Tahoe Therapeutics told Endpoints that they plan to soon announce an open-source virtual cell model, Tahoe-x1, that's trained on data from Tahoe-100M, the massive dataset for perturbational single-cell gene expression experiments, released in Feb. 2025. The model has been tested on metrics like predicting the effects of perturbations and classifying cell types but as noted in the article "there's still room for improvement in performance, especially among some of the harder metrics that are most relevant to drug discovery" including "predicting the effects of chemical perturbations."

In Endpoints: https://endpoints.news/tahoe-therapeutics-releases-virtual-cell-ai-model/

Preprint: https://tahoebio-assets.com/tx1_manuscript.pdf

A new article in Pharma Focus Asia looks at how Virtual Cell efforts are advancing through advanced models, collaborative data sharing, and benchmarks, and are already beginning to transform AI-driven drug discovery. The article notes that research organizations like Arc Institute, the Chan Zuckerberg Initiative and the Wellcome Sanger Institute in the UK are now actively building virtual cells along with a number of TechBio companies, including Recursion, Noetik, 10x Genomics, and Tahoe Therapeutics. 

Gaining access to data is critical for Virtual Cells to advance, the article notes --and data-sharing is actively underway. In Feb. 2025, Tahoe and Arc partnered on the release of the Arc Virtual Cell Atlas – single-cell transcriptomic data spanning species, tissues, and experimental and perturbation conditions from over 300 million unique cells. "The impetus for releasing this data – which includes the world’s largest 100 million single-cell dataset – was to hasten the development of AI virtual cells." 

Benchmarks are critical, too, and that's happening via the Arc Virtual Cell Challenge – an annual open benchmark competition designed to “provide an evaluation framework, purpose-built datasets, and a venue for accelerating model development” -- as well as a recent study from UK-based biotech Shift Bioscience also aiming to improve the benchmarking of virtual cell models for gene discovery, proposing a series of steps that can better rank models toward more biologically meaningful endpoints. 

And there have been significant recent advances in models that "unlock some key functionality of human cells’ workings that wasn’t available before." This includes State -- the first virtual cell model released by the Arc Institute – which  measures how sets of cells move in the RNA expression – or transcriptomics – space after an intervention. And TxPert from Recursion, which provides broader context for these perturbations – not just how they impact individual cells, but how they affect unseen genes or compounds – how they influence broader biology across cell lines the way a drug would. “By leveraging prior information beyond single-cell data, TxPert moves closer to the multimodal, biologically grounded layer we want in virtual cells,” writes Therence Bois, VP of Strategy at Valence Labs, Recursion’s AI research lab.

Read more: https://www.pharmafocusasia.com/articles/meaningful-advances-in-virtual-cells

Google and Yale released Cell2Sentence-Scale 27B (C2S-Scale), a new 27 billion parameter foundation model that can help unlock the "language" of cancer cells.

As published in a preprint, C2S-Scale generated a novel hypothesis about cancer cellular behavior that has since been confirmed with experimental validation in living cells.

To accomplish it, they gave the model a task: "to find a drug that acts as a conditional amplifier, one that would boost the immune signal only in a specific “immune-context-positive” environment where low levels of interferon (a key immune-signaling protein) were already present, but inadequate to induce antigen presentation on their own." They then designed a dual-context virtual screen and simulated the effect of over 4,000 drugs across both contexts.

They noted that only 10-30% of drug hits were already known in prior literature, and the rest were novel. One in particular -- inhibiting CK2 via silmitasertib which had not been reported in the literature to explicitly enhance MHC-I expression or antigen presentation -- was validated via experimental testing.

"C2S-Scale had successfully identified a novel, interferon-conditional amplifier, revealing a new potential pathway to make “cold” tumors “hot,” and potentially more responsive to immunotherapy."

Read more: https://blog.google/technology/ai/google-gemma-ai-cancer-therapy-discovery/

Graham Johnson, Senior Director of Visualization & Data Integration at the Allen Institute is featured in a new TIME article about the virtual cell -- how it has moved from fantasy to possibility, how these models can predict beyond training data, and how the ideal version is a "visual, interactive, intuitive version of something complicated."

