Register today for the Virtual Cell Challenge and use AI to solve one of biology’s most complex problems. Announced in @CellCellPress, the competition is hosted by Arc Institute and sponsored by @nvidia, @10xGenomics, and @UltimaGenomics.

Cell paper: Website: Entrants will create models that will predict effects of single gene perturbations in cells. The top teams will receive prizes valued at $100k, $50k, and $25k—equal parts cash and @nvidia DGX Cloud credits.

For the inaugural challenge, Arc has generated a new single-cell transcriptomics dataset of 300,000 H1 human embryonic stem cells with 300 genetic perturbations, which will be deployed throughout the competition in segments for fine-tuning, validation, and testing.

The challenge will specifically evaluate how well models can predict changes in gene activity when individual genes are silenced. Competitors' interim performance will be shared on a live leaderboard, before the final assessment and announcement of the winners in December.

"The ability for models to generalize to new cell contexts is ultimately key to unlocking virtual cells for drug discovery and we hope this challenge will ultimately help accelerate progress towards that goal," says Arc CTO @davey_burke.

We’re grateful to the sponsors of the Virtual Cell Challenge (), leaders in AI, single cell and spatial biology, and DNA sequencing who have joined with Arc to push the boundaries of virtual cell modeling.

@nvidia provides the AI computing power that enables researchers to model cellular behavior at unprecedented scale and complexity.

@10xGenomics is a life science technology company building products to accelerate the mastery of biology and advance human health.

@UltimaGenomics is a leading developer of low-cost, high-quality sequencing technology that continuously drives the scale of omics information to enable unprecedented advances in biology and improvements in human health.

Arc plans to repeat the Virtual Cell Challenge each year with new single-cell datasets comprising different cell types, and with stiffer challenges requiring entrants’ models to predict the effects of more complicated changes. Sign up for updates at: