Virtual cell papers

Kevin Sangster · 7 October 2026

Updated through October 2026
Papers and preprints: 2009–2026

This list builds on Eric Kernfeld’s Virtual Cell literature dump (1 January 2026), with additions through October 2026.

2026

Rishi Verma et al. PIE: Generalizing perturbation effects across unseen perturbations, contexts and datasets
Sanjukta Bhattacharya et al. Elucidating the Design Space of Generative Models for Single-Cell Perturbation Prediction
Elijah Cole et al. Foundation Models Improve Perturbation Response Prediction
Shuizhou Chen et al. SCALE: Scalable Conditional Atlas-Level Endpoint transport for virtual cell perturbation prediction
Jiapeng Chen et al. Pop-Corn: Predicting Perturbation Phenotype Effects Across Single-Cell and Spatial Contexts
Mahshid Heidari et al. Evaluating Single-Cell Perturbation Response Models Is Far from Straightforward
Lehang Li et al. A mechanism-annotated benchmark reveals limited fidelity to drug-response signatures in single-cell perturbation models
Alexis Molina and Xinyi Zhang Perturbation response decomposition enables biologically aligned generalization to unseen perturbations and cellular contexts
Giovanni Palla et al. Tabular Foundation Models Are Competitive Cellular Perturbation Predictors Across Biological Scales
Philipp Sven Lars Schäfer et al. Towards Principled Evaluation of Single-Cell Perturbation Prediction Models
Zijun Song et al. PertDiffBench: Benchmarking Diffusion Models for Single-Cell Perturbation Response Prediction
Junwei Sun et al. Beyond Expression Prediction: Benchmarking Differential Expression Classification in Single-Cell Perturbation Models
Jiacheng Wang et al. Speciesformer learns conserved cellular states for cross-species generative virtual cell modeling
Xi Wang et al. Reliable single-cell perturbations explain and improve model performance
Jon Bezney et al. PHAROS: turning single-cell perturbation models into target-directed drug-combination screens
Giovanni Palla et al. Scalable Single-Cell Gene Expression Generation with Latent Diffusion Models (scLDM) 2025 preprint; 2026 publication

2025

Amir Akbarnejad et al. Mapping and reprogramming human tissue microenvironments with MintFlow
Xiangru Tang et al. CellForge: Agentic Design of Virtual Cell Models
scAgents: A Multi-Agent Framework for Fully Autonomous End-to-End Single-Cell Perturbation Analysis
Dominik Klein et al. CellFlow enables generative single-cell phenotype modeling with flow matching
Ajay Nadig et al. Consequences of training data composition for deep learning models in single-cell biology
Gabriel M. Mejia et al. Diversity by Design: Addressing Mode Collapse Improves scRNA-seq Perturbation Modeling on Well-Calibrated Metrics
Henry E. Miller et al. Deep Learning-Based Genetic Perturbation Models Do Outperform Uninformative Baselines on Well-Calibrated Metrics
Qiyuan Liu et al. Effects of Distance Metrics and Scaling on the Perturbation Discrimination Score
Russell Littman et al. Gene-embedding-based prediction and functional evaluation of perturbation expression responses with PRESAGE
Chujun He et al. MORPH Predicts the Single-Cell Outcome of Genetic Perturbations Across Conditions and Data Modalities
Sergio Romero-Tapiador et al. ScAPE: A lightweight multitask learning baseline method to predict transcriptomic responses to perturbations
Srijit Seal et al. Small molecule bioactivity benchmarks are often well-predicted by counting cells
Syed Asad Rizvi et al. Scaling Large Language Models for Next-Generation Single-Cell Analysis (C2S-Scale)
Jeffrey Zhong et al. Benchmarking gene embeddings from sequence, expression, network, and text models for functional prediction tasks
Emmanuel Noutahi et al. Virtual Cells: Predict, Explain, Discover
Ramon Viñas Torné et al. Systema: a framework for evaluating genetic perturbation response prediction beyond systematic variation
Guadalupe Gonzalez et al. Combinatorial prediction of therapeutic perturbations using causally inspired neural networks
Yicheng Gao et al. Causal disentanglement for single-cell representations and controllable counterfactual generation
Gregory Koytiger et al. Generative genomics accurately predicts future experimental results (GEM-1)
Luka Kovačević et al. No Foundations without Foundations — Why semi-mechanistic models are essential for regulatory biology
Hengshi Yu et al. PerturbNet predicts single-cell responses to unseen chemical and genetic perturbations Journal publication
Lukas Heumos et al. Pertpy: an end-to-end framework for perturbation analysis

2024

Haotian Cui et al. scGPT: toward building a foundation model for single-cell multi-omics using generative AI
Minsheng Hao et al. Large-scale foundation model on single-cell transcriptomics (scFoundation)
Charlotte Bunne et al. How to build the virtual cell with artificial intelligence: Priorities and opportunities
Jennifer E. Rood et al. Toward a foundation model of causal cell and tissue biology with a Perturbation Cell and Tissue Atlas
Nathaniel J. Evans et al. Graph Structured Neural Networks for Perturbation Biology
Thomas Gaudelet et al. Season combinatorial intervention predictions with Salt & Peper

2023

Christina V. Theodoris et al. Transfer learning enables predictions in network biology (Geneformer)
Mohammad Lotfollahi et al. Predicting cellular responses to complex perturbations in high-throughput screens (CPA)
Yusuf Roohani et al. Predicting transcriptional outcomes of novel multigene perturbations with GEARS Published online in 2023; journal issue in 2024
Xingzhi Sun et al. Revealing dynamic temporal trajectories and underlying regulatory networks with Cflows

2022

Fan Yang et al. scBERT as a large-scale pretrained deep language model for cell type annotation of single-cell RNA-seq data
Hengshi Yu et al. PerturbNet predicts single-cell responses to unseen chemical and genetic perturbations Preprint

Foundations (pre-2022)

2019

Mohammad Lotfollahi et al. scGen predicts single-cell perturbation responses

2018

Romain Lopez et al. Deep generative modeling for single-cell transcriptomics (scVI)
Han Chen et al. The Genotype–Phenotype Relationships in the Light of Natural Selection

2017

Brian Cleary et al. Efficient Generation of Transcriptomic Profiles by Random Composite Measurements

2011

Kathrin Plath and William E. Lowry Progress in understanding reprogramming to the induced pluripotent state

2009

Jacob Hanna et al. Direct cell reprogramming is a stochastic process amenable to acceleration