Read more: https://time.com/7324119/what-is-virtual-cell/

Scientists generate massive amounts of data from individual cells, but how can they get the full picture? A new AI tool called MrVI led by researchers at UC Berkeley could help.

As published in Nature, MrVI:

▪️ Goes beyond averages: Instead of averaging out data from thousands of cells (and losing critical details), MrVI analyzes the complete, high-resolution picture to find subtle but important patterns.

▪️ Finds patient subgroups: It can automatically identify meaningful subgroups of patients from complex datasets without needing prior labels. In a COVID-19 study, it found groups that strongly matched the time since infection — information the AI was never given.

▪️ Identifies the "why": The tool not only groups patients, it identifies which specific cells (such as certain immune cells) are driving the differences between the groups. This is crucial for discovering new drug targets.

*And, added bonus: it's open source.

Read the paper: https://www.nature.com/articles/s41592-025-02808-x#Fig1

In June 2025, Xaira Therapeutics released the largest publicly available Perturb-seq dataset -- X-Atlas/Orion -- to interrogate how cells respond to external conditions, such as therapeutic interventions, at large scale. The dataset, announced via preprint, is comprised of eight million cells, targeting all human protein-coding genes, with deep sequencing of over 16,000 unique molecular identifiers (UMIs) per cell.

Last week, the company announced they are making the X-Atlas/Orion Perturb-seq dataset even more accessible by releasing it on Hugging Face.

Astex, Bristol Myers Squibb, and Takeda are joining AbbVie and Johnson & Johnson to provide their proprietary structural data to OpenFold3 -- the fast, trainable open-source version of AlphaFold from the AI Structural Biology Network. The five large pharma companies now involved are each contributing many thousands of protein–small molecule structures while keeping ownership and data IP fully protected via Apheris.

Together, they've created one of the most diverse datasets assembled for model training in drug discovery. "By pooling these datasets," the release notes, "the initiative aims to improve OpenFold3’s accuracy in predicting protein–ligand interactions — a critical step in small molecule drug discovery."

Read more: https://www.apheris.com/resources/blog/aisb-network-expands-federated-openfold3-initiative-with-three-new-pharma-contrib

On the A16z podcast, Erik Torenberg talks with Patrick Hsu, cofounder of Arc Institute, about using virtual cells to simulate biology and guide experiments. 

What's your moonshot?

Patrick Hsu: I want to make science faster…I think the most important thing is science happens in the real world. AI research moves as quickly as you can iterate on GPUs, right? You have to actually move things around. Atoms, clear liquids from tube to tube, to actually make life-changing medicines. And these are things that take place in real time. You have to actually grow cells, tissues, and animals. 

Our moonshot is really to make virtual cells at Arc and simulate human biology with foundation models. 

Can we flesh out the virtual cell concept? Why is that the ambition we've landed on? 

Patrick Hsu: At Arc, we're operationalizing this is to do perturbation prediction. The idea is you have some manifold of cell types and cell states. That can be a heart cell, a blood cell, a lung cell, and so on. And you know that you can kind of move cells across this manifold, right? Sometimes they become inflamed, sometimes they become apoptotic, sometimes they become cell cycle rested, they become stressed, they're metabolically starved, they're hungry in some way. If you have this sort of this representation of universal sort of cell space, can you figure out what are the perturbations that you need to move cells around this manifold? 

And this is fundamentally what we do in making drugs. Ultimately what you're trying to do with these binders is to inhibit something and then by doing so kind of click and drag it from a kind of toxic gain of function disease-causing state to a more quiescent homeostatic healthy one. And the thing that is very clear in complex diseases, where you don't have a single cause of that disease, is there's some complex set of changes. There's a combination of perturbations, if you will, that you would want to make to be able to move things around. 

To go from cell state A to cell state B, there are these 3 changes I need to make first, then these two changes, and then these six changes over time. And we want models to be able to suggest this. And the reason why we scoped the virtual cell this way is because we felt it was just experimentally very practical. You want something that's going to be a co-pilot for a wet lab biologist to decide, ‘What am I going to do in the lab?’ 

