How to build an AI-driven digital organism | Nature Medicine

Abstract Biology lies at the core of medicine, pharmacy, public health and longevity. However, in the physical world, biology is often too complex to manipulate and too expensive and risky to tamper with. In this Perspective, we put forward a vision of using AI to model and simulate biology and life. We present our vision on how to address this challenge through the construction of an AI-driven digital organism (AIDO)—a system of integrated multiscale foundation models—in a modular, connectable and holistic fashion to reflect biological scales, connectedness and complexities. An AIDO opens up a safe, affordable and high-throughput alternative platform for predicting, simulating and programming biology at all levels, from molecules to cells to individuals. We envision that an AIDO is poised to trigger a new wave of better-guided wet-lab experimentation and better-informed first-principle reasoning, which can eventually help us better decode and improve life. This is a preview of subscription content, access via your institution Access options Access through your institution Access Nature and 54 other Nature Portfolio journals Get Nature+, our best-value online-access subscription 27,99 € / 30 days cancel any time Learn more Subscribe to this journal Receive 12 print issues and online access 251,40 € per year only 20,95 € per issue Learn more Buy this article Purchase on SpringerLink Instant access to the full article PDF. 39,95 € Prices may be subject to local taxes which are calculated during checkout Fig. 1: Biology as a multiscale system. Fig. 2: AIDO as a multiscale computational representation of biology. Fig. 3: A three-stage roadmap for building an AIDO. Fig. 4: Principles for integrating different modalities and scales into a unified AIDO. Subjects Computational models Machine learning References Bommasani, R. et al. On the opportunities and risks of foundation models. Preprint at https://arxiv.org/abs/2108.07258 (2021). Dalla-Torre, H. et al. Nucleotide Transformer: building and evaluating robust foundation models for human genomics. Nat. Methods 22 , 287–297 (2025). Article CAS PubMed Google Scholar Nguyen, E. et al. HyenaDNA: long-range genomic sequence modeling at single nucleotide resolution. In Adv. Neural Information Processing Systems Vol. 36 https://proceedings.neurips.cc/paper_files/paper/2023/hash/86ab6927ee4ae9bde4247793c46797c7-Abstract-Conference.html (2023). Nguyen, E. et al. Sequence modeling and design from molecular to genome scale with Evo. Science 386 , eado9336 (2024). Article CAS PubMed PubMed Central Google Scholar Fu, X. et al. A foundation model of transcription across human cell types. Nature 637 , 965–973 (2025). Article CAS PubMed PubMed Central Google Scholar Chen, J. et al. Interpretable RNA foundation model from unannotated data for highly accurate RNA structure and function predictions. Preprint at https://arxiv.org/abs/2204.00300 (2022). Li, S. et al. CodonBERT large language model for mRNA vaccines. Genome Res. 34 , 1027–1035 (2024). Lin, Z. et al. Evolutionary-scale prediction of atomic-level protein structure with a language model. Science 379 , 1123–1130 (2023). Article CAS PubMed Google Scholar Nijkamp, E. et al. ProGen2: exploring the boundaries of protein language models. Cell Syst. 14 , 968–978 (2023). Article CAS PubMed Google Scholar Chen, B. et al. xTrimoPGLM: unified 100-billion-parameter pretrained transformer for deciphering the language of proteins. Nat. Methods 22 , 1028–1039 (2025). Article CAS PubMed Google Scholar Jumper, J. et al. Highly accurate protein structure prediction with AlphaFold. Nature 596 , 583–589 (2021). Article CAS PubMed PubMed Central Google Scholar Abramson, J. et al. Accurate structure prediction of biomolecular interactions with AlphaFold 3. Nature 630 , 493–500 (2024). Article CAS PubMed PubMed Central Google Scholar Zhang, Z. et al. Protein representation learning by geometric structure pretraining. In International Conference on Learning Representations (ICLR) https://openreview.net/forum?id=to3qCB3tOh9 (2023). Theodoris, C. V. et al. Transfer learning enables predictions in network biology. Nature 618 , 616–624 (2023). Article CAS PubMed PubMed Central Google Scholar Hao, M. et al. Large-scale foundation model on single-cell transcriptomics. Nat. Methods 21 , 1481–1491 (2024). Article CAS PubMed Google Scholar Cui, H. et al. scGPT: toward building a foundation model for single-cell multi-omics using generative AI. Nat. Methods 21 , 1470–1479 (2024). Article CAS PubMed Google Scholar Bai, D. et al. scLong: a billion-parameter foundation model for capturing long-range gene context in single-cell transcriptomics. Nat. Commun. 17 , 2380 (2026). Article CAS PubMed PubMed Central Google Scholar Luo, R. et al. BioGPT: generative pre-trained transformer for biomedical text generation and mining. Brief. Bioinform. 23 , bbac409 (2022). Article PubMed Google Scholar Ma, J. et al. Segment anything in medical images. Nat. Commun. 