mlsanityData to 14 Oct 2026

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

arXiv 2409.11654 ↗First posted 18 Sep 2024q-bio.QM cs.AI cs.LGcited by 57

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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.

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

33 of 186 references.

References · 33

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 2; 2025: 21; 2026: 34.

57 papers.

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  1. 2025CellFlux: Simulating Cellular Morphology Changes via Flow Matchingcited by 20
  2. 2025Curly Flow Matching for Learning Non-gradient Field Dynamicscited by 20
  3. 2026PerturbDiff: Functional Diffusion for Single-Cell Perturbation Modelingcited by 8
  4. 2025Virtual Cells: Predict, Explain, Discovercited by 7
  5. 2025AI-powered virtual tissues from spatial proteomics for clinical diagnostics and biomedical discoverycited by 6
  6. 2025VCWorld: A Biological World Model for Virtual Cell Simulationcited by 6
  7. 2025Content Generation Models in Computational Pathology: A Comprehensive Survey on Methods, Applications, and Challengescited by 5
  8. 2025Generative AI for Biosciences: Emerging Threats and Roadmap to Biosecuritycited by 5
  9. 2025TEDDY: A Family Of Foundation Models For Understanding Single Cell Biologycited by 4
  10. 2025Generative Distribution Embeddings: Lifting autoencoders to the space of distributions for multiscale representation learningcited by 4
  11. 2025OwkinZero: Accelerating Biological Discovery with AIcited by 4
  12. 2025Flow Autoencoders are Effective Protein Tokenizerscited by 4
38 more
  1. 2026HarmonyCell: Automating Single-Cell Perturbation Modeling under Semantic and Distribution Shiftscited by 4
  2. 2024Binding Affinity Prediction: From Conventional to Machine Learning-Based Approachescited by 3
  3. 2024Efficient Fine-Tuning of Single-Cell Foundation Models Enables Zero-Shot Molecular Perturbation Predictioncited by 3
  4. 2025MorphGen: Controllable and Morphologically Plausible Generative Cell-Imagingcited by 3
  5. 2025Aligning LLMs with Biomedical Knowledge using Balanced Fine-Tuningcited by 3
  6. 2026MapPFN: Learning Causal Perturbation Maps in Contextcited by 3
  7. 2026AssayBench: An Assay-Level Virtual Cell Benchmark for LLMs and Agentscited by 3
  8. 2025Efficient Cell Painting Image Representation Learning via Cross-Well Aligned Masked Siamese Networkcited by 2
  9. 2026CellFluxRL: Biologically-Constrained Virtual Cell Modeling via Reinforcement Learningcited by 2
  10. 2025Strategic priorities for transformative progress in advancing biology with proteomics and artificial intelligencecited by 1
  11. 2025scDataset: Scalable Data Loading for Deep Learning on Large-Scale Single-Cell Omicscited by 1
  12. 2026Central Dogma Transformer: Towards Mechanism-Oriented AI for Cellular Understandingcited by 1
  13. 2026Elucidating the Design Space of Flow Matching for Cellular Microscopycited by 1
  14. 2025The cell as a token: high-dimensional geometry in language models and cell embeddingscited by 0
  15. 2025Artificial Intelligence Virtual Cells: From Measurements to Decisions across Modality, Scale, Dynamics, and Evaluationcited by 0
  16. 2025AI-driven Large-scale Electron Microscopy enables Whole-tissue Subcellular Digitizationcited by 0
  17. 2025TRIDENT: A Trimodal Cascade Generative Framework for Drug and RNA-Conditioned Cellular Morphology Synthesiscited by 0
  18. 2025Cell-cell Communication Inference and Analysis: Biological Mechanisms, Computational Approaches, and Future Opportunitiescited by 0
  19. 2026Parameter-free representations outperform single-cell foundation models on downstream benchmarkscited by 0
  20. 2026BLINK: Behavioral Latent Modeling of NK Cell Cytotoxicitycited by 0
  21. 2026SCALE:Scalable Conditional Atlas-Level Endpoint transport for virtual cell perturbation predictioncited by 0
  22. 2026Key-Embedded Privacy for Decentralized AI in Biomedical Omicscited by 0
  23. 2026Perspective on Bias in Biomedical AI: Preventing Downstream Healthcare Disparitiescited by 0
  24. 2026AblateCell: A Reproduce-then-Ablate Agent for Virtual Cell Repositoriescited by 0
  25. 2026Intervention-Aware Multiscale Representation Learning from Imaging Phenomics and Perturbation Transcriptomicscited by 0
  26. 2026CellxPert: Inference-Time MCMC Steering of a Multi-Omics Single-Cell Foundation Model for In-Silico Perturbationcited by 0
  27. 2026Reading the Cell, Designing the Cure: Perturbation-Conditioned Molecular Diffusion for Function-Oriented Drug Designcited by 0
  28. 2026BrainDyn: A Sheaf Neural ODE for Forecasting and Generating Brain Dynamicscited by 0
  29. 2026Chreode: A Cell World Model for One-Step Temporal Dynamics and Perturbation Predictioncited by 0
  30. 2026Applications of temporal graph learning for predicting the dynamics of biological systemscited by 0
  31. 2026Querying Counterfactuals on Tissue Graphs with Supervised Disentanglementcited by 0
  32. 2026Knowledge Graphs and Reasoning LLMs for Finding Simple Yet Effective Transcriptomic Perturbation Predictorscited by 0
  33. 2026FreeBridge: Variational Schr"odinger Bridges for Cellular Transition Dynamicscited by 0
  34. 2026PerturbCellRL: Aligning Distributions and Grounding Biology via Post-Training Perturbation Generatorscited by 0
  35. 2026Building artificial intelligence virtual tissue (AIVT) for tissue state representation, feature prediction, and dynamic simulationcited by 0
  36. 2026Leveraging Biokinetic Knowledge Priors for Data-Scarce Bioprocess Modelingcited by 0
  37. 2026Human-Guided Causal Knowledge Injection for Virtual Cellscited by 0
  38. 2026Learning latent progression states from spatial heterogeneity in uterine histopathologycited by 0

50 most cited of 57 All 57 (JSON)