What’s needed for a virtual cell to succeed in drug discovery? A new perspective paper from Recursion and our AI research engine
@valence_ailays out our vision for a virtual cell as a system that can reliably drive the discovery of new drugs via an iterative loop of: predict, explain, discover. We’ve spent more than a decade building many of the foundational elements of a virtual cell at Recursion – namely, massive fit-for-purpose datasets generated from millions of cell experiments each week in our automated lab; machine learning models that have been trained on that data; and industry-leading computing power. Our pipeline of potential medicines serves as the final proving ground for how these models translate to patients in the real world. 🔹 As described in the paper, we are building virtual cells that will: ▪️ Predict: Accurately predicting how cells respond to a broad range of perturbations — from genetic edits to novel chemical compounds – is critical to prioritizing hypotheses and de-risking early-stage drug discovery. ▪️ Explain: By explaining their reasoning, virtual cells will provide causal insights essential for building biological understanding and designing increasingly precise therapies. ▪️ Discover: We envision virtual cells evolving into powerful engines for biological discovery and serving as active guides in the search for new drugs. 👉 Read the paper: arxiv.org/abs/2505.14613