用大模型+生物知识构建可解释的虚拟细胞模拟器
VCWorld: A Biological World Model for Virtual Cell Simulation
- 结合生物知识与大模型推理，实现可解释的细胞模拟
- 在药物扰动预测中表现领先，路径与已有证据一致
- 适合需要机制解释的生物医学研究者使用
虚拟细胞建模旨在预测细胞对扰动的响应。现有模型严重依赖大规模单细胞数据，学习基因表达与扰动间的显式映射。尽管近期模型尝试融合多源生物信息，但其泛化能力受限于数据质量、覆盖度和批次效应。更关键的是，这些模型常为黑箱，仅提供预测而缺乏可解释性或与生物学原理的一致性，削弱了其在科研中的可信度。为此，我们提出VCWorld，一种细胞级别的白盒模拟器，将结构化生物知识与大语言模型的迭代推理能力相结合，构建生物世界模型。VCWorld以数据高效方式重现扰动诱导的信号级联，并生成可解释的逐步预测及明确的机制假设。在药物扰动基准测试中，VCWorld达到当前最优预测性能，推断的机制通路与公开生物证据一致。
原文摘要 · Abstract (English)
Virtual cell modeling aims to predict cellular responses to perturbations. Existing virtual cell models rely heavily on large-scale single-cell datasets, learning explicit mappings between gene expression and perturbations. Although recent models attempt to incorporate multi-source biological information, their generalization remains constrained by data quality, coverage, and batch effects. More critically, these models often function as black boxes, offering predictions without interpretability or consistency with biological principles, which undermines their credibility in scientific research. To address these challenges, we present VCWorld, a cell-level white-box simulator that integrates structured biological knowledge with the iterative reasoning capabilities of large language models to instantiate a biological world model. VCWorld operates in a data-efficient manner to reproduce perturbation-induced signaling cascades and generates interpretable, stepwise predictions alongside explicit mechanistic hypotheses. In drug perturbation benchmarks, VCWorld achieves state-of-the-art predictive performance, and the inferred mechanistic pathways are consistent with publicly available biological evidence.
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