AI for Nucleic Acids (AI4NA)
Keywords: RNA structure prediction, RNA function prediction, generative models for nucleic acids, nucleic acids modifications, nucleic acids foundation models, drug discovery, genome assembly, nucleic acids therapeutics, gene expression, single-cell transcriptomics and genomics
TL;DR: The workshop aims to foster collaborations that advance the role of AI in nucleic acid research, ultimately pushing the boundaries of what AI can achieve in understanding and manipulating life’s fundamental molecules.
Abstract: In recent years, the AI community has made significant strides in protein research, particularly since the breakthrough of AlphaFold2, which has led to advancements in structural biology and drug discovery. The success achieved on proteins gives hope to achieve comparable success on nucleic acids, RNA and DNA. The proposed workshop aims to highlight the unique challenges and possibilities of applying AI to nucleic acids. While advances in RNA structure prediction and nucleic acid language models show promise, the field lags behind proteins in the scale and quality of data and predictive accuracy. Addressing these challenges will drive critical applications in diagnostics, therapeutics, and biotechnology, such as mRNA therapeutics design, RNA-targeting small molecules, and improved genetic variant calling. Furthermore, there is space for advancement in reconstructing complex genomes, such as cancer or plant genomes, and detecting and understanding epigenetic and epitranscriptomic modifications. By bringing together AI researchers and domain experts in nucleic acids at the ICLR workshop, we aim to foster collaborations that advance the role of AI in nucleic acid research, ultimately pushing the boundaries of what AI can achieve in understanding and manipulating life’s fundamental molecules.
Submission Number: 95
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