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One of the most interesting applications of artificial intelligence (AI) to life sciences was just announced earlier this month. |
| It is a level of precision – in terms of biological understanding and predictive capability – that is almost hard to believe. |
Developed by the non-profit Arc Institute in Palo Alto, California, Stack is a frontier biological AI model designed to accurately simulate and predict cellular conditions using the kinds of prompting of AI models that we have grown so used to. |
| This is an absolute breakthrough in single-cell biology that will have significant – and positive – ramifications for the biotech industry. |
| Cellular Context |
| Stack's development was a result of the large-scale single-cell RNA sequencing that had taken place over the prior years. |
| Data on hundreds of millions of cells has been collected across a wide range of tissues and conditions. |
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| Source: Arc Institute |
| From this massive repository of single-cell information, the team at Arc Institute trained its foundation model on data collected on 149 million uniformly preprocessed human single cells. |
| The data on these 149 million human single cells was intentionally diverse. |
| It spanned hundreds of tissues, diseases, donors, and states to understand how individual cells work, as well as how cells interact with one another. |
| This robust data set allowed the frontier model to understand cellular context, which is an understanding of not just the individual cell, but its relationship to other cells in a variety of conditions. |
| The result proved to be pretty incredible. |
| For example, the model can be given data on drug-treated immune cells and then predict how epithelial cells (skin cells) will react to that same drug. |
| What's so exciting is that the model can accurately perform this task, even though it wasn't explicitly trained to do so. It can just successfully infer an accurate outcome. |
| It's easy to imagine the implications for the biotech industry. |
| Researchers and companies can simply feed Stack real-world clinical data… and learn how a drug would work on different kinds of cells. |
| Doing so can help a company avoid bad decisions, as well as accelerate positive applications of a drug that perhaps they had not originally expected. |
| These newfound abilities will not only accelerate drug development but also dramatically reduce the cost of doing so. |
| And it gets better… |
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| Predictive Perturbations |
| The team at Arc took its research one step further… |
| They built on Stack and created Perturb Sapiens, which is an atlas of predicted cellular responses to perturbations. |
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| Source: Arc Institute |
| A perturbation simply means some kind of disruption to a cell's normal state. |
| This might be through the introduction of a drug therapy, a genetic modification through something like CRISPR genetic editing technology, or even subjecting the cells to environmental stress. |
| Understanding how different kinds of cells react to perturbations is a gold mine for accelerating the development of highly efficacious drugs and reducing the time and money spent on those that are ineffective and have high levels of toxicity. |
| The model isn't perfect yet, but it is a tremendous resource for the industry now, with approximately 20,000 predicted cell type-tissue-perturbation combinations. |
| A researcher can use a tool like this in a matter of minutes to discover predicted perturbations… and then confirm results with targeted experimentation. |
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| This is so powerful. |
| A biotech company can show model data on cells treated with its drug, and then the model will output how completely different cells would react to that same drug. |
| And the results do not require that perturbation to have been done before. |
| It simply infers the likely effect of the drug on the new cells. |
| Prompting Outcomes |
| Arc's Stack AI model works much the same way as large language models (LLMs). |
| You can ask a question, like, "What would be the impact on liver cells if exposed to this cardiovascular drug?" |
| We can take the model one step further and ask questions like, "What kinds of cells will experience off-target effects?" |
| And if we have an individual patient's cellular data, we can ask the model individualized questions, like, "How will this patient's cells respond to this drug therapy?" |
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| It's impossible not to be excited about life sciences and the biotech industry today. |
| Research and analysis that used to take years and hundreds of millions of dollars can now be performed with predictive accuracy using these frontier AI models and a bunch of GPUs. |
| The costs of running these models have become insignificant. And the outputs can be generated in a day, if not in minutes. Not years. |
| 2026 is the year the biotech golden age takes off. |
| It's the beginning of an acceleration in the improvement of the human condition and human longevity itself. |
| There is so much to look forward to. |
| Jeff |
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