predictive modeling

This research study focuses on the utilization of machine learning models to predict transesterification reactions using zirconium-based Metal-Organic Framework (MOF) as a catalyst. Various machine learning algorithms, including Multiple Linear Regression (MLR), Polynomial regression (PLR), Decision Tree (DT), Random Forest (RF), Adaboost (AB), XGBoost (XGB), K-Nearest Neighbors (KNN), Support Vector Machines (SVM), and Artificial Neural Networks (ANN), were employed to analyze the collected data. The performance of each model was evaluated using mean accuracy and median accuracy metrics. Among the tested models, XGBoost exhibited the highest predictive accuracy, with a mean accuracy of 91.07% and a median accuracy of 96.72%. These results demonstrate that XGBoost effectively captures the intricate relationships between the input features and the outcomes of transesterification reactions. Furthermore, a feature importance analysis conducted using XGBoost revealed the relative significance of various factors in the reaction process. The analysis revealed that the preparation method held the highest importance. The factors ranked by importance were: preparation method, catalyst loading, alcohol type, reaction temperature, MOF, reaction time, acid functionalized, base functionalized, metal precursor, and linker. These findings enhance our understanding of transesterification reactions catalyzed by zirconium-based MOFs and highlight the effectiveness of machine learning models in predicting their outcomes.

Computational toxicology is a rapidly growing field that utilizes artificial intelligence (AI) and machine learning (ML) to predict the toxicity of chemical compounds. Computational toxicology is an important tool for assessing the risks associated with the exposure of finfish and shellfish to environmental contaminants. By providing insights into the behavior and effects of these compounds, computational models can help to inform management decisions and protect the health of aquatic ecosystems and the humans who depend on them for food and recreation. In aqua-toxicology research, Quantitative Structure-Activity Relationship (QSAR) models are commonly used to establish the relationship between chemical structures and their aquatic toxicity. Various ML algorithms have been developed to construct QSAR models, including Random Forest (RF), Artificial Neural Networks (ANNs), Support Vector Machines (SVMs), Bayesian networks (BNs), k-Nearest Neighbor (kNN), Probabilistic Neural Networks (PNNs), Naïve Bayes, and Decision Trees. Deep learning techniques, such as Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), have also been applied in computational toxicology to improve the accuracy of QSAR predictions. Moreover, data mining graphs, networks and graph kernels have been utilized to extract relevant features from chemical structures and improve predictive capabilities. In conclusion, the application of artificial intelligence and machine learning in the field of computational toxicology has immense potential to revolutionize aquatic toxicology research. Through the utilization of advanced algorithms and data analysis techniques, scientists can now better understand and predict the effects of various toxicants on aquatic organisms.