15 , 654 (2024). Article CAS PubMed PubMed Central Google Scholar Bunne, C. et al. How to build the virtual cell with artificial intelligence: priorities and opportunities. Cell 187 , 7045–7063 (2024). Article CAS PubMed PubMed Central Google Scholar Xing, E. P., Deng, M. & Hou, J. Critique of world model. Preprint at https://arxiv.org/abs/2507.05169 (2025). Xing, E. & Song. L. A world model of the virtual cell. GenBio https://genbio.ai/research/virtual-cell-may-3.pdf (3 May 2026). Devlin, J., Chang, M. -W., Lee, K. & Toutanova, K. BERT: pre-training of deep bidirectional transformers for language understanding. In Proc. 2019 Conference of the North American Chapter of the Association for Computational Linguistics (NAACL-HLT) 4171–4186 https://aclanthology.org/N19-1423/ (2019). Radford, A., Narasimhan, K., Salimans, T. & Sutskever, I. Improving language understanding by generative pre-training. OpenAI https://cdn.openai.com/research-covers/language-unsupervised/language_understanding_paper.pdf (2018). van den Oord, A., Vinyals, O. & Kavukcuoglu, K. Neural discrete representation learning. In Adv. Neural Information Processing Systems Vol. 30, 6306–6315 (Curran Associates, 2017). van den Oord, A., Li, Y. & Vinyals, O. Representation learning with contrastive predictive coding. Preprint at https://arxiv.org/abs/1807.03748 (2018). Radford, A. et al. Learning transferable visual models from natural language supervision. In Proc. 38th International Conference on Machine Learning (ICML), PMLR Vol. 139, 8748–8763 (2021). Chen, B. et al. MSAGPT: neural prompting protein structure prediction via MSA generative pre-training. Adv. Neural Inf. Process. Syst . 37 https://proceedings.neurips.cc/paper_files/paper/2024/hash/41f3347f8f47c17bbadaed584e68d8bd-Abstract-Conference.html (2024). Sun, N. et al. Mixture of experts enable efficient and effective protein understanding and design. Preprint at bioRxiv https://doi.org/10.1101/2024.11.29.625425 (2024). Li, P., Cheng, X., Song, L. & Xing, E. Retrieval augmented protein language models for protein structure prediction. Preprint at bioRxiv https://doi.org/10.1101/2024.12.02.626519 (2024). Kipf, T. N. & Welling, M. Semi-supervised classification with graph convolutional networks. In 5th Intl. Conference on Learning Representations (ICLR, 2017). Dai, H., Dai, B. & Song, L. Discriminative embeddings of latent variable models for structured data. In Proc. 33rd International Conference on Machine Learning (ICML), PMLR Vol. 48, 2702–2711 https://proceedings.mlr.press/v48/daib16.html (2016). Gilmer, J., Schoenholz, S. S., Riley, P. F., Vinyals, O. & Dahl, G. E. Neural message passing for quantum chemistry. In Proc. 34th International Conference on Machine Learning (ICML), PMLR Vol. 70, 1263–1272 https://proceedings.mlr.press/v70/gilmer17a.html (2017). Ellington, C. N. et al. Accurate and general DNA representations emerge from genome foundation models at scale. Preprint at bioRxiv https://doi.org/10.1101/2024.12.01.625444 (2024). Zou, S. et al. A large-scale foundation model for RNA function and structure prediction. Preprint at bioRxiv https://doi.org/10.1101/2024.11.28.625345 (2024). Zhang, J. et al. Balancing locality and reconstruction in protein structure tokenizer. Preprint at bioRxiv https://doi.org/10.1101/2024.12.02.626366 (2024). Ho, N. et al. Scaling dense representations for single cell with transcriptome-scale context. Preprint at bioRxiv https://doi.org/10.1101/2024.11.28.625303 (2024). Lundberg, S. M. & Lee, S.-I. A unified approach to interpreting model predictions. In Adv. Neural Information Processing Systems Vol. 30, 4765–4774 (Curran Associates, 2017). Lengerich, B. et al. Contextualized machine learning. Preprint at https://arxiv.org/abs/2310.11340 (2023). Download references Author information Authors and Affiliations GenBio AI, Palo Alto, CA, USA Le Song, Eran Segal & Eric Xing Mohamed bin Zayed University of Artificial Intelligence, Abu Dhabi, United Arab Emirates Le Song, Eran Segal & Eric Xing Weizmann Institute of Science, Rehovot, Israel Eran Segal Carnegie Mellon University, Pittsburgh, PA, USA Eric Xing Authors Le Song View author publications Search author on: PubMed Google Scholar Eran Segal View author publications Search author on: PubMed Google Scholar Eric Xing View author publications Search author on: PubMed Google Scholar Contributions All authors conceived and wrote this Perspective. Corresponding author Correspondence to Eric Xing . Ethics declarations Competing interests All authors have a financial interest in GenBio AI. Peer review Peer review information Nature Medicine thanks Weidi Xie and James Heath for their contribution to the peer review of this work. Primary Handling Editor: Karen O’Leary, in collaboration with the Nature Medicine team. Additional information Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Rights and permissions Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Reprints and permissions About this article Cite this article Song, L., Segal, E. & Xing, E. How to build an AI-driven digital organism. Nat Med (2026). https://doi.org/10.1038/s41591-026-04595-0 Download citation Received : 25 February 2026 Accepted : 16 July 2026 Published : 13 August 2026 Version of record : 13 August 2026 DOI : https://doi.org/10.1038/s41591-026-04595-0 Share this article Anyone you share the following link with will be able to read this content: Get shareable link Sorry, a shareable link is not currently available for this article. Copy shareable link to clipboard Provided by the Springer Nature SharedIt content-sharing initiative