Awesome-Virtual-Cell

Awesome Virtual Cell

A curated list of papers, datasets, benchmarks, talks, and community resources for AI-powered virtual cell research.

Here, AIVC stands for Artificial Intelligence Virtual Cell, a term popularized by the Cell perspective “How to Build the Virtual Cell with Artificial Intelligence: Priorities and Opportunities.” The focus of this repository is broad but practical: resources that help researchers understand, model, benchmark, or build virtual cells and related cellular foundation models.

For scientific figure ideas and plotting templates, see Awesome Scientific Figures.

Contents

• Scope
• Inclusion Rules
• Overview Papers
• Research Papers
• Datasets
• Reports and Blogs
• Videos
• Historical and Foundational Works
• Related Resources

Scope

• In scope: virtual cell perspectives, perturbation modeling, single-cell and multimodal foundation models, spatial and morphology modeling, biological AI agents, datasets, benchmarks, and community resources closely connected to virtual cell research.
• Also included: adjacent work that is broadly useful for the virtual cell community, especially when it contributes data, evaluation methods, or modeling tools for cellular systems.
• Usually out of scope: generic biomedical AI work with weak cell-modeling relevance, low-confidence secondary sources, broken links, or items that do not add clear value beyond more central references already listed here.

Inclusion Rules

• Prefer peer-reviewed papers, high-signal preprints, official project pages, and primary-source links.
• Include code, datasets, project pages, or Chinese summaries when they are clearly useful.
• Keep entries concise and broadly reusable for readers who are scanning the field.
• Use lightweight tags in Research Papers only as browsing aids. They are intentionally approximate, not rigid taxonomy.

Overview Papers

• [Nature News] Can AI Build a Virtual Cell? Scientists Race to Model Life’s Smallest Unit (Nature 2025) [paper] [中文解读]

• [Nature Perspective] Towards Multimodal Foundation Models in Molecular Cell Biology (Nature 2025) [paper] [中文解读]

• [Nature Methods] The virtual cell (Nature Methods 2025) [paper]

• [Nature] The Human Cell Atlas from a cell census to a unified foundation model (Nature 2024) [paper]

• [Nature Review] Interpretation, extrapolation and perturbation of single cells (Nature Reviews Genetics 2026) [paper]

• [Nature Review] Revisiting the blueprint for an interpretable virtual cell (Nature Reviews Genetics 2026) [paper]

• [npj Digital Medicine] AI-driven virtual cell models in preclinical research: technical pathways, validation mechanisms, and clinical translation potential (npj Digital Medicine 2025) [paper]

• [Nature Review] Adapting systems biology to address the complexity of human disease in the single-cell era (Nature Reviews Genetics 2025) [paper]

• [Nature Genetics] Causal machine learning for single-cell genomics (Nature Genetics 2025) [paper]

• [Nature Methods] Multimodal foundation transformer models for multiscale genomics (Nature Methods 2025) [paper]

• [Cell Perspective] Empowering Biomedical Discovery with AI Agents (Cell 2024) [paper] [中文解读]

• [Cell Perspective] How to Build the Virtual Cell with Artificial Intelligence: Priorities and Opportunities (Cell 2024) [paper] [中文解读]

• [Cell Review] Toward a Foundation Model of Causal Cell and Tissue Biology with a Perturbation Cell and Tissue Atlas (Cell 2024) [paper] [中文解读]

Research Papers

Tag hints: [Virtual Cell], [Perturbation], [Foundation Model], [Spatial], [Morphology], [Agent], [Benchmark], [Tool], [Related]. Tags are lightweight and non-exhaustive.

2026

• [Dataset Size & Diversity] Evaluating the role of pretraining dataset size and diversity on single-cell foundation model performance (Nature Methods 2026) [paper]

• [Lingshu-Cell] [Virtual Cell] Lingshu-Cell: A generative cellular world model for transcriptome modeling toward virtual cells (arXiv 2026) [paper]

• [SCALE] [Perturbation] SCALE: Scalable Conditional Atlas-Level Endpoint transport for virtual cell perturbation prediction (arXiv 2026) [paper]

• [Conditional Monge Gap] [Perturbation] Conditional Monge Gap enables generalizable single-cell perturbation modelling (Nature Machine Intelligence 2026) [paper]

• [ProtiCelli] [Virtual Cell] Generative machine learning unlocks the first proteome-wide image of human cells (bioRxiv 2026) [paper] [code]

