Virtual Cells: Predict, Explain, Discover

Drug discovery is fundamentally a process of inferring the effects of treatments on patients, and would therefore benefit immensely from computational models that can reliably simulate patient responses, enabling researchers to generate and test large numbers of therapeutic hypotheses safely and economically before initiating costly clinical trials. Even a more specific model that predicts the functional response of cells to a wide range of perturbations would be tremendously valuable for discovering safe and effective treatments that successfully translate to the clinic. Creating such virtual cells has long been a goal of the computational research community that unfortunately remains unachieved given the daunting complexity and scale of cellular biology. Nevertheless, recent advances in AI, computing power, lab automation, and high-throughput cellular profiling provide new opportunities for reaching this goal. In this perspective, we present a vision for developing and evaluating virtual cells that builds on our experience at Recursion. We argue that in order to be a useful tool to discover novel biology, virtual cells must accurately predict the functional response of a cell to perturbations and explain how the predicted response is a consequence of modifications to key biomolecular interactions. We then introduce key principles for designing therapeutically-relevant virtual cells, describe a lab-in-the-loop approach for generating novel insights with them, and advocate for biologically-grounded benchmarks to guide virtual cell development. Finally, we make the case that our approach to virtual cells provides a useful framework for building other models at higher levels of organization, including virtual patients. We hope that these directions prove useful to the research community in developing virtual models optimized for positive impact on drug discovery outcomes.

@article{noutahi2025_2505.14613,
  title={ Virtual Cells: Predict, Explain, Discover },
  author={ Emmanuel Noutahi and Jason Hartford and Prudencio Tossou and Shawn Whitfield and Alisandra K. Denton and Cas Wognum and Kristina Ulicna and Jonathan Hsu and Michael Cuccarese and Emmanuel Bengio and Dominique Beaini and Christopher Gibson and Daniel Cohen and Berton Earnshaw },
  journal={arXiv preprint arXiv:2505.14613},
  year={ 2025 }
}