Application of Artificial Intelligence and Machine Learning in Computational Toxicology in Aquatic Toxicology

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.