aiqbench analysis
PerturbPFN advances machine learning in biological discovery by leveraging synthetic data to infer latent system graphs for predicting cellular responses without test-time gradients. This approach offers an interpretable alternative to black-box models, balancing prediction accuracy with low inference costs while identifying sparse atomic intervention targets. The method demonstrates how structured priors can effectively navigate the vast small-molecule design space where experimental coverage is limited.
PerturbPFN: Probing the Limits of Synthetic Priors in Drug Perturbation Modelling
arXiv:2607.23447v1 Announce Type: new 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.
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.
19 pages. Accepted at the 2nd ICML Workshop on Foundation Models for Structured Data (FMSD 2026), Seoul, South Korea
Machine Learning (cs.LG)
arXiv:2607.23447 [cs.LG]
(or arXiv:2607.23447v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2607.23447 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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