• [AetherCell] [Virtual Cell] AetherCell: A generative engine for virtual cell perturbation and in vivo drug discovery (bioRxiv 2026) [paper]

• [AlphaCell] [Virtual Cell] Towards building a World Model to simulate perturbation-induced cellular dynamics by AlphaCell (bioRxiv 2026) [paper]

• [VCWorld] [Virtual Cell] VCWorld: A Biological World Model for Virtual Cell Simulation (ICLR 2026 Poster) [paper] [code] [ask deepwiki]

• [Spatial Perturb-seq] [Spatial] Spatial perturb-seq: single-cell functional genomics within intact tissue architecture (Nature Communications 2026) [paper][code]

• [Celcomen] [Spatial] Celcomen: spatial causal disentanglement for single-cell and tissue perturbation modeling (Nature Communications 2026) [paper] [code]

• [stVCR] [Spatial] stVCR: spatiotemporal dynamics of single cells (Nature Methods 2026) [paper] [code]

• [CONCORD] [Foundation Model] Revealing a coherent cell-state landscape across single-cell datasets with CONCORD (Nature Biotechnology 2026) [paper][code]

• [AI Scientist] [Related] Towards end-to-end automation of AI research (Nature 2026) [paper] [code]

• [Conformation Description Language] [Related] Bridging three-dimensional molecular structures and artificial intelligence with a conformation description language (Nature Machine Intelligence 2026) [paper]

• [CRISPRi Map] [Perturbation] A genome-scale single-cell CRISPRi map of trans gene regulation across human pluripotent stem cell lines (Cell Genomics 2026) [paper]

• [TxPert] [Perturbation] TxPert: using multiple knowledge graphs for prediction of transcriptomic perturbation effects (Nature Biotechnology 2026) [paper] [code]

• [Therapeutic Design] [Related] Deep-learning-based de novo discovery and design of therapeutics that reverse disease-associated transcriptional phenotypes (Cell 2026) [paper]

• [X-Pert] [Perturbation] Unified Multimodal Learning Enables Generalized Cellular Response Prediction to Diverse Perturbations (bioRxiv) [paper] [code] [ask deepwiki]

• [MVCBench] [Benchmark] MVCBench: A Multimodal Benchmark for Drug-induced Virtual Cell Phenotypes (bioRxiv) [paper] [code] [ask deepwiki]

• [HarmonyCell] [Perturbation] HarmonyCell: Automating Single-Cell Perturbation Modeling under Semantic and Distribution Shifts (bioRxiv) [paper]

• [scDFM] [Perturbation] scDFM: Distributional Flow Matching Model for Robust Single-Cell Perturbation Prediction (ICLR 2026 Poster) [paper] [code]

• [Doloris] [Perturbation] Doloris: Dual Conditional Diffusion Implicit Bridges with Sparsity Masking Strategy for Unpaired Single-Cell Perturbation Estimation (ICLR 2026 Poster) [paper] [code]

• [Departures] [Perturbation] Departures: Distributional Transport for Single-Cell Perturbation Prediction with Neural Schrödinger Bridges (AAAI 2026) [paper] [preprint]

• [PETRI] [Foundation Model] PETRI: Learning Unified Cell Embeddings from Unpaired Modalities via Early-Fusion Joint Reconstruction (ICLR 2026 Poster) [paper]

• [STRAND] [Perturbation] STRAND: Sequence-Conditioned Transport for Single-Cell Perturbations (arXiv 2026) [paper]

• [PerturbDiff] [Perturbation] PerturbDiff: Functional Diffusion for Single-Cell Perturbation Modeling (arXiv 2026) [paper] [project]

• [scBIG] [Perturbation] Beyond Independent Genes: Learning Module-Inductive Representations for Gene Perturbation Prediction (arXiv 2026) [paper]

• [CellxPert] [Perturbation] CellxPert: Inference-Time MCMC Steering of a Multi-Omics Single-Cell Foundation Model for In-Silico Perturbation (arXiv 2026) [paper]

• [Perturbation Representation] [Perturbation] What Makes a Representation Good for Single-Cell Perturbation Prediction? (arXiv 2026) [paper]

• [CisTransCell] [Perturbation] CisTransCell: Single-Cell Perturbation Prediction via Gene Function, Regulatory Control, and Cellular Context (arXiv 2026) [paper]

• [Latent Causal Processes] [Perturbation] Learning Latent Dynamical Causal Processes for Single-Cell Perturbation Prediction (arXiv 2026) [paper]

