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Recently, biotechnology company Atomic AI announced the successful development of the first large-scale language model (LLM) that utilizes chemical mapping data. Atomic AI combines advanced machine learning techniques with the latest structural biology to solve the mysteries of RNA drug discovery

Researchers at Atomic AI have created a new platform component that leverages in-house custom wet Laboratory analysis of large-scale chemical mapping data collected. The scientists collected data on millions of RNA sequences and made more than a billion nucleotide-level measurements. Trained on this data, ATOM-1 develops a rich understanding of RNA, which can then be used to optimize the properties of different RNA patterns.

Atomic AI published an article on December 14 A preprint paper titled "ATOM-1: A basic model of RNA structure and function based on chemical map data" was published on bioRxiv. In this paper, Atomic AI describes in detail their unique ATOM-1™ platform components. This basic model can accurately predict the structure and function of RNA and plays an important role in improving the development of RNA therapeutics

Dr. Manjunath Ramarao, Chief Scientific Officer of Atomic AI Said:

"ATOM-1 is able to predict structural and functional aspects of RNA and key features of RNA patterns, including small molecules, mRNA vaccines, siRNA and circular RNA, to Help design treatments efficiently. Our goal is to create a streamlined drug discovery process to advance our own pipeline and work with partners to help validate their RNA targets and tools to ultimately deliver them to patients quickly and more effectively The treatments needed."

Stephan Eismann, Ph.D., founding scientist and director of machine learning at Atomic AI said:

"By building a large dataset based on RNA nucleotide modifications and next-generation sequencing, the Atomic AI team created the first RNA-based model. We are excited about the broad application of our model to other aspects of RNA research and its use in optimizing RNA-based drugs. We are excited about the potential of various properties, such as stability and translation efficiency of mRNA vaccines or activity and toxicity of siRNA."

RNA-based drugs and RNA-targeted drugs are emerging as promising new ways to treat disease. Optimizing these therapeutics requires time-consuming and expensive experimental screening, while rational design requires an accurate understanding of RNA structure and function. To date, there has been little high-quality RNA data available to the life sciences community. Because existing methods, such as animal models for gathering in vivo information or cryo-electron microscopy (cryo-EM) for determining 3D RNA structure, are difficult to use and time-consuming. Optimizing key RNA therapeutic properties, including stability, toxicity, and translation efficiency, has been challenging due to a lack of “real” data

To address this design challenge, Atomic AI launched ATOM-1, the first RNA-based model trained on chemical map data, through a data collection strategy developed specifically for machine learning training. Using a small probe neural network on top of ATOM-1 embeddings, we demonstrate that this model has developed a rich internal representation of RNA. Trained with a limited amount of additional data, these small networks achieved state-of-the-art accuracy on key RNA prediction tasks, demonstrating that this approach could enable therapeutic design across the entire RNA field.

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