CellForge: Agentic Design of Virtual Cell Models
Virtual cell modeling represents an emerging frontier at the intersection of
artificial intelligence and biology, aiming to predict quantities such as
responses to diverse perturbations quantitatively. However, autonomously
building computational models for virtual cells is challenging due to the
complexity of biological systems, the heterogeneity of data modalities, and the
need for domain-specific expertise across multiple disciplines. Here, we
introduce CellForge, an agentic system that leverages a multi-agent framework
that transforms presented biological datasets and research objectives directly
into optimized computational models for virtual cells. More specifically, given
only raw single-cell multi-omics data and task descriptions as input, CellForge
outputs both an optimized model architecture and executable code for training
virtual cell models and inference. The framework integrates three core modules:
Task Analysis for presented dataset characterization and relevant literature
retrieval, Method Design, where specialized agents collaboratively develop
optimized modeling strategies, and Experiment Execution for automated
generation of code. The agents in the Design module are separated into experts
with differing perspectives and a central moderator, and have to
collaboratively exchange solutions until they achieve a reasonable consensus.
We demonstrate CellForge's capabilities in single-cell perturbation prediction,
using six diverse datasets that encompass gene knockouts, drug treatments, and
cytokine stimulations across multiple modalities. CellForge consistently
outperforms task-specific state-of-the-art methods. Overall, CellForge
demonstrates how iterative interaction between LLM agents with differing
perspectives provides better solutions than directly addressing a modeling
challenge. Our code is publicly available at
https://github.com/gersteinlab/CellForge.
68917696f01a094725f834b1
Xiangru Tang
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Zhuoyun Yu
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Jiapeng Chen
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Yan Cui
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Daniel Shao
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Weixu Wang
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Fang Wu
claimed_verified
2025-08-06T19:22:05.506Z
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https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/7oMEoBmaFiCR9K2q9Z_7q.png
Fang Wu
user
fangwu97
68917696f01a094725f834b8
Yuchen Zhuang
68917696f01a094725f834b9
Wenqi Shi
68917696f01a094725f834ba
Zhi Huang
68917696f01a094725f834bb
Arman Cohan
68917696f01a094725f834bc
Xihong Lin
68917696f01a094725f834bd
Fabian Theis
68917696f01a094725f834be
Smita Krishnaswamy
68917696f01a094725f834bf
Mark Gerstein
CellForge: Agentic Design of Virtual Cell Models
Virtual cell modeling represents an emerging frontier at the intersection of
artificial intelligence and biology, aiming to predict quantities such as
responses to diverse perturbations quantitatively. However, autonomously
building computational models for virtual cells is challenging due to the
complexity of biological systems, the heterogeneity of data modalities, and the
need for domain-specific expertise across multiple disciplines. Here, we
introduce CellForge, an agentic system that leverages a multi-agent framework
that transforms presented biological datasets and research objectives directly
into optimized computational models for virtual cells. More specifically, given
only raw single-cell multi-omics data and task descriptions as input, CellForge
outputs both an optimized model architecture and executable code for training
virtual cell models and inference. The framework integrates three core modules:
Task Analysis for presented dataset characterization and relevant literature
retrieval, Method Design, where specialized agents collaboratively develop
optimized modeling strategies, and Experiment Execution for automated
generation of code. The agents in the Design module are separated into experts
with differing perspectives and a central moderator, and have to
collaboratively exchange solutions until they achieve a reasonable consensus.
We demonstrate CellForge's capabilities in single-cell perturbation prediction,
using six diverse datasets that encompass gene knockouts, drug treatments, and
cytokine stimulations across multiple modalities. CellForge consistently
outperforms task-specific state-of-the-art methods. Overall, CellForge
demonstrates how iterative interaction between LLM agents with differing
perspectives provides better solutions than directly addressing a modeling
challenge. Our code is publicly available at
https://github.com/gersteinlab/CellForge.
CellForge, an agentic system using a multi-agent framework, transforms raw single-cell multi-omics data into optimized computational models for virtual cells, outperforming state-of-the-art methods in single-cell perturbation prediction.
multi-agent framework single-cell multi-omics data task analysis method design experiment execution LLM agents gene knockouts drug treatments cytokine stimulations