• [msInfer] [Tool] Large-scale proteome inference from unpaired single-cell transcriptomic and proteomic data by msInfer (Research Square 2026) [paper]

• [Stack] [Foundation Model] Stack: In-Context Learning of Single-Cell Biology (bioRxiv) [paper] [code] [ask deepwiki]

2025

• [Pertpy] [Tool] Pertpy: an End-to-end Framework for Perturbation Analysis (Nature Methods) [paper] [code] [ask deepwiki]

• [Benchmarking] [Benchmark] Benchmarking Algorithms for Generalizable Single-Cell Perturbation Response Prediction (Nature Methods) [paper] [code] [ask deepwiki]

• [Scouter] [Perturbation] Scouter predicts transcriptional responses to genetic perturbations with large language model embeddings (Nature Computational Science 2025) [paper]

• [GPerturb] [Perturbation] GPerturb: Gaussian process modelling of single-cell perturbation data (Nature Communications 2025) [paper] [code]

• [Squidiff] [Perturbation] Squidiff: Predicting Cellular Development and Responses to Perturbations using a Diffusion Model (Nature Methods) [paper] [code] [ask deepwiki]

• [Nicheformer] [Spatial] Nicheformer: A Foundation Model for Single-Cell and Spatial Omics (Nature Methods) [paper] [中文解读] [code] [ask deepwiki]

• [NicheCompass] [Spatial] Quantitative characterization of cell niches in spatially resolved omics data (Nature Genetics 2025) [paper] [code]

• [STAMP] [Tool] STAMP: Single-cell transcriptomics analysis and multimodal profiling through imaging (Cell 2025) [paper]

• [Perturb-FISH] [Spatial] Simultaneous CRISPR screening and spatial transcriptomics reveal intracellular, intercellular, and functional transcriptional circuits (Cell 2025) [paper]

• [ADLF] [Perturbation] Active Learning Framework Leveraging Transcriptomics Identifies Modulators of Disease Phenotypes (Science) [paper] [code]

• [Tahoe-x1] [Foundation Model] Tahoe-x1: Scaling Perturbation-Trained Single-Cell Foundation Models to 3 Billion Parameters (bioRxiv 2025) [paper] [code] [ask deepwiki] [hugging face files]

• [LPM] [Perturbation] In Silico Biological Discovery with Large Perturbation Models (Nature Computational Science 2025) [paper] [中文解读] [code] [ask deepwiki]

• [CellNavi] [Perturbation] CellNavi Predicts Genes Directing Cellular Transitions by Learning a Gene Graph-Enhanced Cell State Manifold (Nature Cell Biology 2025) [paper] [中文解读] [code] [ask deepwiki]

• [EpiAgent] [Foundation Model] EpiAgent: Foundation Model for Single-Cell Epigenomics (Nature Methods 2025) [paper] [中文解读] [code] [ask deepwiki]

• [CellWhisperer] [Tool] Multimodal learning enables chat-based exploration of single-cell data (Nature Biotechnology 2025) [paper] [code]

• [CRISPR-GPT] [Agent] CRISPR-GPT for Agentic Automation of Gene-Editing Experiments (Nature Biomedical Engineering 2025) [paper] [中文解读] [code] [ask deepwiki]

• [Cell-o1] [Agent] Cell-o1: Training LLMs to Solve Single-Cell Reasoning Puzzles with Reinforcement Learning (arXiv 2025) [paper] [code] [hugging face] [ask deepwiki]

• [Systema] [Benchmark] Systema: A Framework for Evaluating Genetic Perturbation Response Prediction Beyond Systematic Variation (Nature Biotechnology 2025) [paper] [中文解读] [code] [ask deepwiki]

• [RegVelo] [Perturbation] RegVelo: Gene-Regulatory-Informed Dynamics of Single Cells (bioRxiv) [paper] [code] [ask deepwiki]

• [IMPA] [Morphology] Predicting cell morphological responses to perturbations using generative modeling (Nature Communications 2025) [paper] [code]

• [PhenoProfiler] [Morphology] PhenoProfiler: Advancing Morphology Representations for Image-based Drug Discovery (Nature Communications 2025) [paper] [code] [webserver] [ask deepwiki]

• [PERISCOPE] [Morphology] A genome-wide atlas of human cell morphology (Nature Methods 2025) [paper] [dataset] [code]

• [Morph Map] [Morphology] Morphological map of under- and overexpression of genes in human cells (Nature Methods 2025) [paper] [dataset] [code]

