🧬 New AI model predicts cellular responses to drugs and treatments

• Arc releases State, a virtual cell model trained on data from nearly 170 million cells to predict how different cell types react to drugs and genetic perturbations.
• The model shows 50 percent improvement in distinguishing perturbation effects and twice the accuracy in identifying differentially expressed genes compared to existing models.
• State can be used to simulate how stem cells, cancer cells, and immune cells respond to various treatments without first needing to test them experimentally.

Trained on data from 170 million cells

State is trained on observational data from nearly 170 million cells and perturbational data from over 100 million cells across 70 cell lines. The model uses data from the Arc Virtual Cell Atlas and is available for non-commercial use.

The model consists of two interlocking modules. The State Embedding model converts transcriptome data into a multidimensional vector space that computers can more easily understand. The State Transition model predicts how cells will transition between different parts of the learned manifold in response to a given perturbation.

Improved accuracy compared to existing models

During benchmarking on the Tahoe-100M dataset, State demonstrated a 50 percent improvement in distinguishing perturbation effects. The model achieved twice the accuracy in identifying true differentially expressed genes compared to existing computational approaches.

State is the first model to consistently beat simple linear baselines in this field. The model has been trained on perturbation data from more than 100 million cells, which is more than any other model to date.

Focus on perturbation data for causal relationships

State primarily uses perturbation data where specific genes are deliberately altered to observe their effects on the cell. Unlike observational data, perturbation data captures causal relationships between genes and directly reflects the underlying biological mechanisms.

While it might take tens of thousands of observations to infer a direct relationship between two genes, perturbation data can capture the same interaction with a single measurement. Arc has developed scBaseCount, the first agentic AI system in this space that collects and analyzes single-cell data in a uniform way.

Application for drug development

About 90 percent of drugs fail clinical trials due to poor efficacy or unintended side effects. Each drug that researchers test is essentially a tailored probe designed to perturb cells in a particular way.

A high-performing virtual cell model can help researchers discover new drugs capable of shifting cells between states - from diseased to healthy - with fewer side effects. This could improve success rates in clinical trials.

State can be used to simulate how cells respond to perturbations and then use those predictions to nominate and discover new drugs experimentally. Researchers can run millions of in silico perturbations to narrow down their hypotheses in the process of making original discoveries.

Scalability and future development

Training data for the virtual cell continues to grow, which improves the model's predictive accuracy. Scaling laws have been observed in several domains for several years, but this has only recently been established for biology.

Arc has also unveiled Cell_Eval, a comprehensive evaluation framework for virtual cell modeling. The framework advances beyond conventional metrics in the field and includes a suite of biologically relevant and interpretable metrics focused on differential expression prediction.

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