and for validation set n = 35, r 2 = 0.73.

Correlation Intensity Index (CII) as a criterion of predictive potential: Applying to model thermal conductivity of metal oxide-based ethylene glycol nanofluids

Thermal conductivity of the metal oxide-based nanofluids dispersed in ethylene glycol is studied here as an endpoint for nano-QSPR analysis. The Monte Carlo technique is applied to build up the model. The Correlation Intensity Index (CII) is an element of the Monte Carlo method aimed to build up the thermal conductivity models. The attempt to procreate nano-QSPR models with more predictive power is successful by applying the CII as a novel criterion. According to the comparison of statistical results, it is proved that the models generated based on CII are superior to the developed models built up without CII.

The Use of the Index of Ideality of Correlation to Build Up Models for Bioconcentration Factor.

We establish a QSPR analysis for the bioconcentration factor of 851 heterogeneous structural compounds. Linear models are proposed via two different approaches: i. the optimal descriptor method implemented in CORAL, and ii. multivariable linear regressions on the best molecular descriptors found with the Replacement Method on 44,216 structural descriptors. Such variables are derived with different freely available softwares, such as PaDEL, PyDescriptor, Mold2 , QuBiLs-MAS and ISIDA/Fragmentor. The same validation set is employed in order to compare the predictive performance between the so-obtained CORAL and RM based models. Finally, the comparison of several models for the bioconcentration factor confirms the ability of the so-called index of ideality of correlation to be a criterion of predictive potential in Quantitative Structure-Property Relationships.

The index of ideality of correlation: models for flammability of binary liquid mixtures

Data on flammability of binary liquid mixtures are necessary to rational classification of different binary mixtures of liquids. List of corresponding binary mixtures, which have practical applications, is large and gradually, this list is increasing. Hence, reliable models for the endpoint can be useful. Simplified molecular input-line entry system (SMILES) is the representation of the molecular structure. The SMILES can be applied to build up quantitative structure—property/activity relationships (QSPRs/QSARs). Quasi-SMILES is the expansion of traditional SMILES by means of additional symbols that reflect “eclectic” conditions which able to influence physicochemical endpoints. The applying of the quasi-SMILES to build up model for flammability of binary liquid mixtures has indicated that the approach gives quite good model for the flash points (°C) of binary mixtures of organic substances. The index of ideality of correlation (IIC) is a new criterion of predictive potential. The attempts of applying of the IIC to improve models for flammability of binary liquid mixtures were successful.

The Index of Ideality of Correlation (IIC): model for sweetness

The prediction of physicochemical and biochemical endpoints related to food, cosmetics, drug discovery, and medicine using computational approaches is relevant to the need of the modern society. The experimental determination of the endpoints is impossible for all necessary substances. The Index of Ideality of Correlation (IIC) is a criterion of predictive potential of a model. The index has been implemented in the CORAL software, which is a tool to build up quantitative structure—activity relationships. In the present work, two models for sweetness potential are built using this software. The first model is built without taking into account the IIC. The second model is built with taking into account the IIC. The results show that the IIC improves the predictive potential. This was confirmed in three computational experiments with random splits of the data related to sweetness into training and validation sets.

Predicting Cytotoxicity of 2-Phenylindole Derivatives Against Breast Cancer Cells Using Index of Ideality of Correlation

Breast cancer is one of the leading types of cancer in women worldwide. Quantitative structure-activity relationship (QSAR) methods play an important role in the search for new anticancer agents. A QSAR model for cytotoxicity against the breast cancer cell line MCF7, based on hybrid optimal descriptors, has been suggested. A modified version of the hybrid descriptor is suggested. A QSAR model for the anticancer activity of 2-phenylindole derivatives was built using the Index of Ideality of Correlation (IIC), which is a new criterion for predictive potential. The calculation can be carried out with a modified version of the CORAL software. The model for the anticancer activity suggested here is better than the one described in the literature. Taking into account the data on molecular rings together with the use of new criterion of predictive potential (IIC), the QSAR improves the prediction for anticancer activity.

The index of ideality of correlation: improvement of models for toxicity to algae

Toxicity to algae is important characteristic of substances from ecologic point of view. The CORAL software (http://www.insilico.eu/coral) gives possibility to build up model of toxicity to algae using data on the molecular architecture and experimental toxicity, without additional data on physicochemical and/or biochemical parameters. Considerable improvement of the model is observed in the case of using the index of ideality of correlation (IIC) in the role of additional criterion of predictive potential. The IIC is calculated with using of the correlation coefficient between experimental and calculated values of endpoint for the calibration set, with taking into account the positive and negative dispersions between experimental and calculated values. The best model calculated with use the IIC is characterized (the validation set) by n = 50, r2 = 0.947, RMSE = 0.401 whereas, model calculated without use the IIC is characterized by n = 50, r2 = 0.805, and RMSE = 0.539. The suggested models are built up in accordance to five OECD principles.