• [MorphDiff] [Morphology] Prediction of Cellular Morphology Changes under Perturbations with a Transcriptome-Guided Diffusion Model (Nature Communications 2025) [paper] [中文解读] [code] [ask deepwiki]

• [rBio-1] [Agent] rBio1-Training Scientific Reasoning LLMs with Biological World Models as Soft Verifiers (bioRxiv 2025) [paper] [中文解读] [code] [ask deepwiki]

• [TranscriptFormer] [Foundation Model] A Cross-Species Generative Cell Atlas across 1.5 Billion Years of Evolution: The Transcriptformer Single-Cell Model (bioRxiv 2025) [paper] [code] [ask deepwiki]

• [CellAtria] [Agent] An Agentic AI Framework for Ingestion and Standardization of Single-Cell RNA-Seq Data Analysis (bioRxiv 2025) [paper] [中文解读] [code] [ask deepwiki]

• [Scvi-hub] [Tool] Scvi-hub: An Actionable Repository for Model-Driven Single-Cell Analysis (Nature Methods 2025) [paper] [中文解读] [code] [ask deepwiki]

• [GraphVelo] [Spatial] GraphVelo Allows for Accurate Inference of Multimodal Velocities and Molecular Mechanisms for Single Cells (Nature Communications 2025) [paper] [中文解读] [code] [ask deepwiki]

• [Stereo-Cell] [Spatial] Stereo-Cell: Spatial Enhanced-Resolution Single-Cell Sequencing with High-Density DNA Nanoball-Patterned Arrays (Science 2025) [paper] [中文解读] [code] [ask deepwiki]

• [SToFM] [Spatial] SToFM: A Multi-scale Foundation Model for Spatial Transcriptomics (ICML 2025 Poster) [paper] [中文解读] [code] [ask deepwiki]

• [NicheFlow] [Spatial] Modeling Microenvironment Trajectories on Spatial Transcriptomics with NicheFlow (NeurIPS 2025) [paper] [code]

• [scGPT-spatial] [Spatial] scGPT-spatial: Continual Pretraining of Single-Cell Foundation Model for Spatial Transcriptomics (bioRxiv 2025) [paper] [code]

• [SpatialAgent] [Agent] SpatialAgent: An Autonomous AI Agent for Spatial Biology (bioRxiv 2025) [paper] [中文解读] [code] [ask deepwiki]

• [CellFlux] [Morphology] CellFlux: Simulating Cellular Morphology Changes via Flow Matching (ICML 2025 Poster) [paper] [code] [ask deepwiki]

• [CellCLIP] [Morphology] CellCLIP: Learning Perturbation Effects in Cell Painting via Text-Guided Contrastive Learning (NeurIPS 2025) [paper] [code]

• [CELTIC] [Morphology] Cell context-dependent in silico organelle localization in label-free microscopy images (Nature Methods 2025) [paper] [code]

• [MorphoDiff] [Morphology] MorphoDiff: Cellular Morphology Painting with Diffusion Models (ICLR 2025) [paper] [preprint] [code]

• [PRESCRIBE] [Perturbation] PRESCRIBE: Predicting Single-Cell Responses with Bayesian Estimation (NeurIPS 2025 Poster) [paper]

• [GDE] [Related] Generative Distribution Embeddings: Lifting Autoencoders to the Space of Distributions for Multiscale Representation Learning (NeurIPS 2025 Poster) [paper] [preprint]

• [CellPB] [Benchmark] Benchmarking AI Models for in Silico Gene Perturbation of Cells (bioRxiv 2025) [paper] [code] [ask deepwiki]

• [PerturBench] [Benchmark] Benchmarking Machine Learning Models for Cellular Perturbation Analysis (NeurIPS 2025 Datasets and Benchmarks Track) [paper] [code]

• [CellForge] [Agent] CellForge: Agentic Design of Virtual Cell Models (arXiv 2025) [paper] [中文解读] [code] [ask deepwiki]

• [Cradle-VAE] [Perturbation] Cradle-VAE: Enhancing Single-Cell Gene Perturbation Modeling with Counterfactual Reasoning-based Artifact Disentanglement (AAAI 2025) [paper] [code]

• [XTransferCDR] [Perturbation] Learning Cross-Domain Representations for Transferable Drug Perturbations on Single-Cell Transcriptional Responses (AAAI 2025) [paper] [code]

