Toward AI-Driven Digital Organism: Multiscale Foundation Models for Predicting, Simulating and Programming Biology at All Levels
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Abstract (arXiv)
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.
Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 17 of its 63 references that are in the map. First posted on arXiv 9 Dec 2024.
Builds on
17 of 63 references.
References · 17
- 2023GPT-4 Technical Reportcited by 18,396
- 2023LLaMA: Open and Efficient Foundation Language Modelscited by 14,132
- 2015Distilling the Knowledge in a Neural Networkcited by 10,324
- 2018Representation Learning with Contrastive Predictive Codingcited by 7,777
- 2023Mamba: Linear-Time Sequence Modeling with Selective State Spacescited by 4,718
- 2021On the Opportunities and Risks of Foundation Modelscited by 4,033
- 2022FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awarenesscited by 3,673
- 2024Mixtral of Expertscited by 1,823
- 2019Generating Long Sequences with Sparse Transformerscited by 1,546
- 2017Mixed Precision Trainingcited by 1,156
- 2023Gemini: A Family of Highly Capable Multimodal Modelscited by 872
- 2023LongNet: Scaling Transformers to 1,000,000,000 Tokenscited by 204
5 more
- 2022Protein Representation Learning by Geometric Structure Pretrainingcited by 162
- 2023Continual Pre-Training of Large Language Models: How to (re)warm your model?cited by 137
- 2024xTrimoPGLM: Unified 100B-Scale Pre-trained Transformer for Deciphering the Language of Proteincited by 50
- 2023Contextualized Machine Learningcited by 4
- 2024From Glucose Patterns to Health Outcomes: A Generalizable Foundation Model for Continuous Glucose Monitor Data Analysiscited by 2
Built on by
Citing papers per year: 2025: 3; 2026: 2.
5 papers.
- 2025Computational Protein Science in the Era of Large Language Models (LLMs)cited by 6
- 2025MergeDNA: Context-aware Genome Modeling with Dynamic Tokenization through Token Mergingcited by 2
- 2025Benchmarking and Evaluation of AI Models in Biology: Outcomes and Recommendations from the CZI Virtual Cells Workshopcited by 1
- 2026PRiMeFlow: Capturing Complex Expression Heterogeneity in Perturbation Response Modellingcited by 1
- 2026"Rebuilding" Statistics in the Age of AI: A Town Hall Discussion on Culture, Infrastructure, and Trainingcited by 0
- 2026PRiMeFlow: Capturing Complex Expression Heterogeneity in Perturbation Response Modelling15 Apr 2026
- 2026"Rebuilding" Statistics in the Age of AI: A Town Hall Discussion on Culture, Infrastructure, and Training24 Jan 2026
- 2025MergeDNA: Context-aware Genome Modeling with Dynamic Tokenization through Token Merging17 Nov 2025
- 2025Benchmarking and Evaluation of AI Models in Biology: Outcomes and Recommendations from the CZI Virtual Cells Workshop14 Jul 2025
- 2025Computational Protein Science in the Era of Large Language Models (LLMs)17 Jan 2025