# How to Build the Virtual Cell with Artificial Intelligence: Priorities and Opportunities

arXiv: https://arxiv.org/abs/2409.11654 · HTML: https://mlsanity.com/paper/2409.11654/

## Key facts

- Cited by 57 papers in the map (data through 14 Oct 2026).
- Builds on 33 of its 186 references that are in the map.
- First posted on arXiv 18 Sep 2024.

## Builds on (most cited first, 30 of 33)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [Flow Matching for Generative Modeling](https://mlsanity.com/paper/2210.02747/) (2022)
- [Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles](https://mlsanity.com/paper/1612.01474/) (2016)
- [Transformers are RNNs: Fast Autoregressive Transformers with Linear Attention](https://mlsanity.com/paper/2006.16236/) (2020)
- [A Gentle Introduction to Conformal Prediction and Distribution-Free Uncertainty Quantification](https://mlsanity.com/paper/2107.07511/) (2021)
- [Towards Out-Of-Distribution Generalization: A Survey](https://mlsanity.com/paper/2108.13624/) (2021)
- [DiffDock: Diffusion Steps, Twists, and Turns for Molecular Docking](https://mlsanity.com/paper/2210.01776/) (2022)
- [HyenaDNA: Long-Range Genomic Sequence Modeling at Single Nucleotide Resolution](https://mlsanity.com/paper/2306.15794/) (2023)
- [Empowering Biomedical Discovery with AI Agents](https://mlsanity.com/paper/2404.02831/) (2024)
- [The Promises and Pitfalls of Deep Kernel Learning](https://mlsanity.com/paper/2102.12108/) (2021)
- [Position: Bayesian Deep Learning is Needed in the Age of Large-Scale AI](https://mlsanity.com/paper/2402.00809/) (2024)
- [Supervised Training of Conditional Monge Maps](https://mlsanity.com/paper/2206.14262/) (2022)
- [BioDiscoveryAgent: An AI Agent for Designing Genetic Perturbation Experiments](https://mlsanity.com/paper/2405.17631/) (2024)
- [Masked Autoencoders for Microscopy are Scalable Learners of Cellular Biology](https://mlsanity.com/paper/2404.10242/) (2024)
- [On Uncertainty, Tempering, and Data Augmentation in Bayesian Classification](https://mlsanity.com/paper/2203.16481/) (2022)
- [Aligned Diffusion Schr"odinger Bridges](https://mlsanity.com/paper/2302.11419/) (2023)
- [Modelling Cellular Perturbations with the Sparse Additive Mechanism Shift Variational Autoencoder](https://mlsanity.com/paper/2311.02794/) (2023)
- [Channel Vision Transformers: An Image Is Worth 1 x 16 x 16 Words](https://mlsanity.com/paper/2309.16108/) (2023)
- [Atomic Convolutional Networks for Predicting Protein-Ligand Binding Affinity](https://mlsanity.com/paper/1703.10603/) (2017)
- [On Stein Variational Neural Network Ensembles](https://mlsanity.com/paper/2106.10760/) (2021)
- [Hierarchical Gaussian Process Priors for Bayesian Neural Network Weights](https://mlsanity.com/paper/2002.04033/) (2020)
- [Contextual Vision Transformers for Robust Representation Learning](https://mlsanity.com/paper/2305.19402/) (2023)
- [Unbalanced Diffusion Schr"odinger Bridge](https://mlsanity.com/paper/2306.09099/) (2023)

