Deep Learning in Environmental Toxicology: Current Progress and Open Challenges

Abstract

Ubiquitous chemicals in the environment may pose a threat to human health and the ecosystem, so comprehensive toxicity information must be obtained. Due to the inability of traditional experimental methods to meet the needs of toxicity testing of a large number of chemicals, in vivo and in vitro assays have been shifted to a new paradigm, computer-assisted virtual screening. However, the commonly used virtual screening techniques, including read-across and machine learning-based quantitative structure−activity relationship (QSAR), have limitations in assessing complicated, high-dimensional, and multimodal bioactivity data. In these cases, deep learning (DL) has emerged as a desirable solution for the application of QSARs in toxicity prediction. Therefore, this paper introduces and discusses (a) architectures of six commonly used DL algorithms (fully connected neural network, convolutional neural network, recurrent neural network, long short-term memory, graph neural network, and generative adversarial network), (b) the application scenarios of six DL algorithms, e.g., toxicity prediction and data generation, and (c) challenges and future trends of DLs in toxicity prediction. We believe that by consolidating toxicological mechanisms and DL algorithms, this survey can help readers to build prediction models with excellent performance while promoting further discussions of the fusion of environmental toxicology and DL.

Advancing Toxicity Predictions: A Review on in Vitro to in Vivo Extrapolation in Next-Generation Risk Assessment

“…Machine learning (ML) algorithms have also been used in IVIVE, which involves screening or predicting in vivo toxicity by combining chemical structure characterization information with in vitro high-throughput screening (HTS) assay data. , However, ML models have inherent limitations, with one key issue being the limited interpretability associated with “black box” methodologies. To address this limitation, the adverse outcome pathway (AOP) framework has been utilized to provide a theoretical basis for IVIVE and enhance the predictive capabilities of ML algorithms. − …”

Applications of Artificial Intelligence, Machine Learning, and Data Analytics in Water Environments

“…The special issue includes several review articles encompassing a wide spectrum, ranging from a historical perspective of water data to computational modeling in wastewater treatment to ML modeling of environmental chemical reactions, environmental toxicology, heavy metal removal, and cyanobacterial harmful algal blooms (HABs) . One significant application of these innovative tools is ML-assisted environmental monitoring, which can address diverse problems, such as predicting effluent nutrients or influent flow rates and nutrient loads at wastewater treatment plants, , formation of disinfection byproducts, drivers of the accumulation of potentially toxic elements in sediments, greenhouse gas emissions, , occurrence of PFAS, water quality assessment, microplastics, microcystins, and differentiation of landfill leachate and domestic sludge .…”