Akademik Veri Yönetim Sistemi
The International Thales Congress on Life, Engineering, Architecture, and Mathematics, Cairo, Mısır, 18 - 19 Ekim 2024, ss.173-195
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.