Artificial intelligence - driven toxicogenomics for predictive environmental and pharmaceutical safety

Abstract

Toxicogenomics integrates high-throughput genomic technologies with toxicology to evaluate molecular responses to chemical and pharmaceutical exposures. The increasing volume and complexity of multi-omics data necessitate advanced computational approaches capable of extracting meaningful and predictive insights. In this context, artificial intelligence (AI) and machine learning (ML) techniques—including support vector machines, random forests, and deep learning architectures—are increasingly employed for early toxicity prediction and risk assessment, with reported improvements in predictive accuracy and sensitivity relative to conventional toxicological approaches. This paper explores the role of AI-driven toxicogenomics in enhancing predictive environmental and pharmaceutical safety. It highlights the application of ML-based and data-driven models for the integrative analysis of transcriptomic, proteomic, and metabolomic datasets to identify toxicity-associated biomarkers, elucidate mechanisms of action, and predict adverse drug and environmental effects prior to clinical manifestation. Notably, AI-driven models have demonstrated significant improvements in prediction accuracy, sensitivity, and scalability compared to traditional toxicological approaches, reducing reliance on animal testing and accelerating decision-making processes. Furthermore, the paper discusses the significance of AI-based toxicogenomics in drug development, environmental contaminant screening, and regulatory toxicology, while addressing key challenges such as data heterogeneity, model interpretability, and regulatory acceptance. The convergence of computer science and toxicogenomics represents a robust framework for next-generation predictive toxicology, facilitating safer pharmaceutical development, more precise environmental risk assessment, and evidence-based public health and regulatory decision-making.