# Large Language Models Meet Virtual Cell: A Survey

arXiv: https://arxiv.org/abs/2510.07706 · HTML: https://mlsanity.com/paper/2510.07706/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 111 references that are in the map.
- First posted on arXiv 9 Oct 2025.

## Builds on (most cited first, 15 of 15)

- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [ReAct: Synergizing Reasoning and Acting in Language Models](https://mlsanity.com/paper/2210.03629/) (2022)
- [Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context](https://mlsanity.com/paper/1901.02860/) (2019)
- [Understanding the planning of LLM agents: A survey](https://mlsanity.com/paper/2402.02716/) (2024)
- [DNABERT-2: Efficient Foundation Model and Benchmark For Multi-Species Genome](https://mlsanity.com/paper/2306.15006/) (2023)
- [DNA-GPT: Divergent N-Gram Analysis for Training-Free Detection of GPT-Generated Text](https://mlsanity.com/paper/2305.17359/) (2023)
- [Towards Scientific Intelligence: A Survey of LLM-based Scientific Agents](https://mlsanity.com/paper/2503.24047/) (2025)
- [BioDiscoveryAgent: An AI Agent for Designing Genetic Perturbation Experiments](https://mlsanity.com/paper/2405.17631/) (2024)
- [BioRAG: A RAG-LLM Framework for Biological Question Reasoning](https://mlsanity.com/paper/2408.01107/) (2024)
- [RiNALMo: General-Purpose RNA Language Models Can Generalize Well on Structure Prediction Tasks](https://mlsanity.com/paper/2403.00043/) (2024)
- [Evolving Roles of LLMs in Scientific Innovation: Assistant, Collaborator, Scientist, and Evaluator](https://mlsanity.com/paper/2507.11810/) (2025)
- [Multi-modal Self-supervised Pre-training for Regulatory Genome Across Cell Types](https://mlsanity.com/paper/2110.05231/) (2021)
- [From Intention To Implementation: Automating Biomedical Research via LLMs](https://mlsanity.com/paper/2412.09429/) (2024)
- [A Multi-Modal AI Copilot for Single-Cell Analysis with Instruction Following](https://mlsanity.com/paper/2501.08187/) (2025)
- [GENEVIC: GENetic data Exploration and Visualization via Intelligent interactive Console](https://mlsanity.com/paper/2404.04299/) (2024)

## Built on by (most cited first, 1 of 1)

- [CellScientist: From Execution Feedback to Auditable Model-Revision Trajectories for Cellular Perturbation Prediction](https://mlsanity.com/paper/2605.07335/) (2026)

## Abstract (arXiv)

Large language models (LLMs) are transforming cellular biology by enabling the development of "virtual cells"--computational systems that represent, predict, and reason about cellular states and behaviors. This work provides a comprehensive review of LLMs for virtual cell modeling. We propose a unified taxonomy that organizes existing methods into two paradigms: LLMs as Oracles, for direct cellular modeling, and LLMs as Agents, for orchestrating complex scientific tasks. We identify three core tasks--cellular representation, perturbation prediction, and gene regulation inference--and review their associated models, datasets, evaluation benchmarks, as well as the critical challenges in scalability, generalizability, and interpretability.