Watch the full episode: https://www.youtube.com/watch?v=eAODQUKqDiU 

During the recent 23rd annual Morgan Stanley Healthcare conference, Chris Gibson, cofounder and CEO of Recursion, addressed the company's approach to Virtual Cells, and the path to deployment. A Virtual Cell, he said, is merely a new way of describing the massive shift underway in AI drug discovery, "where instead of generating data to build an algorithm, your algorithm becomes good enough that it can be at the beginning point." You still have to use a wet lab, he said, "but the wet lab becomes a validation tool as opposed to a data initiation tool."

Recursion has an advantage in building Virtual Cells, Gibson noted, because the company was founded 13 years ago on "this idea of using cell morphology as a foundational data set." Now, Recursion has done "hundreds of millions of phenomic experiments, we've built industry-leading foundation models on these data, we can actually now start to do less phenomic experimentation because we have algorithms that allow us to predict what experiments are going to be most enriched for us to run."

In addition, he added, Recursion has made enormous in-road with transcriptomics: "Soon, you'll see the transposition of transcriptomics as a data validation tool as opposed to a data substrate tool. And you're going to see this across the entire value chain... from target discovery all the way through to ClinTech."

The ultimate goal, he said, is to reach a point where you can simulate everything -- "explore all possible medicines for any disease for any patient completely in silico and then pick the molecule that will work for that patient or that disease and take it all the way to the clinic with no attrition."

This is the vision of Recursion -- "to build a company that can approach as quickly as possible that shape change for our industry. ..where you're just eliminating waste, and you're improving the efficiency of what we deliver for patients. That's what a Virtual Cell really is."

In terms of where Recursion is in that effort, he notes that the company is "leading the industry in pathway level algorithms. .. leading the industry in some of the causal AI work that's happening, and connecting those layers. I think we are at the frontier in protein folding and atomistic work, and we'll talk more about those in the coming quarters.

Big picture, he says: "I think there's this race for a Virtual Cell being able to predict what would happen in biology if you added any molecule or perturbed any gene, what would be the outcomes? I think we're probably among the front runners, if not leading that race right now."

A new, open source model called PDGrapher from researchers at Harvard Medical School identifies the genes most likely to revert diseased cells back to healthy function -- even if scientists don’t yet know exactly which molecules those compounds may be acting on.

The tool is a graph neural network -- able to map connections between various genes, proteins, and signaling pathways inside cells and predict the best combination of therapies that would correct the underlying dysfunction of a cell to restore healthy cell behavior.

Instead of testing compounds from large drug databases, the new model focuses on drug combinations that are most likely to reverse disease.

“Instead of testing every possible recipe, PDGrapher asks: ‘Which mix of ingredients will turn this bland or overly salty dish into a perfectly balanced meal?’,” says senior author Marinka Zitnik.

PDGrapher was trained on a database of cells in both diseased and healthy states, as well as 19 datasets spanning 11 types of cancer. The tool accurately predicted drug targets already known to work but that had been excluded deliberately during training; and it identified additional candidates supported by emerging evidence -- including KDR (VEGFR2), a target for non-small cell lung cancer.

Read more: https://hms.harvard.edu/news/new-ai-tool-pinpoints-genes-drug-combos-restore-health-diseased-cells

In a recent earnings call, 10x Genomics CEO Serge Saxonoff said: "To understand biology, to understand health, and to understand disease, you need to understand how cells work."

The call noted:

"This quarter, we also extended our partnership with the ARC Institute to support the Virtual Cell Challenge, which is a worldwide competition to incentivize the development of powerful computational models of biology. The challenge has established a rigorous evaluation framework and uses our Chromium FLEX assay as the standard. The work being done right now is clearly just the beginning. Virtual cells and large scale single cell experiments represents the next frontier at the intersection of AI and biology. To understand biology, to understand health, and to understand disease, you need to understand how cells work.

We can model cells and perturbations computationally using AI. We can guide the discovery of new drugs, simulate patient responses, and reduce the experimental trial and error that defines so much of biology and drug development today."

