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<p>RNA is a remarkably versatile molecule that has been engineered for applications in therapeutics, diagnostics, and in vivo information-processing systems. However, the complex relationship between the sequence, structure, and function of RNA often necessitates extensive experimental screening of candidate sequences. Here we present a generalized, efficient neural network architecture that utilizes the <Emphasis Type="Underline">s</Emphasis>equence <Emphasis Type="Underline">and st</Emphasis>ructure <Emphasis Type="Underline">o</Emphasis>f <Emphasis Type="Underline">R</Emphasis>NA <Emphasis Type="Underline">m</Emphasis>olecules (SANDSTORM) to inform functional predictions across a diverse range of settings. We pair these predictive models with <Emphasis Type="Underline">g</Emphasis>enerative <Emphasis Type="Underline">a</Emphasis>dversarial <Emphasis Type="Underline">R</Emphasis>NA <Emphasis Type="Underline">d</Emphasis>esign <Emphasis Type="Underline">n</Emphasis>etworks (GARDN), allowing the generative modelling of a diverse range of functional RNA molecules with targeted experimental attributes. This approach enables the design of novel sequence candidates that outperform those encountered during training or returned by classical thermodynamic algorithms, and can be deployed using as few as 384 example sequences. SANDSTORM and GARDN thus represent powerful new predictive and generative tools for the development of RNA molecules with improved function.</p>

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Generative and predictive neural networks for the design of functional RNA molecules

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年5月4日

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  • Aidan T. Riley,
  • James M. Robson,
  • Aiganysh Ulanova,
  • Alexander A. Green

摘要

RNA is a remarkably versatile molecule that has been engineered for applications in therapeutics, diagnostics, and in vivo information-processing systems. However, the complex relationship between the sequence, structure, and function of RNA often necessitates extensive experimental screening of candidate sequences. Here we present a generalized, efficient neural network architecture that utilizes the sequence and structure of RNA molecules (SANDSTORM) to inform functional predictions across a diverse range of settings. We pair these predictive models with generative adversarial RNA design networks (GARDN), allowing the generative modelling of a diverse range of functional RNA molecules with targeted experimental attributes. This approach enables the design of novel sequence candidates that outperform those encountered during training or returned by classical thermodynamic algorithms, and can be deployed using as few as 384 example sequences. SANDSTORM and GARDN thus represent powerful new predictive and generative tools for the development of RNA molecules with improved function.