AI isn't just for designing RNA sequences. Its real value is in creating predictive models of complex cellular functions. This allows scientists to determine the precise set of instructions (RNAs) needed to make a cell perform a complex series of tasks, like targeting a brain tumor.

Standard AI models trained on public, observational biological data excel at descriptive tasks but underperform even linear models on causal predictions. To predict cellular responses to drug-like perturbations, models must be trained specifically on causal data generated from targeted experiments.

To create a predictive "virtual cell," data collection must shift from passive observation to active intervention. The strategy is to massively scale perturbation experiments (like Perturb-seq) across countless contexts and measure multi-modal responses, teaching the model cause and effect.

Today's "virtual cell" models represent training data well but cannot predict outcomes for novel interventions. The next frontier is building models that generalize to serve as true predictive oracles for experiments that haven't yet been performed, a key focus for BioHub.

Beyond its core architecture, X-Cell integrates five types of biological priors, including text embeddings from scientific literature, protein interaction networks, and morphology information. This diverse context allows the model to make more accurate predictions and provides interpretability by showing which priors are most important for specific cell types.

The primary obstacle to creating sophisticated AI models of cells isn't the AI itself, but the data. Existing datasets often perturb only one cellular variable at a time, failing to capture the complex interactions that arise from simultaneous changes. New platforms are needed to generate this multi-dimensional data.

The progress of AI in predicting cancer treatment is stalled not by algorithms, but by the data used to train them. Relying solely on static genetic data is insufficient. The critical missing piece is functional, contextual data showing how patient cells actually respond to drugs.

While acknowledging the power of Large Language Models (LLMs) for linear biological data like protein sequences, CZI's strategy recognizes that biological processes are highly multidimensional and non-linear. The organization is focused on developing new types of AI that can accurately model this complexity, moving beyond the one-dimensional, sequential nature of language-based models.

The key utility of a "virtual cell" model isn't just predicting outcomes within its training data. Its power is the ability to generalize and make accurate causal predictions in entirely new contexts, such as different cell types or primary cells from donors, where large-scale experiments are difficult or impossible.