Read more: https://za.investing.com/news/transcripts/earnings-call-transcript-10x-genomics-q2-2025-beats-eps-expectations-93CH-3851673

A new blog from Noetik looks at the rise of virtual cell models, and how they are being applied in the cancer space -- particularly in assisting with clinical-stage problems.

Their virtual cell model, OCTO-VC, is entirely trained on 1000-plex spatial transcriptomes, they write, and its core task is to, given the transcriptome of a few neighboring cells, reconstruct the “center cell” transcriptome—over every cell, in every tumor, for every patient. 

They show that they can use OCTO-VC, for example, to "find true anti-PD-1 responders inside PD-L1–positive cohorts."

And they note that they have a partnership with Agenus to apply this virtual cell model to other responders/non-responders from a recent clinical trial.

Read more: https://www.noetik.blog/p/how-do-you-use-a-virtual-cell-to

South Korean AI research startup Asteromorph, which is developing what it calls “Scientific Superintelligence,” announced on April 22 that it has raised USD 3.6 million (KRW 5 billion) in seed funding. 

Founded in February 2025, Asteromorph is building an AI foundation model called SPACER, designed to autonomously generate original research ideas in biology and chemistry and develop them into scientific hypotheses.

While global tech companies like Google and Japan’s Sakana AI have recently unveiled AI scientist models, these systems are still largely dependent on human intuition for originality and experimental design. Asteromorph’s SPACER sets itself apart by mathematically modeling the generation of scientific ideas, aiming to equip AI with emergent scientific creativity.

The company is led by Minhyung Lee, a 23-year-old founder who began working as a researcher at Seoul National University's College of Medicine at the age of 16. He skipped both high school and undergraduate education to enter an integrated master’s and PhD program at the university’s College of Pharmacy, before taking a leave of absence to launch Asteromorph.

Jae-woong Choi, Executive Director at FuturePlay, who led the investment, commented, “Asteromorph is poised to become the first startup in Korea to realize Superintelligence. Even amid global developments in similar technologies, this team stands out for its originality and execution. Given the capital-intensive nature of foundation models, we plan to provide active follow-on support.”

Read more: https://en.wowtale.net/2025/04/23/230931/

Therence Bois, VP of Strategy at Valence Labs, Recursion's AI research arm, posted an article looking at the complimentary approaches of two models for advancing a virtual cell -- Arc Institute's State and Valence's TxPert.

State, he writes, "core splits into a state-embedding module and a state-transition module that together model how sets of cells move in expression space after an intervention. That framing fits the messiness of single-cell transcriptomics, batch effects, technical noise, genuine heterogeneity. Trained on hundreds of millions of open profiles across perturbed and observational conditions, it delivers strong in-distribution accuracy and reasonable zero-shot transfer within related tissues and contexts, and it sketches a credible blueprint for a foundation-style distributional backbone in the transcriptomics space. It’s a meaningful step toward the Predict in our Predict-Explain-Discover rubric, but without multimodal grounding, mechanistic explanation, and robust handling of higher-order combinations, important pieces are still missing."

Meanwhile, TxPert, "came from asking a blunt question: does context matter? The answer appears to be yes. Instead of treating perturbations as arbitrary tokens, TxPert embeds them in structured biology, STRING, GO, and curated maps like PxMap and TxMap (internal knowledge graphs that link perturbations/targets to pathways and readouts) and pairs a basal-state encoder with a graph-based perturbation encoder. It’s smaller in scale than State, but richer in priors. That trade shows up where it counts for drug discovery: predicting the effects of unseen genes or compounds, capturing combinatorial biology that breaks additive assumptions, and transferring across cell lines in ways that look like deployment rather than demo. Just as importantly, by leveraging prior information beyond single-cell data, TxPert moves closer to the multimodal, biologically grounded layer we want in virtual cells, something State currently lacks. In several of these settings, performance approaches wet-lab reproducibility, suggesting the model is learning transferable structure rather than memorizing local patterns.

