DEEP LEARNING APPROACHES IN AQUATIC TOXICOLOGY STUDIES

Aquatic organisms serve as critical indicators of pollution by toxicsubstances, thereby establishing their role as primary models intoxicological research. The continuous introduction of novel chemicalsinto the environment, driven by advancements in technology, hasheightened the demand for toxicological studies utilizing aquaticorganisms. However, conventional experimental methodologiesfrequently prove inadequate in meeting the toxicity testing requirementsfor a diverse range of chemicals. Consequently, both in vivo and in vitrotoxicology studies have increasingly turned towards innovativeapproaches, such as computer-aided virtual screening. The limitedavailability of resources and qualified personnel necessary for theimplementation of virtual screening techniques underscores the need forheightened awareness regarding this issue. In this context, the presentstudy introduces a variety of deep learning (DL) algorithms, includingfully connected neural networks, convolutional neural networks, recurrent neural networks, long short-term memory networks, graphneural networks, and generative adversarial networks. Furthermore, thisstudy aims to promote the training of professionals in this domain andto facilitate the development of high-performance predictive models forfuture research by exploring the application scenarios of thesealgorithms in aquatic toxicology studies, particularly in environmentalrisk estimation and data generation.