## Built on by (most cited first, 30 of 57)

- [CellFlux: Simulating Cellular Morphology Changes via Flow Matching](https://mlsanity.com/paper/2502.09775/) (2025)
- [Curly Flow Matching for Learning Non-gradient Field Dynamics](https://mlsanity.com/paper/2510.26645/) (2025)
- [PerturbDiff: Functional Diffusion for Single-Cell Perturbation Modeling](https://mlsanity.com/paper/2602.19685/) (2026)
- [Virtual Cells: Predict, Explain, Discover](https://mlsanity.com/paper/2505.14613/) (2025)
- [AI-powered virtual tissues from spatial proteomics for clinical diagnostics and biomedical discovery](https://mlsanity.com/paper/2501.06039/) (2025)
- [VCWorld: A Biological World Model for Virtual Cell Simulation](https://mlsanity.com/paper/2512.00306/) (2025)
- [Content Generation Models in Computational Pathology: A Comprehensive Survey on Methods, Applications, and Challenges](https://mlsanity.com/paper/2505.10993/) (2025)
- [Generative AI for Biosciences: Emerging Threats and Roadmap to Biosecurity](https://mlsanity.com/paper/2510.15975/) (2025)
- [TEDDY: A Family Of Foundation Models For Understanding Single Cell Biology](https://mlsanity.com/paper/2503.03485/) (2025)
- [Generative Distribution Embeddings: Lifting autoencoders to the space of distributions for multiscale representation learning](https://mlsanity.com/paper/2505.18150/) (2025)
- [OwkinZero: Accelerating Biological Discovery with AI](https://mlsanity.com/paper/2508.16315/) (2025)
- [Flow Autoencoders are Effective Protein Tokenizers](https://mlsanity.com/paper/2510.00351/) (2025)
- [HarmonyCell: Automating Single-Cell Perturbation Modeling under Semantic and Distribution Shifts](https://mlsanity.com/paper/2603.01396/) (2026)
- [Binding Affinity Prediction: From Conventional to Machine Learning-Based Approaches](https://mlsanity.com/paper/2410.00709/) (2024)
- [Efficient Fine-Tuning of Single-Cell Foundation Models Enables Zero-Shot Molecular Perturbation Prediction](https://mlsanity.com/paper/2412.13478/) (2024)
- [MorphGen: Controllable and Morphologically Plausible Generative Cell-Imaging](https://mlsanity.com/paper/2510.01298/) (2025)
- [Aligning LLMs with Biomedical Knowledge using Balanced Fine-Tuning](https://mlsanity.com/paper/2511.21075/) (2025)
- [MapPFN: Learning Causal Perturbation Maps in Context](https://mlsanity.com/paper/2601.21092/) (2026)
- [AssayBench: An Assay-Level Virtual Cell Benchmark for LLMs and Agents](https://mlsanity.com/paper/2605.10876/) (2026)
- [Efficient Cell Painting Image Representation Learning via Cross-Well Aligned Masked Siamese Network](https://mlsanity.com/paper/2509.19896/) (2025)
- [CellFluxRL: Biologically-Constrained Virtual Cell Modeling via Reinforcement Learning](https://mlsanity.com/paper/2603.21743/) (2026)
- [Strategic priorities for transformative progress in advancing biology with proteomics and artificial intelligence](https://mlsanity.com/paper/2502.15867/) (2025)
- [scDataset: Scalable Data Loading for Deep Learning on Large-Scale Single-Cell Omics](https://mlsanity.com/paper/2506.01883/) (2025)
- [Central Dogma Transformer: Towards Mechanism-Oriented AI for Cellular Understanding](https://mlsanity.com/paper/2601.01089/) (2026)
- [Elucidating the Design Space of Flow Matching for Cellular Microscopy](https://mlsanity.com/paper/2603.26790/) (2026)
- [The cell as a token: high-dimensional geometry in language models and cell embeddings](https://mlsanity.com/paper/2503.20278/) (2025)
- [Artificial Intelligence Virtual Cells: From Measurements to Decisions across Modality, Scale, Dynamics, and Evaluation](https://mlsanity.com/paper/2510.12498/) (2025)
- [AI-driven Large-scale Electron Microscopy enables Whole-tissue Subcellular Digitization](https://mlsanity.com/paper/2511.02860/) (2025)
- [TRIDENT: A Trimodal Cascade Generative Framework for Drug and RNA-Conditioned Cellular Morphology Synthesis](https://mlsanity.com/paper/2511.18287/) (2025)
- [Cell-cell Communication Inference and Analysis: Biological Mechanisms, Computational Approaches, and Future Opportunities](https://mlsanity.com/paper/2512.03497/) (2025)

## Abstract (arXiv)

The cell is arguably the most fundamental unit of life and is central to understanding biology. Accurate modeling of cells is important for this understanding as well as for determining the root causes of disease. Recent advances in artificial intelligence (AI), combined with the ability to generate large-scale experimental data, present novel opportunities to model cells. Here we propose a vision of leveraging advances in AI to construct virtual cells, high-fidelity simulations of cells and cellular systems under different conditions that are directly learned from biological data across measurements and scales. We discuss desired capabilities of such AI Virtual Cells, including generating universal representations of biological entities across scales, and facilitating interpretable in silico experiments to predict and understand their behavior using virtual instruments. We further address the challenges, opportunities and requirements to realize this vision including data needs, evaluation strategies, and community standards and engagement to ensure biological accuracy and broad utility. We envision a future where AI Virtual Cells help identify new drug targets, predict cellular responses to perturbations, as well as scale hypothesis exploration. With open science collaborations across the biomedical ecosystem that includes academia, philanthropy, and the biopharma and AI industries, a comprehensive predictive understanding of cell mechanisms and interactions has come into reach.