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The Launch of rBio
On Thursday, the Chan Zuckerberg Initiative (CZI) announced the introduction of rBio, the first artificial intelligence model designed to reason about cellular biology through virtual simulations, eliminating the need for costly laboratory experiments. This groundbreaking development has the potential to significantly speed up biomedical research and drug discovery.
Innovative Approach to Biological Research
The rBio reasoning model, detailed in a research paper published on bioRxiv, introduces a novel method known as “soft verification.” This technique utilizes predictions from virtual cell models as training signals rather than relying solely on experimental data. This shift in methodology allows researchers to test biological hypotheses computationally before investing time and resources into expensive laboratory work.
Ana-Maria Istrate, a senior research scientist at CZI and the lead author of the study, explained, “The idea is that you have these super powerful models of cells, and you can use them to simulate outcomes rather than testing them experimentally in the lab. The traditional paradigm has been that 90% of the work in biology is conducted experimentally, while only 10% is computational. With virtual cell models, we aim to reverse that trend.”
CZI’s Ambitious Goals
This announcement marks a significant milestone in CZI’s overarching mission to “cure, prevent, and manage all diseases by the end of this century.” Under the guidance of pediatrician Priscilla Chan and Meta CEO Mark Zuckerberg, the $6 billion philanthropic initiative has increasingly directed its focus toward the intersection of artificial intelligence and biology.
Challenges in AI and Biological Research
As enterprise AI faces limitations such as power caps, rising token costs, and inference delays, CZI’s rBio addresses a crucial challenge in applying AI to biological research. While large language models like ChatGPT excel in processing text, biological foundation models often deal with complex molecular data that cannot be easily queried in natural language. Scientists have struggled to bridge the gap between powerful biological models and user-friendly interfaces.
Istrate noted, “Foundation models of biology—like GREmLN and TranscriptFormer—are built on biological data modalities, which means they cannot be interacted with in natural language. You have to find complicated ways to prompt them.”
The Solution: Conversational AI
rBio tackles this problem by distilling knowledge from CZI’s TranscriptFormer, a virtual cell model trained on 112 million cells from 12 species over 1.5 billion years of evolution, into a conversational AI system that researchers can query in plain English.
The core innovation of rBio lies in its training methodology. Traditional reasoning models learn from questions with clear answers, like mathematical equations. However, biological inquiries often involve uncertainty and probabilistic outcomes that do not fit neatly into binary categories. CZI’s research team, led by Senior Director of AI Theofanis Karaletsos and Istrate, addressed this challenge using reinforcement learning with proportional rewards.
Advanced Training Methodology
Rather than relying on simple yes-or-no verification, the model receives rewards proportional to the likelihood that its biological predictions align with reality, as determined by virtual cell simulations. The research paper explains, “We applied new methods to how LLMs are trained. Using an off-the-shelf language model as a scaffold, the team trained rBio with reinforcement learning, a common technique in which the model is rewarded for correct answers. However, instead of asking a series of yes/no questions, the researchers adjusted the rewards based on the likelihood that the model’s answers were correct.”
This approach enables scientists to pose complex questions, such as “Would suppressing the actions of gene A result in an increase in activity of gene B?” and receive scientifically grounded responses regarding cellular changes, including transitions from healthy to diseased states.
Testing and Performance
In tests against the PerturbQA benchmark—a standard dataset for evaluating gene perturbation predictions—rBio demonstrated competitive performance compared to models trained on experimental data. The system not only outperformed baseline large language models but also matched the performance of specialized biological models on key metrics. Notably, rBio exhibited strong “transfer learning” capabilities, effectively applying knowledge about gene co-expression patterns learned from TranscriptFormer to make accurate predictions about gene perturbation effects, a completely different biological task.