• [Brief Communication] [Benchmark] Deep-Learning-Based Gene Perturbation Effect Prediction Does Not Yet Outperform Simple Linear Baselines (Nature Methods 2025) [paper] [code] [ask deepwiki]

• [Brief Communication] [Benchmark] Limitations of Cell Embedding Metrics Assessed Using Drifting Islands (Nature Biotechnology 2025) [paper] [code] [ask deepwiki]

• [GeneAgent] [Agent] GeneAgent: Self-Verification Language Agent for Gene-Set Analysis Using Domain Databases (Nature Methods 2025) [paper] [code] [ask deepwiki]

• [Theory] [Virtual Cell] Human Interpretable Grammar Encodes Multicellular Systems Biology Models to Democratize Virtual Cell Laboratories (Cell 2025) [paper] [code] [ask deepwiki]

• [GREmLN] [Foundation Model] GREmLN: A Cellular Regulatory Network-Aware Transcriptomics Foundation Model (bioRxiv 2025) [paper] [中文解读] [code] [ask deepwiki]

• [CellVoyager] [Agent] CellVoyager: AI CompBio Agent Generates New Insights by Autonomously Analyzing Biological Data (bioRxiv 2025) [paper] [中文解读] [code] [ask deepwiki]

• [CausCell] [Perturbation] Causal Disentanglement for Single-Cell Representations and Controllable Counterfactual Generation (Nature Communications 2025) [paper] [中文解读] [code] [ask deepwiki]

• [CLIP^n] [Morphology] Transitive Prediction of Small-Molecule Function through Alignment of High-Content Screening Resources (Nature Biotechnology 2025) [paper] [code] [ask deepwiki]

• [DrugPT] [Perturbation] DrugPT: A Flexible Framework for Integrating Gene and Chemical Representations in Perturbation Modeling (bioRxiv 2025) [paper]

• [OmniPert] [Perturbation] OmniPert: A Deep Learning Foundation Model for Predicting Responses to Genetic and Chemical Perturbations in Single Cancer Cells (bioRxiv 2025) [paper]

• [UNAGI] [Perturbation] A Deep Generative Model for Deciphering Cellular Dynamics and in Silico Drug Discovery in Complex Diseases (Nature Biomedical Engineering 2025) [paper] [code] [ask deepwiki]

• [OmiCLIP] [Spatial] A Visual-Omics Foundation Model to Bridge Histopathology with Spatial Transcriptomics (Nature Methods 2025) [paper] [code] [ask deepwiki]

• [Biomni] [Agent] Biomni: A General-Purpose Biomedical AI Agent (bioRxiv 2025) [paper] [code] [ask deepwiki]

• [OCTO-vc] [Virtual Cell] OCTO-vc: Virtual Cells in Real Tissue (© by Noetik 2025) [technical report] [online demonstration]

• [STATE] [Perturbation] Predicting Cellular Responses to Perturbation across Diverse Contexts with STATE (bioRxiv 2025) [paper] [code] [ask deepwiki]

• [UniPert-G2CP] [Perturbation] Genetic-To-Chemical Perturbation Transfer Learning through Unified Multimodal Molecular Representations (bioRxiv 2025) [paper] [code] [ask deepwiki]

• [UniCure] [Perturbation] Unicure: A Foundation Model for Predicting Personalized Cancer Therapy Response (bioRxiv 2025) [paper] [code] [ask deepwiki]

• [Cell-GraphCompass] [Foundation Model] Cell-GraphCompass: Modeling Single Cells with Graph Structure Foundation Model (National Science Review 2025) [paper] [code] [ask deepwiki]

• [scPRINT] [Foundation Model] scPRINT: Pre-training on 50 Million Cells Allows Robust Gene Network Predictions (Nature Communications 2025) [paper] [code] [ask deepwiki]

• [CellFM] [Foundation Model] CellFM: A Large-Scale Foundation Model Pre-trained on Transcriptomics of 100 Million Human Cells (Nature Communications 2025) [paper] [中文解读] [code] [ask deepwiki]

• [C2S-Scale] [Foundation Model] C2S-Scale: Scaling Large Language Models for Next-Generation Single-Cell Analysis (bioRxiv 2025) [paper] [中文解读] [code] [ask deepwiki]

• [scNET] [Foundation Model] scNET: Learning Context-Specific Gene and Cell Embeddings by Integrating Single-Cell Gene Expression Data with Protein-Protein Interactions (Nature Methods 2025) [paper] [code] [ask deepwiki]

