PerturbPFN: Probing the Limits of Synthetic Priors in Drug Perturbation Modelling

• URL: http://arxiv.org/abs/2607.23447v1
• Date: Sun, 26 Jul 2026 04:06:42 GMT
• Title: PerturbPFN: Probing the Limits of Synthetic Priors in Drug Perturbation Modelling
• Abstract summary: PerturbPFN is a PFN-style amortized model for unknown-target perturbation prediction.<n>It infers a latent system graph, sparse atomic intervention targets, and intervention strengths, then propagates their effects through an SCM decoder.<n>We evaluate PerturbPFN on both real single-cell perturbation data and synthetic benchmarks, covering effect prediction, target identification, and regulatory structure discovery.
• Score: 32.58158334613679
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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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