ArticleMolecular systems biology2025
PerturbNet predicts single-cell responses to unseen chemical and genetic perturbations.
Article in Molecular systems biology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers.
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Who cites it
29 citing papers in PubMed.
- VCCV: conservative transcriptomic corroboration for measurement prioritization of computational drug-target hypotheses.Bioinformatics (Oxford, England) · 2026Article
- Pop-Corn: Predicting Perturbation Phenotype Effects Across Single-Cell and Spatial Contexts.bioRxiv : the preprint server for biology · 2026Article
- A systematic comparison of single-cell perturbation response prediction models.Science advances · 2026Article
- PHAROS: turning single-cell perturbation models into target-directed drug-combination screens.bioRxiv : the preprint server for biology · 2026Article
- A transcription factor regulatory atlas for activity inference and perturbation prediction.Nucleic acids research · 2026Article
- DECANT: decoupling mechanism from context in single-cell drug perturbation representation.Bioinformatics (Oxford, England) · 2026Article
- Multi-omics-driven precision medicine.iMeta · 2026Review
- Review
- Harnessing artificial intelligence to advance CRISPR-based genome editing technologies.Nature reviews. Genetics · 2026Review
- MultiPert: An adversarial alignment and dual attention framework for single-cell multi-omics perturbation prediction.PLoS computational biology · 2026Article
- Learning genetic perturbation effects with variational causal inference.PLoS computational biology · 2026Article
- AI-guided ethnopharmacology for cardiovascular drug discovery: from biomedical data to experimental validation.Frontiers in pharmacology · 2026Review
- Dissecting cellular ecosystem with single-cell CRISPR screens.Blood science (Baltimore, Md.) · 2025Article
- The Constrained Disorder Principle: A Paradigm Shift for Accurate Interactome Mapping and Information Analysis in Complex Biological Systems.Bioengineering (Basel, Switzerland) · 2025Review
- GeneRAIN: multifaceted representation of genes via deep learning of gene expression networks.Genome biology · 2025Article
- Multi-view gene panel characterization for spatially resolved omics.Briefings in bioinformatics · 2025Article
- Learning Genetic Perturbation Effects with Variational Causal Inference.bioRxiv : the preprint server for biology · 2025Article
- Machine learning to dissect perturbations in complex cellular systems.Computational and structural biotechnology journal · 2025Review
- Linking transcriptome and morphology in bone cells at cellular resolution with generative AI.Journal of bone and mineral research : the official journal of the American Society for Bone and Mineral Research · 2024Article
- A mini-review on perturbation modelling across single-cell omic modalities.Computational and structural biotechnology journal · 2024Review
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Chemical and genetic perturbations, such as those induced by small molecules and CRISPR, effect complex changes in the molecular states of cells. Despite advances in high-throughput single-cell perturbation screening technology, the space of possible perturbations is far too large to measure exhaustively. Here, we introduce PerturbNet, a flexible deep generative model designed to predict the distribution of cell states induced by unseen chemical or genetic perturbations. PerturbNet accurately predicts gene expression changes in response to unseen small molecules based on their chemical structures while also accounting for key covariates such as dosage and cell type. Moreover, PerturbNet accurately predicts the distribution of single-cell gene expression states following CRISPR activation or CRISPR interference by leveraging gene functional annotations. Our approach significantly outperforms previous methods, particularly for predicting the effects of perturbing completely unseen genes. Finally, we demonstrate for the first time that amino acid sequence embeddings can be used to predict gene expression changes induced by missense mutations. We use PerturbNet to predict the effects of all point mutations in GATA1 and nominate variants that significantly impact the cell state distribution of human hematopoietic stem cells. Using a crystal structure of GATA1 bound to DNA, we validate that these large-effect variants occur in the core DNA-contact region of GATA1 and tend to involve large changes in amino acid side-chain volume.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.