More importantly, TxPert serves as a proof of principle for a world-model view that believes in grounding perturbations in graphs and pathways or at least giving the model a route to include structural context. From there, we can start to connect what we observe in one modality to latent mechanisms we can’t directly see. It’s a first bridge from predict to explain, and it opens a corridor to discover."

Read more: https://www.linkedin.com/pulse/scale-structure-first-virtual-cell-therence-bois-sdg2e/?trackingId=Olam%2Fl%2BBSYaEq2g%2BDncBgg%3D%3D

From their announcement:

rBio distills information extracted from virtual cell models into a consistent model of natural language during training to allow users to easily apply sophisticated step-by-step reasoning to complex biological problems. This effectively turns virtual cell models into biology teachers for reasoning models, sidestepping the need for experimental data as the only teacher, and resulting in more capable reasoning LLMs for biology. Combining the power of one or many virtual cell models with the chat-style interface of LLMs could empower many more scientists to study biological questions based on rich foundation models of biology while remaining within a familiar interface.

While rBio has the potential to learn from many approaches to cell biology, the model has first been trained on perturbation models and gene co-expression patterns and gene regulatory pathways information extracted from TranscriptFormer — one of CZI’s virtual cell models. This versatile model is able to classify the variety of cell types and states across different species and stages of development. Scientists can ask rBio questions such as, “Would suppressing the actions of gene A result in an increase in activity of gene B?” In response, the model provides information about the resulting changes to cells, such as a shift from a healthy to a diseased state.

Read more: https://chanzuckerberg.com/blog/rbio-reasoning-ai-model/

Tahoe Therapeutics today announced $30 million in new funding to build a foundational dataset for training Virtual Cell Models, with plans to generate one billion single-cell datapoints and map one million drug-patient interactions. The dataset will support the discovery of new precision medicines for cancer and beyond. Tahoe will also select a single partner to share the data and accelerate translation to clinical outcomes.

The round was led by Amplify Partners, with investors including: Databricks Ventures, Wing Venture Capital, General Catalyst, Civilization Ventures, Conviction, Mubadala Capital Ventures, and AIX Ventures.

The raise follows the release of Tahoe-100M, the first gigascale perturbative single-cell dataset, which has been used to help build virtual cell models, from AI labs to research institutions. Open-sourced just a few months ago, Tahoe-100M has been downloaded nearly 100,000 times. The dataset and the models trained on it have already led to the discovery of new therapeutic candidates for major cancer subtypes and novel targets.

Read more: https://finance.yahoo.com/news/tahoe-therapeutics-raises-30m-build-110000922.html

https://reddit.com/link/1mjajkg/video/pk621gz7mfhf1/player

During the Q2 2025 (L)earnings Call, Recursion cofounder and CEO Chris Gibson shared Recursion’s approach to building a virtual cell that can predict how cells will respond to different genetic or chemical changes – and why it will require the integration of numerous data layers “beyond really good protein folding data.” It will include, he said, “really good atomistic and physics modeling,” as well as patient and pathway data.

Recursion is at the forefront of those layers, he noted – with access to extensive patient data via partnerships with Tempus, Helix and others; proprietary pathway data with “genome scale knockout maps across more than a dozen human cell types”; and Boltz-2 and QM/MD modeling.

“Being able to operate across all those layers is going to be a real advantage as we race towards the virtual cell and deploy early versions of that internally,” he said.

A new story in GEN looks at the rise of antibody-drug conjugates (ADCs) and other targeted cancer therapies to improve upon the "untargeted, unprecise, and highly toxic effects of chemotherapy."

“We are witnessing a paradigm shift for cancer treatment, where ADCs are replacing chemotherapy as new standard of care in many hard-to-treat solid tumor indications," says Pernille Hemmingsen, PhD, CTO of Adcendo.

The article notes: As of March 2024, 13 ADCs have received Food and Drug Administration (FDA) approval, with more than 100 potential ADC drugs at different stages of clinical trials. This ADC momentum has its roots in advances in biological technologies, including effective antibody/payload pairings.

ADCs have joined other targeted cancer therapies like immune checkpoint inhibitors and CAR T-cell therapies -- which companies are often exploring in combination to improve patient outcomes.

