Dear Colleagues,

Big data and artificial intelligence (AI) approaches, including machine and deep learning, are playing an increasingly important role in toxicology research. This field is still in its infancy, and more research is needed in areas such as predictive toxicology, adverse drug reactions, toxicity pathway analysis, environmental toxicology, image analysis, toxicity prediction for occupational and environmental exposure, risk assessment, and AI model explainability. Multi-omics data integration, real-time toxicity monitoring, customized toxicology, human–computer interfaces, ethics and laws, high-throughput screening, medication repurposing, and toxicity prediction across species are among the other fields of research. The purpose of these investigations is to increase the accuracy, robustness, and interpretability of AI models in order to inform risk assessments, improve decision-making in chemical control and public health, and understand the molecular causes of toxicity. Text mining of the scientific literature is one example of how big data and AI may be utilized in toxicology research to uncover new possible targets for drug development by extracting information from publications and identifying commonly cited compounds and biological processes relevant to toxicity.

Social media and digital media can also help toxicology research by collecting large amounts of data on people's interactions with toxic compounds, validating the results of AI models, identifying emerging trends, monitoring environmental exposure, engaging the public in discussions, polling public opinion, and enabling participatory toxicology approaches. However, problems of data privacy, dependability, and bias must be addressed to assure the data's credibility. These platforms can also be used in forensic toxicology to collect information about a person's drug use, create a timeline of drug use, and track a person's movements, but the challenge is to verify the information through other means and not rely on it as the sole basis for any forensic toxicology conclusions.

In the domains indicated above, this Special Issue asks for research on transformative big data, social and digital media, and AI approaches to toxicology. The current research in this context is in its infancy and requires more exploration from this multidisciplinary community.

Prof. Dr. Rashid Mehmood
Guest Editor

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• big data in toxicology
• machine and deep learning in toxicology
• social media and digital media in toxicology
• multi-omics data integration (e.g., genomics, proteomics, metabolomics)
• multimedia (e.g., image, voice, video, natural language) analysis in toxicology
• predictive and personalized toxicology
• adverse drug reactions
• data privacy, dependability, and bias in toxicology
• interpretability and explainability of AI models in toxicology
• ethics and regulations in toxicology