[论文解读] Virtual Cells: Predict, Explain, Discover

Emmanuel Noutahi, Jason Hartford+13 more|ArXiv.org|May 20, 2025

Cell Image Analysis Techniques被引用 3

一句话总结

这一观点概述了构建虚拟细胞，预测对干扰的细胞反应，通过分子机制解释它们，并通过实验室循环实验发现可治疗的可操作生物学，利用 AI/ML 与多模态数据。

ABSTRACT

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.

研究动机与目标

• 推动开发能够在不同背景和模态下预测细胞对扰动的功能性反应的虚拟细胞。
• 主张对预测结果的机制性、以生物学为基础的解释，以实现可证伪性和假设生成。
• 提出一个实验室-循环的范式，通过实验反馈迭代改进虚拟细胞。
• 倡导有生物学意义的基准来指导虚拟细胞的发展。
• 提示虚拟细胞的框架可以扩展到更高的组织层级，包括虚拟患者。

提出的方法

• 将 Predict-Explain-Discover（P-E-D）能力作为虚拟细胞的核心要求。
• 描述设计原则，如在初始细胞状态的条件下预测相对变化。
• 主张将解释建立在关键生物分子相互作用和动态扰动之上。
• 概述将基于 ML 的干预数据、结构信息驱动推理和原子级见解整合以锚定解释。
• 推动一个实验室-循环工作流程，在其中虚拟细胞产生可验证的假设并从实验结果中更新。
• 为虚拟细胞模型推荐有生物学意义的基准和评估标准。

实验结果

研究问题

• RQ1 虚拟细胞如何在不同背景和模态下准确预测细胞对扰动的功能性反应？
• RQ2 机制性解释应采取何种形式才能在科学上具有可证伪性且在生物学上具可操作性？
• RQ3 实验室-循环实验如何通过实验反馈改进虚拟细胞，以发现新生物学及具有治疗相关性的假设？
• RQ4 哪些基准应指导虚拟细胞在药物发现中的开发与评估？

主要发现

• 虚拟细胞应相对于细胞的初始状态预测功能性变化，以提高对上下文的敏感性。
• 解释应将扰动框定为对关键分子相互作用的动态改变，并以结构/生物物理洞见为锚点。
• 一个实验室-循环范式可以通过实验反馈迭代地证伪预测并改进对生物学的理解。
• 多模态数据整合与先进的 AI/ML 能实现可扩展的预测和解释，而无需对整个机制进行仿真。
• 以生物学为基础的基准对于在多层组织尺度上引导虚拟细胞的发展与评估至关重要。