Computational approaches are becoming indispensable in the field of translational pharmacology and systems medicine. In particular, the integration of computational modelling and network medicine is reshaping our understanding of drug toxicity, safety profiling, and clinical pharmacovigilance. With the rapid accumulation of multi-omics, electronic health records, and real-world pharmacovigilance data, there is an urgent need for advanced computational frameworks to identify hidden drug–target–phenotype associations and elucidate mechanisms underlying adverse drug reactions (ADRs) and toxicities. Drug toxicology and pharmacovigilance are key components of translational medicine, bridging molecular mechanisms with patient outcomes. However, predicting and preventing drug-induced toxicities remain challenging due to the complexity of biological systems and individual heterogeneity. Network-based and machine learning methods, such as graph neural networks, causal inference, and mechanistic modelling, are providing new opportunities to reveal system-level toxicity pathways, optimize drug safety assessment, and enhance early signal detection in clinical practice.

This collection aims to gather latest research and comprehensive reviews that explore computational, network-based, and AI-driven strategies in drug toxicology and pharmacovigilance. We seek contributions that demonstrate how integrative modelling, network pharmacology, and multi-scale data analysis can advance the prediction, understanding, and management of drug-related adverse effects, ultimately contributing to safer and more effective therapeutics.

Potential topics include, but are not limited to:

- Computational modelling for drug toxicity prediction

- Network medicine approaches to drug safety and toxicity mechanisms

-Machine learning and AI in pharmacovigilance signal detection

-Integrative modelling of drug–target–pathway interactions

-Systems pharmacology and multi-omics network analysis in toxicology

-Network-based biomarker discovery for adverse drug reactions

-Data-driven prediction of idiosyncratic and immune-related toxicities

-Translational applications of network and computational models in clinical drug safety assessment

All submissions in this collection undergo the journal’s standard peer review process. Similarly, all manuscripts authored by a Guest Editor(s) will be handled by the Editor-in-Chief. As an open access publication, this journal levies an article processing fee (details here). We recognize that many key stakeholders may not have access to such resources and are committed to supporting participation in this issue wherever resources are a barrier. For more information about what support may be available, please visit OA funding and support, or email OAfundingpolicy@springernature.com or the Editor-in-Chief.

The Neuroscience section aims to foster publications that bridge the gap between basic preclinical research and its medical applications for patients in the neuroscience field. We welcome contributions that present new biomarkers, therapeutic targets, novel tools, methods, or strategies to advance in the development of new solutions for neurological patients. This section seeks to expand knowledge in the discipline by prioritizing the publication of high-quality original research.