What is this collection about?

Digital twins are computational representations of complex systems that are continuously informed by data. In the context of biological systems, these are rapidly emerging as a powerful paradigm for transforming drug discovery. By integrating mechanistic modelling, multi-modal data, and advanced artificial intelligence, digital twins can provide predictive, interpretable, and actionable insights across scales from molecular interactions to patient-level responses. These developments hold promise for accelerating therapeutic development, improving decision-making, and enabling more precise and adaptive intervention strategies.

With this Collection, the editors at Communications AI & Computing, Nature Biotechnology, Nature Computational Science, Nature Communications, npj Systems Biology and Applications, npj Drug Discovery, and Scientific Reports invite contributions that advance the foundations and applications of high-fidelity digital twins in drug discovery. We particularly welcome work that goes beyond static or narrowly scoped models, demonstrating how digital twins can capture dynamic biological processes, incorporate diverse data streams, and support robust prediction or intervention in realistic settings.

Topics of interest include, but are not limited to:

• Multi-scale and mechanistic modelling frameworks integrating molecular, cellular, organ, and organism-level processes
• Hybrid approaches combining physics-based models with machine learning and generative AI
• Data assimilation, uncertainty quantification, and model validation for digital twin systems
• Digital twins for target identification, biomarker discovery, and therapeutic optimization
• Patient-specific or cohort-level modelling for stratification and response prediction
• Integration of real-world, clinical, and experimental data into continuously updated models
• Scalable and interoperable infrastructures for digital twin deployment
• Interpretability, robustness, and regulatory considerations in translational applications

The Collection primarily welcomes original research Articles. Communications AI & Computing will also consider Reviews and Perspectives that synthesize emerging directions in this field. We encourage submissions from all authors—and not by invitation only.

Why is this collection important?

This Collection supports and amplifies research directly related to the United Nation's Sustainable Development Goal 3 – Good Health and Well-being.

Why submit to a collection?

Collections like this one help promote high-quality science. They are led by In-House Editors who are experts in their fields and supported by a dedicated team of Commissioning Editors and Managing Editors at Springer Nature. Collection manuscripts typically see higher citations, downloads, and Altmetric scores, and provide a one-stop-shop on a cutting-edge topic of interest.

Who is involved?

This is a cross-journal Collection involving Communications AI & Computing, Nature Biotechnology, Nature Computational Science, Nature Communications, npj Systems Biology and Applications, npj Drug Discovery, and Scientific Reports. Authors are welcome to submit to any of the participating journals. All manuscripts published in the collection are hosted on a dedicated nature portfolio portal, giving articles high visibility beyond the journal they are published in.

How can I submit my paper?

Visit the Collection page to find out more about this collection and submit your article.