QSAR prediction of HIV inhibition activity of styrylquinoline derivatives by genetic algorithm coupled with multiple linear regressions

Chaotic neural network algorithm with competitive learning integrated with partial Least Square models for the prediction of the toxicity of fragrances in sanitizers and disinfectants

This study addresses the need for accurate structural data regarding the toxicity of fragrances in sanitizers and disinfectants. We compare the predictive and descriptive (model stability) potential of multiple linear regression (MLR) and partial least squares (PLS) models optimized through variable selection (VS). A novel hybrid chaotic neural network algorithm with competitive learning (CCLNNA)-PLS modeling strategy can offer specific optimization with satisfactory results, even for a limited dataset. While also exploring the preliminary comparative analysis, the goal is to introduce an adapted novel CCLNNA optimization strategy for VS, inspired by neural networks, along with exploring the influence of the percentage of significant descriptors in the optimization function to enhance the final model's capabilities. We analyzed an available dataset of 24 molecules, incorporating ADMET and PaDEL descriptors as predictor variables, to explore the relationship between the response/target variable (pLC50) and the meticulously optimized set of descriptors. The suitability of the selected PLS models (cross- and external-validated accuracy combined with percentage of significant descriptors at a level equal to or >80 %) underscores the importance of expanding the dataset to amplify the validation protocols, thus enhancing future model reliability and environmental impact.

Boosting Sinh Cosh Optimizer and arithmetic optimization algorithm for improved prediction of biological activities for indoloquinoline derivatives

Quantitative Structure Activity Relation (QSAR) models are mathematical techniques used to link structural characteristics with biological activities, thus considered a useful tool in drug discovery, hazard evaluation, and identifying potentially lethal molecules. The QSAR regulations are determined by the Organization for Economic Cooperation and Development (OECD). QSAR models are helpful in discovering new drugs and chemicals to treat severe diseases. In order to improve the QSAR model's predictive power for biological activities of naturally occurring indoloquinoline derivatives against different cancer cell lines, a modified machine learning (ML) technique is presented in this paper. The Arithmetic Optimization Algorithm (AOA) operators are used in the suggested model to enhance the performance of the Sinh Cosh Optimizer (SCHO). Moreover, this improvement functions as a feature selection method that eliminates superfluous descriptors. An actual dataset gathered from previously published research is utilized to evaluate the performance of the suggested model. Moreover, a comparison is made between the outcomes of the suggested model and other established methodologies. In terms of pIC50 values for different indoloquinoline derivatives against human MV4-11 (leukemia), human HCT116 (colon cancer), and human A549 (lung cancer) cell lines, the suggested model achieves root mean square error (RMSE) of 0.6822, 0.6787, 0.4411, and 0.4477, respectively. The biological application of indoloquinoline derivatives as possible anticancer medicines is predicted with a high degree of accuracy by the suggested model, as evidenced by these findings.

Development of quantitative structure activity relationships (QSARs) for predicting the aggregation of TiO2 nanoparticles under favorable conditions

This study developed multi-linear regression (MLR) quantitative structure-activity relationships (QSARs) to predict n-TiO2 aggregation in the presence of high concentrations of representative emerging organic contaminants (EOCs), which presented favorable conditions to interaction with n-TiO2. The largest diameter change (Δ 517 nm at 0 h and Δ 1164 nm at 12 h) of n-TiO2 was observed by estrone, while the smallest diameter change (Δ -114 nm at 0 h and - 4 nm at 12 h) was observed by lincomycin during experimental periods. In addition, the zeta potential changes of n-TiO2 were observed that the biggest changes were observed by 17β-estradiol (-1.3 mV) and alachlor (-10.02 mV) at 0 h, while 17β-estradiol (-1.31 mV) and pendimethalin (-11.4 mV) showed the biggest changes at 12 h comparing to control. These changes of n-TiO2 diameter and zeta potential may implicate the effects of unique physico-chemical properties of each EOC on the surface modification of n-TiO2. Based on the interaction results, this study investigated the QSARs between n-TiO2 aggregation and physico-chemical descriptors of EOCs with 7 representative descriptors (pKa, Cw, log Kow, M.W., P.S.A., M.V., # of HBD) for predicting n-TiO2 aggregation rate kinetics at 0 h and 12 h by applying MATLAB statistical methods (model 1 - fitlm and model 2 - stepwiselm). In a model 1, QSARs showed the good coefficients of determination (R2 = 0.92) at 0 h and (R2 = 0.87) at 12 h with 7 descriptors. In a model 2, QSARs showed the goodness of fit of a model (R2 = 0.9998) with 8 descriptors (pKa, Cw, log Kow, M.W., P.S.A., M.V., #HBD, pKa⋅#H bond donors) at 0 h, while QSARs showed the coefficients of determination (R2 = 0.68) with 2 descriptors (pKa, M.V.) at 12 h. Particularly, we observed that some descriptors of EOCs such as pKa and # of HBD having polarity have more influenced on the n-TiO2 aggregation rate kinetics. Our developed QSARs demonstrated that the 7 descriptors of EOCs were significantly effective descriptors for predicting n-TiO2 aggregation rate kinetics in favorable conditions, which may implicate the complexity interactions between heterogeneous surfaces of n-TiO2 and physico-chemical properties of EOCs.

