Targeting bladder cancer with Trigonella foenum-graecum: a computational study using network pharmacology and molecular docking.

Trigonella foenum-graecum (TF-graecum), known as Hulba or Fenugreek, is one of the oldest known medicinal plants. It has been found to have antimicrobial, antifungal, antioxidant, wound-healing, anti-diarrheal, hypoglycemic, anti-diabetic, and anti-inflammatory activities. In our current report, we have collected and screened the active compounds of TF-graecum and their potential targets via different pharmacology platforms. Network construction shows that eight active compounds may act on 223 potential bladder cancer targets. The pathway enrichment analysis for the seven potential targets of the eight compounds selected, based on KEGG pathway analysis, was conducted to clarify the potential pharmacological effects. Finally, molecular docking and molecular dynamics simulation showed the stability of protein-ligand interactions. This study highlights the need for increased research into the potential medical benefits of this plant.Communicated by Ramaswamy H. Sarma.

Deep Learning for Drug Development: Using CNNs in MIA-QSAR to Predict Plasma Protein Binding of Drugs.

Predicting plasma protein binding (PPB) is crucial in drug development due to its profound impact on drug efficacy and safety. In our study, we employed a convolutional neural network (CNN) as a tool to extract valuable information from the molecular structures of 100 different drugs. These extracted features were then used as inputs for a feedforward network to predict the PPB of each drug. Through this approach, we successfully obtained 10 specific numerical features from each drug's molecular structure, which represent fundamental aspects of their molecular composition. Leveraging the CNN's ability to capture these features significantly improved the precision of our predictions. Our modeling results revealed impressive accuracy, with an R2 train value of 0.89 for the training dataset, a [Formula: see text] of 0.98, a [Formula: see text] of 0.931 for the external validation dataset, and a low cross-validation mean squared error (CV-MSE) of 0.0213. These metrics highlight the effectiveness of our deep learning techniques in the fields of pharmacokinetics and drug development. This study makes a substantial contribution to the expanding body of research exploring the application of artificial intelligence (AI) and machine learning in drug development. By adeptly capturing and utilizing molecular features, our method holds promise for enhancing drug efficacy and safety assessments in pharmaceutical research. These findings underscore the potential for future investigations in this exciting and transformative field.

A Quantitative Structure-Activity Relationship for Human Plasma Protein Binding: Prediction, Validation and Applicability Domain.

Purpose: The purpose of this study was to develop a robust and externally predictive in silico QSAR-neural network model for predicting plasma protein binding of drugs. This model aims to enhance drug discovery processes by reducing the need for chemical synthesis and extensive laboratory testing. Methods: A dataset of 277 drugs was used to develop the QSAR-neural network model. The model was constructed using a Filter method to select 55 molecular descriptors. The validation set's external accuracy was assessed through the predictive squared correlation coefficient Q2 and the root mean squared error (RMSE). Results: The developed QSAR-neural network model demonstrated robustness and good applicability domain. The external accuracy of the validation set was high, with a predictive squared correlation coefficient Q2 of 0.966 and a root mean squared error (RMSE) of 0.063. Comparatively, this model outperformed previously published models in the literature. Conclusion: The study successfully developed an advanced QSAR-neural network model capable of predicting plasma protein binding in human plasma for a diverse set of 277 drugs. This model's accuracy and robustness make it a valuable tool in drug discovery, potentially reducing the need for resource-intensive chemical synthesis and laboratory testing.

Dragonfly algorithm-support vector machine approach for prediction the optical properties of blood.

Knowledge of the optical properties of blood plays important role in medical diagnostics and therapeutic applications in laser medicine. In this paper, we present a very rapid and accurate artificial intelligent approach using Dragonfly Algorithm/Support Vector Machine models to estimate the optical properties of blood, specifically the absorption coefficient, and the scattering coefficient using key parameters such as wavelength (nm), hematocrit percentage (%), and saturation of oxygen (%), in building very highly accurate Dragonfly Algorithm-Support Vector Regression models (DA-SVR). 1000 training and testing sets were selected in the wavelength range of 250-1200 nm and the hematocrit of 0-100%. The performance of the proposed method is characterized by high accuracy indicated in the correlation coefficients (R) of 0.9994 and 0.9957 for absorption and scattering coefficients, respectively. In addition, the root mean squared error values (RMSE) of 0.972 and 2.9193, as well as low mean absolute error values (MAE) of 0.2173 and 0.2423, this result showed a strong match with the experimental data. The models can be used to accurately predict the absorption and scattering coefficients of blood, and provide a reliable reference for future studies on the optical properties of human blood.

Development of QSPR-ANN models for the estimation of critical properties of pure hydrocarbons.

