AI Scientist System CellForge Autonomously Designs and Validates Virtual Cell Models, Matching Human Expert Performance

Researchers from Yale University and other institutions have released version 3 of CellForge, demonstrating that a team of autonomous agents can complete the full scientific research cycle from hypothesis to validation. For the complex task of single-cell perturbation modeling, the system generates executable model hypotheses and architectures end-to-end without humans specifying the design, loss function, or training protocol.

The core innovation of CellForge is that "the architecture itself is the hypothesis." Through critic-mediated discussion among agents specialized in data modeling, single-cell biology, and deep learning, it formulates a model hypothesis, then writes training code, runs experiments, and returns a trained predictor including architecture, code, and validation results. Across six perturbation datasets spanning transcriptomic, epigenomic, and proteomic contexts, CellForge yields models competitive with hand-crafted state-of-the-art results and extends the same workflow to paired multi-omic readouts.

Blinded human evaluators rated CellForge's research plans as more scientifically sound and innovative than those from single large language models or deep-research systems. For instance, in drug perturbation tasks, CellForge improved Pearson correlation by 20%; on sparse, high-dimensional scATAC-seq datasets, it achieved approximately a 16-fold gain in Pearson correlation on differentially expressed genes compared to the second-best baseline.

This study proves that AI can effectively build another AI in a complex setting that currently requires a team of human experts. However, current results are based on single runs, with each dataset producing only one architecture instance, indicating this is an existence proof rather than a guarantee of optimal design.