In Silico Modeling, Omics, and High-Throughput Strategies for Predicting Aryl Hydrocarbon Receptor Ligands and Characterizing Toxicological Risk: A Scoping Review

Project Description Background and Rationale The aryl hydrocarbon receptor (AhR) is a ligand-activated transcription factor involved in the regulation of xenobiotic responses, cellular signalling, metabolism, immune function, and other biological processes. Its activity can be influenced by diverse chemical entities, including environmental contaminants, pharmaceuticals, natural products, and endogenous or microbial metabolites. Understanding which chemicals interact with AhR, how they influence receptor activity, and whether these interactions contribute to adverse biological effects is important for environmental toxicology, chemical safety assessment, pharmacology, and predictive toxicology. Several complementary approaches have been developed to investigate AhR activity. Computational methods, including quantitative structure–activity relationship modelling, molecular docking, molecular dynamics, pharmacophore modelling, virtual screening, and machine learning, can support the identification and prioritization of potential AhR ligands. High-throughput and high-content screening approaches enable the assessment of chemical activity across large numbers of compounds, while omics technologies—including transcriptomics, proteomics, metabolomics, epigenomics, and single-cell profiling—can characterize molecular changes associated with chemical exposure and receptor activity. Despite these advances, important questions remain concerning the extent to which computational predictions are independently validated, predicted AhR activity is experimentally confirmed, and molecular responses are linked to mechanistically supported toxicological outcomes. A positive prediction or receptor-activation result does not, by itself, establish that a chemical causes toxicity or produces an adverse effect. Differences in datasets, biological systems, experimental conditions, validation strategies, and outcome definitions also complicate comparisons across studies. This scoping review addresses these issues by mapping the available evidence on computational modelling, omics, and high-throughput/high-content approaches used to predict AhR ligands and characterize their biological and toxicological relevance. Aim and Objectives The overall aim is to map the methods, evidence, and methodological limitations associated with predicting AhR ligands and characterizing AhR-related toxicological responses. The specific objectives are to: Identify and characterize the computational methods, algorithms, datasets, and prediction endpoints used in AhR ligand discovery and activity prediction. Describe the chemical classes, compounds, biological systems, and research contexts represented in the literature. Examine the use of high-throughput and high-content screening approaches in assessing AhR binding, activation, modulation, and related biological responses. Map the contributions of omics technologies to the characterization of molecular responses associated with AhR activity. Characterize the validation strategies used in computational studies, distinguishing internal validation, external validation, independent testing, and experimental confirmation where reported. Examine how computational predictions, screening results, molecular responses, and toxicological outcomes are connected within the available evidence. Identify methodological, mechanistic, and translational gaps that may guide future research and improve the reliability of AhR-related predictive toxicology. Review Design and Methods A scoping-review design was selected to capture the breadth and methodological diversity of the literature rather than to estimate a single pooled effect or determine the effectiveness of one intervention. The review follows the Joanna Briggs Institute methodology for scoping reviews and is reported in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR). The search and reporting procedures are guided by PRISMA-S. Eligibility is structured using the Population–Concept–Context framework. The population encompasses chemical entities and biological systems investigated in relation to AhR activity. The concept includes computational modelling, omics technologies, and relevant high-throughput or high-content approaches. The context includes environmental toxicology, chemical safety, toxicogenomics, pharmacology, drug discovery, and human or ecological toxicology. Eligible studies investigate AhR as a primary target, mechanism, or response pathway; employ at least one relevant computational, omics, or high-throughput/high-content approach; and examine AhR binding, activation, agonism, antagonism, modulation, inhibition, ligand prediction, downstream molecular responses, or related toxicological outcomes. Original empirical, computational, and computational–experimental studies published from 1 January 1996 to September 2026 are considered. Reviews and other non-eligible publication types are excluded from the primary evidence synthesis, although relevant reviews may inform background discussion and citation searching. The literature search covers PubMed/MEDLINE, Scopus, Web of Science Core Collection, Embase, and IEEE Xplore, supplemented by backward and forward citation searching. Search concepts combine AhR-related terms, ligand or receptor-activity terms, and methodological terms relating to computational modelling, omics, and high-throughput/high-content screening. Study selection involves title and abstract screening followed by full-text assessment against the eligibility criteria. Data are charted using a standardized form covering bibliographic characteristics, chemical classes, biological systems, computational methods, datasets, model performance, validation procedures, screening assays, omics platforms, experimental confirmation, toxicological endpoints, mechanistic interpretation, and methodological limitations. The evidence is synthesized through descriptive mapping and qualitative thematic analysis. Quantitative findings are summarized descriptively where appropriate, while performance measures are not pooled when differences in datasets, endpoints, assays, or validation designs prevent meaningful comparison. The review does not assign a universal risk-of-bias or methodological-quality score because its primary purpose is to map the evidence and characterize relevant methodological features. Expected Outcomes and Significance The review is expected to provide an integrated overview of the methods used to predict AhR ligands and investigate their biological and toxicological significance. It will describe the distribution of computational, high-throughput/high-content, and omics approaches; identify the biological systems and chemical classes studied; and characterize how validation and experimental evidence are reported. A central expected contribution is an evidence map showing how far studies progress along the prediction-to-outcome continuum, from computational prediction and receptor activity through experimental confirmation and molecular characterization to mechanistically supported toxicological outcomes. This will help identify where evidence is well established and where important links remain incomplete. The review is also expected to identify recurring methodological challenges, including limited external validation, uncertainty regarding model generalizability and applicability domains, dependence on assay and biological context, incomplete integration of omics with functional evidence, and insufficient connections between molecular responses and defined adverse outcomes. The findings may help researchers prioritize independent testing of computational models, improve reporting of prediction performance and applicability, design experiments to confirm predicted AhR activity, integrate molecular profiling with functional assays, and connect mechanistic findings to relevant toxicological endpoints. The review may also inform future research priorities in predictive toxicology and chemical safety assessment. The review will not assume that computational prediction, receptor activation, or molecular changes necessarily demonstrate toxicity. Instead, it will distinguish these levels of evidence and evaluate the extent to which the available literature supports connections between them. Its principal contribution will be a structured account of the current evidence base, its methodological strengths and limitations, and the research gaps that need to be addressed to improve the interpretation and application of AhR-related predictive approaches.