The task here is to predict the toxicological activity of chemical compounds based on the Tox21 dataset, a benchmark in computational toxicology. After a domain-specific overview of chemical toxicity, we discuss current computational strategies, focusing on machine learning and deep learning. Several architectures are compared in terms of performance, robustness, and interpretability. This research introduces a novel image-based pipeline based on DenseNet121, which processes 2D graphical representations of chemical structures. Additionally, we employ Grad-CAM visualizations, an explainable AI technique, to interpret the model's predictions and highlight molecular regions contributing to toxicity classification. The proposed architecture achieves competitive results compared to traditional models, demonstrating the potential of deep convolutional networks in cheminformatics. Our findings emphasize the value of combining image-based representations with explainable AI methods to improve both predictive accuracy and model transparency in toxicology.

A team of researchers working at the Laboratory for Health Protection of the National Institute of Public Health and the Environment in Bilthoven, The Netherlands, in collaboration with the German Centre for the Protection of Laboratory Animals (Bf3R) from the German Federal Institute for Risk Assessment (BfR) in Berlin, Germany, and the Utrecht Institute of Pharmaceutical Sciences of the Utrecht University, Utrecht, The Netherlands, critically emphasize on the need for microphysiological systems to support the innovations in organoids & organ-on-chip microfluidic devices (Schneider et al., 2021). According to the investigators, the strict evaluation of the potentially toxic effects of certain chemicals, including pharmaceutical compounds, on human and environmental health continues to be tough. The complexity of biological processes and the lack of accessibility to in vivo experiments exacerbate this aspect. Therefore, during the past few years, an increasing number of researchers discovered recurring model systems ranging from single cell lines to complex animal models. During the past five years, microphysiological systems mimicking human physiology on a small scale gained great attention.

Computer programs can, in some cases, predict chemical toxicity as well as tests done on rats and other animals.Credit: Coneyl Jay/SPL Machine-learning software trained on masses of chemical-safety data is so good at predicting some kinds of toxicity that it now rivals -- and sometimes outperforms -- expensive animal studies, researchers report. Computer models could replace some standard safety studies conducted on millions of animals each year, such as dropping compounds into rabbits' eyes to check if they are irritants, or feeding chemicals to rats to work out lethal doses, says Thomas Hartung, a toxicologist at Johns Hopkins University in Baltimore, Maryland. "The power of big data means we can produce a tool more predictive than many animal tests." In a paper published in Toxicological Sciences1 on 11 July, Hartung's team reports that its algorithm can accurately predict toxicity for tens of thousands of chemicals -- a range much broader than other published models achieve -- across nine kinds of test, from inhalation damage to harm to aquatic ecosystems. The paper "draws attention to the new possibilities of big data", says Bennard van Ravenzwaay, a toxicologist at the chemicals firm BASF in Ludwigshafen, Germany.