用合成先验预测未知药物效应，实现低开销可解释建模
PerturbPFN: Probing the Limits of Synthetic Priors in Drug Perturbation Modelling
- 基于层级合成结构先验，通过隐式图结构推断药物靶点与作用强度
- 在真实与合成数据上均表现良好，预测精度接近专用模型且推理成本低
- 适合需可解释性与快速响应的药物研发场景
由于未知靶点和作用机制、高维表达响应以及小分子设计空间实验覆盖有限，预测细胞对未见化学扰动的响应极具挑战。我们提出PerturbPFN，一种基于PFN架构的无目标扰动预测模型，采用分层合成结构先验。该模型不直接回归高维表达响应，而是推断隐含系统图、稀疏原子干预靶点及干预强度，并通过结构因果模型解码器传播影响。模型完全在生物启发的图与表达模拟器生成的合成训练样本上训练，实现无需测试时梯度更新的结构化上下文学习。我们在真实单细胞扰动数据和合成基准上评估了该模型，涵盖效应预测、靶点识别与调控结构发现。结果表明，PerturbPFN在保持低推理开销的同时，实现了与专用基线相当的扰动预测性能，并揭示了可解释的中间估计——包括靶点、强度与系统结构。
原文摘要 · Abstract (English)
Predicting cellular responses to unseen chemical perturbations is challenging due to unknown targets and mechanisms, high-dimensional expression responses, and limited experimental coverage of the large small-molecule design space. We propose PerturbPFN, a PFN-style amortized model for unknown-target perturbation prediction under a hierarchical synthetic structural prior. Instead of directly regressing high-dimensional expression responses, PerturbPFN infers a latent system graph, sparse atomic intervention targets, and intervention strengths, then propagates their effects through an SCM decoder. The model is trained entirely on prior-predictive synthetic episodes generated from biologically motivated graph and expression simulators, enabling structured in-context learning without test-time gradient updates. We evaluate PerturbPFN on both real single-cell perturbation data and synthetic benchmarks, covering effect prediction, target identification, and regulatory structure discovery. Our results show that PerturbPFN offers a complementary trade-off to specialized baselines, achieving competitive perturbation prediction with low inference cost while exposing interpretable intermediate estimates of targets, strengths, and system structure.
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