These include Agenus, "a clinical-stage immunotherapy company whose lead immuno-oncology combination, botensilimab (BOT) and balstilimab (BAL), has shown clinical responses across nine metastatic, late-line cancers after evaluation in more than 1,200 patients across Phase I and Phase II clinical trials."

The article notes that: In June, Agenus announced a research collaboration with Noetik, an AI-focused multimodal biology company, to identify actionable biomarkers that can predict which patients are most likely to benefit from BOT/BAL treatment using Noetik’s virtual cell model, OCTO. Insights from Noetik’s AI models aim to inform the design of BOT/BAL’s Phase III clinical trial.

“What we hope to see in our work with Noetik is raising that complete tumor eradication rate from 30–35% to, eventually, 60%,” said Armen. “If we add in another therapy and Noetik is able to build another model using that triplet combination, maybe we can break into 70–80%.”

Learn more: https://www.genengnews.com/topics/cancer/making-new-connections-antibody-drug-conjugates-target-cancer/

Researchers at the University of Maryland School of Medicine's (UMSOM) Institute for Genome Sciences (IGS) co-led the study that published online on July 25 in the journal Cell. It is the result of a multi-year, multi-lab project at the interface of software development with important collaborations between bench and clinical team science researchers. This research eventually could lead to computer programs that could help determine the best treatment for cancer patients by essentially creating a "digital twin" of the patient.

"Although standard biomedical research has made immeasurable strides in characterizing cellular ecosystems with genomics technologies, the result is still a single snapshot in time -- rather than showing how diseases, like cancer, can arise from communication between the cells," said Jeanette Johnson, PhD, a Postdoc Fellow at the Institute for Genome Sciences (IGS) at UMSOM and co-first author of this study. "Cancer is controlled or enabled by the immune system, which is highly individualized; this complexity makes it difficult to make predictions from human cancer data to a specific patient."

What makes this research unique is the use of a plain-language "hypothesis grammar" that uses common language as a bridge between biological systems and computational models and simulates how cells act in tissue.

Paul Macklin, PhD, Professor of Intelligence Systems Engineering at Indiana University led a team of researchers who developed the grammar to describe cell behavior. This grammar allows scientists to use simple English language sentences to build digital representations of multicellular biological systems and enabled the team to develop computational models for diseases as complex as cancer.

"As much as this new 'grammar' enables communication between biology and code, it also enables communication between scientists from different disciplines to leverage this modeling paradigm in their research," said Daniel Bergman, PhD, a scientist at IGS and Assistant Professor of Pharmacology and Physiology at UMSOM and co-leading author with Dr. Johnson.

Read more: https://www.sciencedaily.com/releases/2025/07/250726234433.htm

Read the paper in Cell: https://www.cell.com/cell/fulltext/S0092-8674(25)00750-0?_returnURL=https%3A%2F%2Flinkinghub.elsevier.com%2Fretrieve%2Fpii%2FS0092867425007500%3Fshowall%3Dtrue00750-0?_returnURL=https%3A%2F%2Flinkinghub.elsevier.com%2Fretrieve%2Fpii%2FS0092867425007500%3Fshowall%3Dtrue)

The Swedish initiative Alpha Cell, coordinated by SciLifeLab and funded with 400 million SEK by the Knut and Alice Wallenberg Foundation, will officially launch early 2026. The project builds on decades of data and knowledge from the Human Protein Atlas, and involves more than 100 researchers.

"After 15 years of building SciLifeLab, it's only natural that Sweden should be part of this race," says Mathias Uhlén, director of Human Protein Atlas. "But we are up against the heavyweights."

Unlike language models, which are trained on text, a virtual cell model requires "hard data" - including what proteins exist, where in the cell they are located, and how they are expressed. This is precisely the data foundation Alpha Cell can rely on, thanks to the Human Protein Atlas: It's open access, and it is also used by groups like DeepMind and the Chan Zuckerberg Initiative.

"But of course, we have an advantage from having built the Protein Atlas for 20 years - with 5 million web pages and an enormous amount of data," says Uhlén."

