DEEP LEARNING APPROACHES IN AQUATIC TOXICOLOGY STUDIES

Aquatic organisms serve as critical indicators of pollution by toxic

substances, thereby establishing their role as primary models in

toxicological research. The continuous introduction of novel chemicals

into the environment, driven by advancements in technology, has

heightened the demand for toxicological studies utilizing aquatic

organisms. However, conventional experimental methodologies

frequently prove inadequate in meeting the toxicity testing requirements

for a diverse range of chemicals. Consequently, both in vivo and in vitro

toxicology studies have increasingly turned towards innovative

approaches, such as computer-aided virtual screening. The limited

availability of resources and qualified personnel necessary for the

implementation of virtual screening techniques underscores the need for

heightened awareness regarding this issue. In this context, the present

study introduces a variety of deep learning (DL) algorithms, including

fully connected neural networks, convolutional neural networks, recurrent neural networks, long short-term memory networks, graph

neural networks, and generative adversarial networks. Furthermore, this

study aims to promote the training of professionals in this domain and

to facilitate the development of high-performance predictive models for

future research by exploring the application scenarios of these

algorithms in aquatic toxicology studies, particularly in environmental

risk estimation and data generation.