Toward AI-Driven Digital Organism

This perspective proposes an AI-Driven Digital Organism (AIDO) framework, a system of hierarchical, integrated foundation models designed to capture the multiscale complexity of biology, from molecular interactions to whole-organism physiology. The authors argue that biological research has been hampered by 'one-model-for-one-task' paradigms, leading to data fragmentation and suboptimal performance. AIDO seeks to address this by moving toward a modular, unified system of foundation models trained on massive, multimodal biological datasets. Technically, the blueprint follows three stages: (1) 'Divide and conquer,' creating component modules for distinct modalities (e.g., DNA, protein, cellular); (2) 'Connect the dots,' using advanced deep learning architectures—such as graph neural networks for interactomes and hybrid transformers for long-sequence genomic/transcriptomic data—to harmonize information across scales; and (3) 'Piece it all together,' using differentiable communication graphs and joint optimization to create a unified system. The framework emphasizes the necessity of custom innovations for biology, including hybrid BPE/biological tokenizers for genetic sequences, 2D positional encodings for spatial transcriptomics and protein folding, and VQ-VAEs for latent representation of molecular structures. To support this, the authors introduce a standardized software stack and infrastructure blueprint to manage the intensive computing required for training and deployment. Ultimately, AIDO is envisioned as a foundational paradigm that shifts biology from a traditionally empirical, experimental-only science toward a 'connectionist' field. By enabling in silico simulation, virtual perturbation experiments, and systematic design, the AIDO promises to reduce the reliance on expensive and risky wet-lab experiments, significantly accelerating breakthroughs in drug discovery, personalized medicine, and evolutionary biology.

We present an approach of using AI to model and simulate biology and life. Why is it important? Because at the core of medicine, pharmacy, public health, longevity, agriculture and food security, environmental protection, and clean energy, it is biology at work. Biology in the physical world is too complex to manipulate and always expensive and risky to tamper with. In this perspective, we layout an engineering viable approach to address this challenge by constructing 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.

The authors define future work as a deep-dive into three critical areas: (1) Explainability, leveraging methods like Shapley values and gradient-based attribution to interpret model decisions; (2) Trustworthiness, by establishing rigorous production pipelines, utilizing public large-scale data with privacy protection for adaptation, and standardizing APIs/benchmarks; and (3) Safety, recognizing that regulation should focus on the transition from in silico designs to physical/wet-lab synthesis to prevent malicious or weaponized biological applications.