• [ProCyon] [Foundation Model] ProCyon: A multimodal foundation model for protein phenotypes (bioRxiv) [paper] [project] [code]

• [SubCell] [Foundation Model] SubCell: Proteome-aware vision foundation models for microscopy capture single-cell biology (bioRxiv 2025) [paper] [code] [ask deepwiki]

• [Token-Mol 1.0] [Related] Token-Mol 1.0: Tokenized Drug Design with Large Language Models (Nature Communications 2025) [paper] [code] [ask deepwiki]

• [Comment] [Virtual Cell] Virtual Cells for Predictive Immunotherapy (Nature Biotechnology Comment 2025) [paper]

• [Recursion] [Virtual Cell] Virtual Cells: Predict, Explain, Discover (arXiv 2025) [paper]

2024

• [Zero-Shot Perturbation] [Perturbation] Efficient Fine-Tuning of Single-Cell Foundation Models Enables Zero-Shot Molecular Perturbation Prediction (arXiv 2024) [paper]

• [Cell Maps] [Virtual Cell] Multimodal cell maps as a foundation for structural and functional genomics (Nature 2025) [paper] [project]

• [CellFlow] [Morphology] CellFlow Enables Generative Single-Cell Phenotype Modeling with Flow Matching (bioRxiv 2025) [paper] [code] [ask deepwiki]

• [Cell Shapes] [Morphology] Cell shapes decode molecular phenotypes in image-based spatial proteomics (bioRxiv 2025) [paper]

• [Prophet] [Perturbation] Scalable and Universal Prediction of Cellular Phenotypes (bioRxiv 2025) [paper] [code] [ask deepwiki]

• [Morphology] Evaluating Feature Extraction in Ovarian Cancer Cell Line Co-Cultures Using Deep Neural Networks (Communications Biology 2025) [paper]

• [ProteinTalks] [Foundation Model] A Perturbation Proteomics-Based Foundation Model for Virtual Cell Construction (bioRxiv 2025) [paper] [中文解读] [code] [ask deepwiki]

• [Virtual Cell] Grow AI Virtual Cells: Three Data Pillars and Closed-Loop Learning (Cell Research 2025) [paper] [中文解读]

• [Virtual Cell] Build the Virtual Cell with Artificial Intelligence: A Perspective for Cancer Research (Military Medical Research 2025) [paper]

• [PS] [Perturbation] Decoding Heterogeneous Single-Cell Perturbation Responses (Nature Cell Biology 2025) [paper] [code] [ask deepwiki]

• [Mixscale] [Perturbation] Systematic Reconstruction of Molecular Pathway Signatures Using Scalable Single-Cell Perturbation Screens (Nature Cell Biology 2025) [paper] [code] [ask deepwiki]

• [scDrugMap] [Benchmark] scDrugMap: benchmarking large foundation models for drug response prediction (Nature Communications 2025) [paper]

• [VCC Commentary] [Benchmark] Virtual Cell Challenge: Toward a Turing Test for the Virtual Cell (Cell Commentary 2025) [paper] [homepage] [beginner’s guidance]

• [CZI Evaluation] [Benchmark] Benchmarking and Evaluation of AI Models in Biology: Outcomes and Recommendations from the CZI Virtual Cells Workshop (arXiv 2025) [paper] [中文解读]

• [Virtual Organs] [Benchmark] From Virtual Cell Challenge to Virtual Organs: Navigating the Deep Waters of Medical AI Models (iCell 2025) [paper]

• [GET] [Foundation Model] A Foundation Model of Transcription across Human Cell Types (Nature 2025) [paper] [code] [ask deepwiki]

2024

• [TranSiGen] [Perturbation] Deep Representation Learning of Chemical-Induced Transcriptional Profile for Phenotype-Based Drug Discovery (Nature Communications 2024) [paper] [code] [ask deepwiki]

• [PRnet] [Perturbation] Predicting transcriptional responses to novel chemical perturbations using deep generative model for drug discovery (Nature Communications 2024) [paper]

• [GenePT] [Foundation Model] Simple and Effective Embedding Model for Single-Cell Biology Built from ChatGPT (Nature Biomedical Engineering 2024) [paper] [code] [ask deepwiki]

• [SCimilarity] [Foundation Model] A Cell Atlas Foundation Model for Scalable Search of Similar Human Cells (Nature 2024) [paper] [code] [ask deepwiki]

