# Virtual Cells: Predict, Explain, Discover

arXiv: https://arxiv.org/abs/2505.14613 · HTML: https://mlsanity.com/paper/2505.14613/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 118 references that are in the map.
- First posted on arXiv 20 May 2025.

## Builds on (most cited first, 12 of 12)

- [GFlowNets for AI-Driven Scientific Discovery](https://mlsanity.com/paper/2302.00615/) (2023)
- [How to Build the Virtual Cell with Artificial Intelligence: Priorities and Opportunities](https://mlsanity.com/paper/2409.11654/) (2024)
- [Generative Modeling of Molecular Dynamics Trajectories](https://mlsanity.com/paper/2409.17808/) (2024)
- [Gotta be SAFE: A New Framework for Molecular Design](https://mlsanity.com/paper/2310.10773/) (2023)
- [On the Scalability of GNNs for Molecular Graphs](https://mlsanity.com/paper/2404.11568/) (2024)
- [SynFlowNet: Design of Diverse and Novel Molecules with Synthesis Constraints](https://mlsanity.com/paper/2405.01155/) (2024)
- [Benchmarking Transcriptomics Foundation Models for Perturbation Analysis : one PCA still rules them all](https://mlsanity.com/paper/2410.13956/) (2024)
- [Goal-conditioned GFlowNets for Controllable Multi-Objective Molecular Design](https://mlsanity.com/paper/2306.04620/) (2023)
- [Training and Serving System of Foundation Models: A Comprehensive Survey](https://mlsanity.com/paper/2401.02643/) (2024)
- [Automated Discovery of Pairwise Interactions from Unstructured Data](https://mlsanity.com/paper/2409.07594/) (2024)
- [Generative artificial intelligence for de novo protein design](https://mlsanity.com/paper/2310.09685/) (2023)
- [Efficient Biological Data Acquisition through Inference Set Design](https://mlsanity.com/paper/2410.19631/) (2024)

## Built on by (most cited first, 7 of 7)

- [VCWorld: A Biological World Model for Virtual Cell Simulation](https://mlsanity.com/paper/2512.00306/) (2025)
- [Efficient Cell Painting Image Representation Learning via Cross-Well Aligned Masked Siamese Network](https://mlsanity.com/paper/2509.19896/) (2025)
- [Elucidating the Design Space of Flow Matching for Cellular Microscopy](https://mlsanity.com/paper/2603.26790/) (2026)
- [Towards Autonomous Mechanistic Reasoning in Virtual Cells](https://mlsanity.com/paper/2604.11661/) (2026)
- [Catastrophic Compositional Generation: Why Vanilla Diffusion Models Fail to Extrapolate](https://mlsanity.com/paper/2606.23920/) (2026)
- [Knowledge Graphs and Reasoning LLMs for Finding Simple Yet Effective Transcriptomic Perturbation Predictors](https://mlsanity.com/paper/2606.08816/) (2026)
- [OmniVCBench: Benchmarking Evidence-Grounded Multimodal Reasoning Towards AI Virtual Cells](https://mlsanity.com/paper/2609.37773/) (2026)

## Abstract (arXiv)

Drug discovery is fundamentally a process of inferring the effects of treatments on patients, and would therefore benefit immensely from computational models that can reliably simulate patient responses, enabling researchers to generate and test large numbers of therapeutic hypotheses safely and economically before initiating costly clinical trials. Even a more specific model that predicts the functional response of cells to a wide range of perturbations would be tremendously valuable for discovering safe and effective treatments that successfully translate to the clinic. Creating such virtual cells has long been a goal of the computational research community that unfortunately remains unachieved given the daunting complexity and scale of cellular biology. Nevertheless, recent advances in AI, computing power, lab automation, and high-throughput cellular profiling provide new opportunities for reaching this goal. In this perspective, we present a vision for developing and evaluating virtual cells that builds on our experience at Recursion. We argue that in order to be a useful tool to discover novel biology, virtual cells must accurately predict the functional response of a cell to perturbations and explain how the predicted response is a consequence of modifications to key biomolecular interactions. We then introduce key principles for designing therapeutically-relevant virtual cells, describe a lab-in-the-loop approach for generating novel insights with them, and advocate for biologically-grounded benchmarks to guide virtual cell development. Finally, we make the case that our approach to virtual cells provides a useful framework for building other models at higher levels of organization, including virtual patients. We hope that these directions prove useful to the research community in developing virtual models optimized for positive impact on drug discovery outcomes.