Drug Repositioning Using Computer-aided Drug Design (CADD).

Drug repositioning is a method of using authorized drugs for other unusually complex diseases. Compared to new drug development, this method is fast, low in cost, and effective. Through the use of outstanding bioinformatics tools, such as computer-aided drug design (CADD), computer strategies play a vital role in the re-transformation of drugs. The use of CADD's special strategy for target-based drug reuse is the most promising method, and its realization rate is high. In this review article, we have particularly focused on understanding the various technologies of CADD and the use of computer-aided drug design for target-based drug reuse, taking COVID-19 and cancer as examples. Finally, it is concluded that CADD technology is accelerating the development of repurposed drugs due to its many advantages, and there are many facts to prove that the new ligand-targeting strategy is a beneficial method and that it will gain momentum with the development of technology.

Enhancing groundwater vulnerability assessment: Comparative study of three machine learning models and five classification schemes for Cuddalore district.

Most of the groundwater vulnerability assessment methods using machine learning are binary classification. This study attempts multi-class classification models to map the groundwater vulnerability against Nitrate contamination. Further, the significance of the number of classes used in the multi-class classification is studied by considering three and five classes. Three machine learning models, namely Random Forest, Extreme Gradient Boosting and CART, with two classification schemes, were developed for the present study. The parameters used in the conventional DRASTIC method and with an additional parameter, Landuse, have been employed for the study. Evaluation metrics such as Accuracy, Kappa, Positive Predictive Value, Negative Predictive Value, and Area Under the Curve of the Receiver Operating Characteristic (AUC-ROC) were compared among all six models to select the optimal one. Based on the model evaluation metrics and consistent distribution of area among the classes Random Forest model with a three-class classification with an AUC of 0.95 is considered optimum for the selected objective. This study highlights the importance of the data classification process and the selection of the number of classes for ML model prediction in assessing groundwater vulnerability. Leveraging the effectiveness of the Geographic Information system and advanced machine learning techniques, the proposed approach offers valuable insights for enhanced groundwater management and contamination mitigation strategies.

Soft computing-based models and decolorization of Reactive Yellow 81 using Ulva Prolifera biochar.

The present research explores the application of optimization tools namely Response Surface Methodology (RSM), Artificial Neural Network (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) in the decolorization of Reactive Yellow 81 (RY81) from an aqueous solution. The characterization of the biochar was carried out using FTIR, elemental analysis, proximate analysis, BET analysis and Thermogravimetric analysis. Five independent variables namely solution pH, biochar dose, contact time, initial dye concentration and temperature were analyzed using RSM, ANN and ANFIS models. The maximum removal efficiency of 86.4% was obtained and the statistical error analysis was calculated. The correlation coefficient of 0.9665, 0.9998 and 0.9999 was obtained for RSM, ANN and ANFIS models, respectively. Adsorption Isotherm models and kinetic models were used to understand the adsorption mechanism. Maximum monolayer adsorption of 225 mg g-1 was predicted by Hill isotherm model. A partition coefficient of 4.09 L g-1 was obtained at an initial dye concentration of 250 mg L-1. It was revealed from the thermodynamic studies that reactions are endothermic and spontaneous. Further, to check the potential of the biochar, regeneration cycle was studied. The desorption efficiency of 99.5% was achieved at an S/L ratio of 3, regeneration cycles of 2, and sodium hydroxide was found as the best elutant for the desorption.