• [scLong] [Foundation Model] scLong: A Billion-Parameter Foundation Model for Capturing Long-Range Gene Context in Single-Cell Transcriptomics (bioRxiv 2024) [paper] [code] [ask deepwiki]

• [scFoundation] [Foundation Model] Large-Scale Foundation Model on Single-Cell Transcriptomics (Nature Methods 2024) [paper] [code] [ask deepwiki]

• [scGPT] [Foundation Model] scGPT: Toward Building a Foundation Model for Single-Cell Multi-Omics Using Generative AI (Nature Methods 2024) [paper] [code] [ask deepwiki]

• [TamGen] [Related] TamGen: Drug Design with Target-Aware Molecule Generation through a Chemical Language Model (Nature Communications 2024) [paper] [code] [ask deepwiki]

• [GeneCompass] [Foundation Model] GeneCompass: Deciphering Universal Gene Regulatory Mechanisms with a Knowledge-Informed Cross-Species Foundation Model (Cell Research 2024) [paper] [code] [ask deepwiki]

• [scTab] [Foundation Model] scTab: Scaling Cross-Tissue Single-Cell Annotation Models (Nature Communications 2024) [paper] [code] [ask deepwiki]

• [SATURN] [Foundation Model] Toward Universal Cell Embeddings: Integrating Single-Cell RNA-Seq Datasets across Species with SATURN (Nature Methods 2024) [paper] [code] [ask deepwiki]

• [UCE] [Foundation Model] Universal Cell Embeddings: A Foundation Model for Cell Biology (bioRxiv 2024) [paper] [code] [ask deepwiki]

• [Cell2Sentence] [Foundation Model] Cell2Sentence: Teaching Large Language Models the Language of Biology (ICML 2024 Poster) [paper] [code] [ask deepwiki]

• [LangCell] [Foundation Model] LangCell: Language-Cell Pre-training for Cell Identity Understanding (ICML 2024 Poster) [paper] [code] [ask deepwiki]

• [CellPLM] [Foundation Model] CellPLM: Pre-training of Cell Language Model beyond Single Cells (ICLR 2024 Poster) [paper] [code] [ask deepwiki]

• [Stanford PhD Thesis] [Virtual Cell] Engineering Cells Using Artificial Intelligence (© by Yusuf Roohani 2024) [paper] [GitHub Homepage] [Arc profile]

Datasets

Perturbation and Cell-State Atlases

• [Arc Virtual Cell Atlas] Large-scale perturbation atlas and codebase from Arc Institute [resource] [repo]

• [Tahoe-100M] Tahoe-100M: A Giga-Scale Single-Cell Perturbation Atlas for Context-Dependent Gene Function and Cellular Modeling (bioRxiv 2025) [paper] [code]

• [X-Atlas/Orion] Genome-Wide Perturb-Seq Datasets via a Scalable Fix-Cryopreserve Platform for Training Dose-Dependent Biological Foundation Models (bioRxiv 2025) [paper] [dataset]

• [scPerturb] scPerturb: Harmonized Single-Cell Perturbation Data (Nature Methods 2024) [paper] [dataset] [code]

• [CIGS] High-Throughput Profiling of Chemical-Induced Gene Expression across 93,644 Perturbations (Nature Methods 2025) [paper] [dataset] [code]

• [L1000/CMap] A Next Generation Connectivity Map: L1000 Platform and the First 1,000,000 Profiles (Cell 2017) [paper] [dataset]

• [Sci-Plex] Massively Multiplex Chemical Transcriptomics at Single-Cell Resolution (Science 2019) [paper] [dataset] [code]

• [Perturb-seq datasets] Genome-scale and combinatorial Perturb-seq datasets from Replogle, Norman, Dixit, Adamson, and related screens [overview]

• [Virtual Cell Challenge] Community perturbation-prediction challenge and hidden evaluation resources [homepage]

Single-Cell Reference Atlases

• [scBaseCount] scBaseCount: An AI Agent-Curated, Uniformly Processed, and Continually Expanding Single Cell Data Repository (bioRxiv 2025) [paper] [code-scRecounter] [code-SRAgent]

• [CZ CELLxGENE Discover] A single-cell data platform for scalable exploration, analysis, and modeling of aggregated data (NAR 2025) [paper] [dataset]

• [Human Cell Atlas] International atlas of human cells and tissues [portal] [paper]

• [Tabula Sapiens] A multiple-organ single-cell transcriptomic atlas of humans (Science 2022) [paper] [dataset]