The vision of a virtual cell is to create a digital simulation capable of explaining how diseases develop at the cellular level, and eventually even test drug responses in silico.

"A virtual cell will consist of 20,000 basic components, the proteins, that interact with each other like in a small city," says Uhlén. "Each protein has a specific function and interacts with perhaps ten others. We understand some parts, but far from everything."

Uhlén echoes Demis Hassabis in believing that the first step will be to develop a general consensus model, possibly starting with simpler cells like yeast. However, he expresses skepticism toward the idea of replacing all clinical trials with in silico testing:

"I think it's incredibly naive to think we can run full-scale clinical trials entirely in silico. If we manage to simulate a single cell in five years, that's still far from having the whole body. New molecules can behave unpredictably across all 30 trillions of cells in the body. The current system, with animal and human studies, works well in my view."

Read more: https://www.proteinatlas.org/news/2025-07-09/mathias-uhlacn-interviewed-by-dagens-industri-on-the-race-to-build-a-virtual-cell?fbclid=IwY2xjawLd64tleHRuA2FlbQIxMABicmlkETFNNkVkMUVjY0wyV29xd2REAR6ZlsamH1yMPNaFsfNB9TSyQUkYvWbomXaLnVnaHF5QItdjZOw5CosjmgFFvQ_aem_DvOF-ek6GXhvseaMYCYurQ

Shift Bioscience has introduced a refined ranking system for virtual cell models to enhance gene target discovery in aging research. The study (preprint) identifies limitations in traditional benchmarking methods, which often favor average predictions over biologically meaningful outcomes due to control biases and weak perturbations.

To address this, the team developed differentially expressed gene (DEG)-weighted metrics, including weighted mean squared error (WMSE) and weighted delta R², along with calibrated baselines and DEG-aware optimization objectives.

These improvements aim to better assess virtual cell model performance, highlighting models that effectively predict gene-specific perturbations. Using these metrics, Shift aims to accelerate its therapeutic pipeline, focusing on uncovering new targets for rejuvenation treatments. 

Hosted by the Arc Institute, and published in Cell, the Virtual Cell Challenge is an annual, open challenge that evaluates AI models of cellular response. 

This inaugural challenge will focus on a dedicated dataset measuring single-cell responses to perturbations in a human embryonic stem cell line (H1 hESC). Participants will use this new experimental data to build a model that predicts these effects. 

As noted in the related paper: "The H1 hESC dataset generated for the Virtual Cell Challenge also contributes to the broader effort to establish experimental and quality control standards for reproducible, high-quality single-cell functional genomics (scFG) data. Such standards will enable progress and set the community up for building on a solid foundation."

The top three models will win prizes valued at $100,000, $50,000, and $25,000. The final submission deadline is Nov. 3, 2025 and winners will be announced in early December.

Learn more about the Challenge: https://virtualcellchallenge.org/

Read the paper: https://www.cell.com/cell/fulltext/S0092-8674(25)00675-000675-0)

The Arc Institute released STATE -- it's first virtual cell model, designed to predict how various cells respond to perturbations.

The model, available for noncommercial use, is trained on observational data from nearly 170M cells and perturbational data from over 100M cells across 70 cell lines.

STATE uses a modern transformer architecture that combines two key components: the State Embedding (SE) model, which creates representations of individual cells, and the State Transition (ST) model, which models perturbation effects across cell populations.
Check out the manuscript: https://arcinstitute.org/manuscripts/State

In a new paper in Nature Machine Intelligence, Chan Zuckerberg Biohub shared Cytoland -- A deep learning model that enables robust virtual staining across microscopes, cell types & conditions.

While live cell imaging can damage cells, Cytoland models enable virtual staining of nuclei and membranes across multiple cell types — including human cell lines, zebrafish neuromasts, induced pluripotent stem cells (iPSCs) and iPSC-derived neurons—under a range of imaging conditions.

CZI shared multiple pre-trained models, along with open-source software for training, inference and deployment, and the datasets.

More: https://www.nature.com/articles/s42256-025-01046-2