The current work aimed to predict three critical properties: critical temperature (Tc), critical volume (Vc), and critical pressure (Pc) of pure hydrocarbons. A multi-layer perceptron artificial neural network (MLP-ANN) has been adopted as a nonlinear modeling technique and computational approach based on a few relevant molecular descriptors. A set of diverse data points was used to build three QSPR-ANN models, including 223 points for Tc, Vc, and 221 for Pc. The entire database was randomly split into two subsets: 80% for the training set and 20% for the testing set. A large number of 1666 molecular descriptors were calculated and then reduced by a statistical methodology based on several phases to retain them into a reasonable number of relevant descriptors, wherein about 99% of initial descriptors were excluded. Thus, the Quasi-Newton backpropagation (BFGS) algorithm was applied to train the ANN structure. The results of three QSPR-ANN models showed good precision, confirmed by the high values of determination coefficient (R2) ranging from 0.9990 to 0.9945, and the low values of calculated errors, such as the Mean Absolute Percentage Error (MAPE) that ranged from 2.2497 to 0.7424% for the best three models of Tc, Vc, and Pc. The weight sensitivity analysis method was applied to know the contribution of each input descriptor individually or by class on each appropriate QSPR-ANN model. Moreover, the applicability domain (AD) method was also used with a strict limit of standardized residual values (di = ±2). However, the results were promising, with nearly 88% of the data points validated within the AD range. Finally, the results of the proposed QSPR-ANN models were compared with other well-known QSPR or ANN models for each property. Consequently, our three models provided satisfactory results, outperforming most of the models mentioned in this comparison. This computational approach can be applied in petroleum engineering and other related fields to accurately determine the critical properties of pure hydrocarbons: Tc, Vc, and Pc.

2D-QSAR, docking, molecular dynamics, studies of PF-07321332 analogues to identify alternative inhibitors against 3CLpro enzyme in SARS-CoV disease.

Given the results of the Pfizer-developed inhibitor PF-07321332 in the treatment of the SARS-Covid-19 epidemic, we aimed to identify potential alternatives to this compound by utilizing various methods; we developed 2 D-QSAR models to predict the therapeutic activity of 78 analogues of PF-07321332, three statistical learning techniques including (MLP-ANN), (SVR), and (MLR) were exploited. Various validation approaches were applied to the three models developed following the use of five most relevant descriptors. The study of the characteristics of these descriptors proved that the inhibitory activity of PF-07321332 analogues is specifically affected by the structure of the molecule, its polarizability, and by the hydrogen bonds. The best model, named MLP-ANN (with a 5-3-1 architecture), was selected on the basis of the following statistical parameters: r2 = 0.922, Q2 = 0.921. In addition, we performed a molecular docking and a molecular dynamics analysis of these compounds. The obtained results confirm that compound 8 can be a good alternative for compound PF-07321332.Communicated by Ramaswamy H. Sarma.

Application of artificial neural network (ANN-MLP) for the prediction of fouling resistance in heat exchanger to MgO-water and CuO-water nanofluids.

In this work, an artificial neural network (ANN) model was developed with the aim of predicting fouling resistance for heat exchanger, the network was designed and trained by means of 375 experimental data points that were selected from the literature. These data points contain six inputs, including time, volumetric concentration, heat flux, mass flow rate, inlet temperature, thermal conductivity and fouling resistance as an output. The experimental data are used for training, testing and validation of the ANN using multiple layer perceptron (MLP). The comparison of statistical criteria of different networks shows that the optimal structure for predicting the fouling resistance of the nanofluid is the MLP network with 20 hidden neurons, which has been trained with Levenberg-Marquardt (LM) algorithm. The accuracy of the model was assessed based on three known statistical metrics including mean square error (MSE), mean absolute percentage error (MAPE) and coefficient of determination (R2). The obtained model was found with the performance of {MSE = 6.5377 × 10-4, MAPE = 2.40% and R2 = 0.99756} for the training stage, {MSE = 3.9629 × 10-4, MAPE = 1.8922% and R2 = 0.99835} for the test stage and {MSE = 5.8303 × 10-4, MAPE = 2.57% and R2 = 0.99812} for the validation stage. In order to control the fouling procedure, and after conducting a sensitivity analysis, it found that all input variables have strong effect on the estimation of the fouling resistance.

QSPR estimation models of normal boiling point and relative liquid density of pure hydrocarbons using MLR and MLP-ANN methods.

This work aimed to predict the normal boiling point temperature (Tb) and relative liquid density (d20) of petroleum fractions and pure hydrocarbons, through a multi-layer perceptron artificial neural network (MLP-ANN) based on the molecular descriptors. A set of 223 and 222 diverse data points for Tb and d20 were respectively used to build two quantitative structure property relationships-artificial neural network (QSPR-ANN) models. For each model, the total database was divided respectively into two subsets: 80% for the training set and 20% for the test set. A total of 1666 descriptors were calculated, and the statistical reduction methodology, based on the Multiple Linear Regression (MLR) method, has been adopted. The Quasi-Newton back propagation (BFGS) algorithm was applied in order to train the ANN. A comparison was made between the outcomes of obtained QSPR-ANN models and other well-known correlations for each property. The two best QSPR-ANN models result showed a good accuracy confirmed by the high determination coefficient (R2) values and the low mean absolute percentage error (MAPE) values ranging from 0.9999 to 0.9931 and from 0.5797 to 0.2600%, respectively for both best models (Tb and d20 models). Furthermore, the comparison between our models and the other quantitative structure property relationships (QSPR) models shows that the QSPR-ANN models provided better results. This computational approach can be applied in the petroleum engineering for an accurate determination of Tb and d20 of pure hydrocarbons.