Predictionof Environmental FateandToxicityofInsecticidesUsing Multi-Target QSAR Approach

Ecotoxicological risk assessments form the foundation of regulatory decisions for industrial chemicals used in various sectors. In this study, a multi-target-QSAR model established by a backpropagation neural network trained with the Levenberg-Marquardt (LM) algorithm was used to construct a statistically robust and easily interpretable Mt-QSAR model with high external predictability for the simultaneous prediction of the environmental fate in form of octanol-water partition coefficient (LogP), (BCF) and acute oral toxicity in mammals and birds (LD50rat ) and (LD50bird ) for a wide range of chemical structural classes of insecticides. Principal component analysis was performed on descriptors selected by the SW-MLR method, and the selected PCs were used for constructing the SW-MLR-PCA-ANN model. The developed well-trained model (RMSE=0.83, MPE=0.004, CCC=0.82, IIC=0.78, R2 =0.69) was statistically robust as indicated by the external validation parameters (RMSE=0.93, MPE=0.008, CCC=0.77, IIC=0.68, R2 =0.61). The AD of the developed Mt-QSAR model was also defined to identify the most reliable predictions. Finally, the missing values in the dataset for the aforementioned targets were predicted using the constructed Mt-QSAR model. The proposed approach can be used for simultaneous prediction of the environmental fate of new insecticides, especially ones that haven't been tested yet.

Exploring different computational approaches for effective diagnosis of breast cancer

Breast cancer has been identified as one among the top causes of female death worldwide. According to recent research, earlier detection plays an important role toward fortunate medicaments and thus, decreasing the mortality rate due to breast cancer among females. This review provides a fleeting summary involving traditional diagnostic procedures from the past and today, and also modern computational tools that have greatly aided in the identification of breast cancer. Computational techniques involving different algorithms such as Support vector machines, deep learning techniques and robotics are popular among the academicians for detection of breast cancer. They discovered that Convolutional neural network was a common option for categorization among such approaches. Deep learning techniques are evaluated using performance indicators such as accuracy, sensitivity, specificity, or measure. Furthermore, molecular docking, homology modeling and Molecular dynamics Simulation gives a road map for future discussions about developing improved early detection approaches that holds greater potential in increasing the survival rate of cancer patients. The different computational techniques can be a new dominion among researchers and combating the challenges associated with breast cancer.

In silico drug discovery of IKK-β inhibitors from 2-amino-3-cyano-4-alkyl-6-(2-hydroxyphenyl) pyridine derivatives based on QSAR, docking, molecular dynamics and drug-likeness evaluation studies

The Inhibitor of IKK-β (nuclear factor kappa B kinase subunit beta), a specific modulator of NF-κB (nuclear factor-κB), is considered a valid target to discover new active compounds for various cancers and rheumatoid arthritis treatment. In this study a series of thirty 2-amino-3-cyano-4-alkyl-6-(2-hydroxyphenyl) pyridine derivatives was involved for a quantitative structure activity relationship model (QSAR) elaboration which allows the prediction of the pIC50 values of new designed compounds. The model can be used to predict the activity of new compounds within its applicability domain. Then a molecular docking study was carried out to identify the interactions between the compounds and the amino acids of the active site. After that, golden triangle, Veber's rule, and Lipinski's rule properties were calculated to identify the drug-likeness properties of the investigated compounds. Finally, in-silico-toxicity studies were performed to predict the toxicity of the new designed compounds. The analysis of the results of QSAR model and molecular docking succeeded to screen 21 interesting compounds with better inhibitory concentration having a good affinity to IKK-β. All compounds were within the range set by Veber's rule and Lipinski's rule. the analysis of golden triangle showed that the thirty 2-amino-3-cyano-4-alkyl-6-(2-hydroxyphenyl) pyridine derivatives would not have clearance and cell membrane permeability problems except comp6 comp12,comp20, comp21, and comp26.As for the new designed compounds, their properties may have these problems, except two compounds which are: A8m, A8p. The A1m, A1p, A3p and A11m compounds were predicted to be nontoxic. These findings indicate that the novel potent candidate drugs have promising potential to IKK-β enzyme inhibition and should motivate future experimental investigations.Communicated by Ramaswamy H. Sarma.

Free variable selection QSPR study to predict (19)F chemical shifts of some fluorinated organic compounds using Random Forest and RBF-PLS methods

In this work, two new and powerful chemometrics methods are applied for the modeling and prediction of the (19)F chemical shift values of some fluorinated organic compounds. The radial basis function-partial least square (RBF-PLS) and random forest (RF) are employed to construct the models to predict the (19)F chemical shifts. In this study, we didn't used from any variable selection method and RF method can be used as variable selection and modeling technique. Effects of the important parameters affecting the ability of the RF prediction power such as the number of trees (nt) and the number of randomly selected variables to split each node (m) were investigated. The root-mean-square errors of prediction (RMSEP) for the training set and the prediction set for the RBF-PLS and RF models were 44.70, 23.86, 29.77, and 23.69, respectively. Also, the correlation coefficients of the prediction set for the RBF-PLS and RF models were 0.8684 and 0.9313, respectively. The results obtained reveal that the RF model can be used as a powerful chemometrics tool for the quantitative structure-property relationship (QSPR) studies.