AI is slowly becoming a real collaborator in understanding life. Over the past few months AI systems have gone from predicting structures or gene expression to actually helping design molecules, simulate cells, and guide lab experiments.
Much of this progress comes from a new generation of foundation models in biology, massive systems trained on DNA, protein, and multi-omics data. These models can learn patterns across biology, making them useful for everything from genome decoding to protein design. According to a recent review, such models are starting to connect different biological layers—genes, cells, tissues—in a unified framework.
Another example is a single-cell foundation model described in Nature Communications Biology, which can integrate cellular data from different species and conditions to reveal hidden regulatory links.
https://www.nature.com/articles/s12276-025-01547-5
Why does this matter? Because the way we do biology is changing. The time between hypothesis and experiment is shrinking dramatically. An idea that once took months to test can now move from model to lab in days. The space for innovation is also expanding. These systems let scientists ask questions that span molecules, cells, and tissues rather than treating them separately. And finally, the responsibility is growing. As AI starts generating biological designs, researchers must make sure results are reproducible, safe, and interpretable.
https://arxiv.org/abs/2505.23579
If you work in genomics, metabolomics, or synthetic biology, this shift affects you directly. Do you have the right datasets to fine-tune these models? Can your infrastructure support rapid cycles of prediction and validation? Do you track provenance and reproducibility for AI-generated hypotheses? The labs that can answer yes to these questions will lead the next phase of digital biology.
AI in biology is moving from being an assistant to becoming a creative partner. The next generation of discoveries will not just come from analyzing data but from collaborating with intelligent systems that can imagine new forms of life and help us test them responsibly.
References
Baek S, et al. “Single-cell foundation models: bringing artificial intelligence to biology.” Nature Communications Biology, 2025.
https://www.nature.com/articles/s12276-025-01547-5
Le Song, Eran Segal, Eric Xing. “Toward AI-Driven Digital Organism: Multiscale Foundation Models for Predicting, Simulating and Programming Biology at All Levels.” arXiv preprint, December 2024.
https://arxiv.org/abs/2412.06993
“Foundational Models for AI in Biology.” Ardigen, 2025.
“Foundation models in drug discovery: phenomenal growth in biotech.” ScienceDirect, 2025.
https://www.sciencedirect.com/science/article/pii/S1359644625002314
Maurizio Morri - Science Blog 
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