Neuro-fuzzy modeling of ibuprofen-sustained release from tablets based on different cellulose derivatives.

In the present study, we investigated the drug release behavior from cellulose derivative (CD) matrices in the oral form of tablets. We used the adaptive neural-fuzzy inference system (ANFIS) to predict the best formulation parameters to get the perfect sustained drug delivery using ibuprofen (IB) as a model drug. The different formulations were prepared with different CDs, namely CMC, HEC, HPC, HPMC, and MC. The amount of the active ingredient varied between 20 and 50%. The flow properties of the powder mixtures were evaluated for their angle of repose, compressibility index, and Hausner ratio, while the tablets were evaluated for weight uniformity, hardness, friability, drug content, disintegration time, and release ratio. All tablet formulations presented acceptable pharmacotechnical properties. In general, the results showed that the drug release rate increases with an increase in the loaded drug. Kinetic studies using the Korsmeyer-Peppas equation showed that different drug release mechanisms were involved in controlling the drug dissolution from tablets. The drug release mechanism was influenced by the gel layer strength of the CDs formed in the dissolution medium. The mean dissolution time (MDT) was determined and the highest MDT value was obtained for the HPMC formulations. Moreover, HPMC exhibited release profiles adequate for sustained release formulations for over 14 h. The intelligent model based on the experimental data was used to predict the effect of the polymer's nature, the amount of the active ingredient, and the kinetic release profile and rate (R2 = 0.9999 and RMSE = 5.7 × 10-3). The ANFIS model developed in this work could accurately model the relationship between IB release behavior and tablet formulation parameters. The proposed model was able to successfully describe this phenomenon and can be considered an efficient tool with predictive capabilities that is useful for the designing and testing of new dosage systems based on polymers.

QSAR modeling in ecotoxicological risk assessment: application to the prediction of acute contact toxicity of pesticides on bees (Apis mellifera L.).

Despite their indisputable importance around the world, the pesticides can be dangerous for a range of species of ecological importance such as honeybees (Apis mellifera L.). Thus, a particular attention should be paid to their protection, not only for their ecological importance by contributing to the maintenance of wild plant diversity, but also for their economic value as honey producers and crop-pollinating agents. For all these reasons, the environmental protection requires the resort of risk assessment of pesticides. The goal of this work was therefore to develop a validated QSAR model to predict contact acute toxicity (LD50) of 111 pesticides to bees because the QSAR models devoted to this species are very scarce. The analysis of the statistical parameters of this model and those published in the literature shows that our model is more efficient. The QSAR model was assessed according to the OECD principles for the validation of QSAR models. The calculated values for the internal and external validation statistic parameters (Q 2 and [Formula: see text] are greater than 0.85. In addition to this validation, a mathematical equation derived from the ANN model was used to predict the LD50 of 20 other pesticides. A good correlation between predicted and experimental values was found (R 2 = 0.97 and RMSE = 0.14). As a result, this equation could be a means of predicting the toxicity of new pesticides.

Using Central Composite Experimental Design to Optimize the Degradation of Tylosin from Aqueous Solution by Photo-Fenton Reaction.

The feasibility of the application of the Photo-Fenton process in the treatment of aqueous solution contaminated by Tylosin antibiotic was evaluated. The Response Surface Methodology (RSM) based on Central Composite Design (CCD) was used to evaluate and optimize the effect of hydrogen peroxide, ferrous ion concentration and initial pH as independent variables on the total organic carbon (TOC) removal as the response function. The interaction effects and optimal parameters were obtained by using MODDE software. The significance of the independent variables and their interactions was tested by means of analysis of variance (ANOVA) with a 95% confidence level. Results show that the concentration of the ferrous ion and pH were the main parameters affecting TOC removal, while peroxide concentration had a slight effect on the reaction. The optimum operating conditions to achieve maximum TOC removal were determined. The model prediction for maximum TOC removal was compared to the experimental result at optimal operating conditions. A good agreement between the model prediction and experimental results confirms the soundness of the developed model.

A Quantitative Structure Activity Relationship for acute oral toxicity of pesticides on rats: Validation, domain of application and prediction.

Quantitative Structure Activity Relationship (QSAR) models are expected to play an important role in the risk assessment of chemicals on humans and the environment. In this study, we developed a validated QSAR model to predict acute oral toxicity of 329 pesticides to rats because a few QSAR models have been devoted to predict the Lethal Dose 50 (LD50) of pesticides on rats. This QSAR model is based on 17 molecular descriptors, and is robust, externally predictive and characterized by a good applicability domain. The best results were obtained with a 17/9/1 Artificial Neural Network model trained with the Quasi Newton back propagation (BFGS) algorithm. The prediction accuracy for the external validation set was estimated by the Q(2)ext and the root mean square error (RMS) which are equal to 0.948 and 0.201, respectively. 98.6% of external validation set is correctly predicted and the present model proved to be superior to models previously published. Accordingly, the model developed in this study provides excellent predictions and can be used to predict the acute oral toxicity of pesticides, particularly for those that have not been tested as well as new pesticides.