Machine Learning Method Developed by Chinese Researchers Analyzes Secondary Structures of Nucleic Acid Aptamers

Researchers at the Hangzhou Institute of Medical Sciences, part of the Chinese Academy of Sciences, have developed a machine learning-based method to analyze nucleic acid aptamers. The study, led by Weihong Tan, Xiaohong Fang, and Tao Bing, identifies common secondary structures in aptamers that bind to specific targets. This approach focuses on single-round aptamer analysis and aims to streamline the discovery process for these molecules, which are widely used in diagnostics and therapeutics.

The research utilizes advanced computational techniques to decode the intricate secondary structures of nucleic acid aptamers. Aptamers are short strands of DNA or RNA that can fold into unique shapes to bind specific targets with high affinity and specificity. By applying machine learning algorithms, the team analyzed structural patterns within a single round of selection rather than relying on traditional multi-round processes. This method reduces time and complexity while providing insights into shared structural features critical for target binding.

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Source: GO-AI-ne1

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Date: February 10, 2026

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GOAI