• [Human BioMolecular Atlas Program] HuBMAP healthy human tissue atlas and common coordinate framework [portal] [paper]

• [GTEx] Genotype-Tissue Expression project for human tissue expression baselines [portal] [overview]

Multimodal, Morphology, and Imaging

• [scGeneScope] scGeneScope: A Treatment-Matched Single Cell Imaging and Transcriptomics Dataset and Benchmark for Treatment Response Modeling (NeurIPS 2025 Datasets and Benchmarks Track) [paper] [dataset]

• [CPJUMP1] Three million images and morphological profiles of cells treated with matched chemical and genetic perturbations (Nature Methods 2024) [paper] [dataset] [code]

• [Cell Painting Gallery] Public high-content cell painting datasets from Broad and partners [dataset] [overview]

• [RxRx] Recursion high-content cellular imaging datasets for perturbation and batch-correction research [datasets]

• [CM4AI] Cell Maps for Artificial Intelligence: AI-Ready Maps of Human Cell Architecture from Disease-Relevant Cell Lines (bioRxiv 2024) [paper] [dataset]

• [STAMP] Single-cell transcriptomics analysis and multimodal profiling through imaging (Cell 2025) [paper]

Spatial and Tissue Context

• [HEST-1k] HEST-1k: A Dataset for Spatial Transcriptomics and Histology Image Analysis (NeurIPS 2024) [paper] [code]

• [Spatial Perturb-seq] Spatial perturb-seq data for functional genomics within intact tissue architecture (Nature Communications 2026) [paper] [code]

• [Perturb-FISH] CRISPR screening with imaging-based spatial transcriptomics (Cell 2025) [paper]

• [10x Genomics spatial datasets] Visium, Xenium, and related public spatial transcriptomics example datasets [datasets]

• [Vizgen MERFISH datasets] Public MERFISH example datasets for spatial transcriptomics [datasets]

Protein, Organelle, and Molecular Priors

• [Human Protein Atlas] Subcellular and tissue protein expression atlases [resource] [subcellular] [paper]

• [OpenCell] Endogenous protein tagging, localization, and interaction data for human cellular organization (Science 2022) [paper] [dataset]

• [ProtiCelli] Proteome-wide image generation resources for human cell protein localization (bioRxiv 2026) [paper] [code]

• [SubCell] Proteome-aware microscopy foundation model resources based on HPA images (bioRxiv 2025) [paper] [code]

• [STRING] Protein functional association and interaction networks [resource] [paper]

• [OmniPath] Signaling, ligand-receptor, and causal network priors for multi-omics analysis [resource] [paper]

Chemical, Drug, and Target Resources

• [Drug Repurposing Hub] Curated compound library with targets, mechanisms, and clinical annotations (Nature Medicine 2017) [paper] [resource]

• [DepMap/CCLE/PRISM] Cancer dependency, molecular profile, and drug-response resources for cell-line perturbation modeling [resource] [paper]

• [ChEMBL] Drug-like molecules, bioactivities, targets, and assays [resource] [paper]

• [BindingDB] Protein-small molecule binding affinity knowledgebase [resource] [paper]

• [PubChem] Compound identifiers, structures, assays, and bioactivity records [resource] [paper]

Reports and Blogs

• [Symposium] AI Proteomics and Virtual Cell (© by Westlake University 2025) [media] [中文解读]

• [Report] Projections at the Frontier: Snapshot 2025 (© by Decoding Bio’s Team 2025) [slide] [中文解读]

• [Post] Chan Zuckerberg Initiative’s rBio Uses Virtual Cells to Train AI, Bypassing Lab Work (© by Michael Nuñez 2025) [blog]

• [Blog] AI’s Next Frontier: Modeling Life Itself (© by Chan Zuckerberg Initiative 2025) [blog] [中文解读]

• [Blog] The State of Research on Virtual Cell Modeling (© by Will Connell 2025) [blog]

• [Blog] What Are Virtual Cells? Learning “Universal Representations” of Life’s Fundamental Unit (© by Elliot Hershberg 2025) [blog]

• [中文 Blog] 什么是虚拟细胞：AI 生物学的“登月时刻”和“苦涩教训” (© by 范阳 2025) [blog]

• [Introduction] Virtual Cells (© by Udara Jay 2025) [blog]

Videos

• [Arc Institute] Predicting Cellular Responses to Perturbation across Diverse Contexts with STATE [YouTube]

• [Valence Labs] Virtual Cells: Predict, Explain, D

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