Scholarlink

<p>Multiple molecular receptors amplify signal variance from various receptor–analyte interactions, enhancing the specificity of surface-enhanced Raman scattering (SERS) detection. Currently, the number and type of receptors are manually determined based on chemical intuition and trial-and-error experimentation, often leading to inefficiency, errors, and missed opportunities. Here, we design a chemistry-informed SERS receptor recommender system (RS) powered by a three-stage ‘identify, rank, and recommend’ XGBoost framework. Our RS predicts the best receptors to interact with structurally similar haloanisoles and generate distinct SERS superprofiles for enhanced differentiation, attaining over 95% accuracy. Leveraging collaborative filtering with our RS database, we further showcase receptor recommendations for an unidentified haloanisole based on its molecular structure and chemical reactivity, even before experimentally collecting its SERS data. This systematic and data-driven methodology based on cheminformatics represents a shift from empirical discovery to an efficient approach for receptor optimization for complex sensing applications involving structurally similar analytes.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Chemistry-informed recommender system to predict optimal molecular receptors in SERS nanosensors

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年8月2日

查看全文
  • Emily Xi Tan,
  • Yong Xiang Leong,
  • Shan Huei Lim,
  • Mike Wei Kuang Chng,
  • In Yee Phang,
  • Xing Yi Ling

摘要

Multiple molecular receptors amplify signal variance from various receptor–analyte interactions, enhancing the specificity of surface-enhanced Raman scattering (SERS) detection. Currently, the number and type of receptors are manually determined based on chemical intuition and trial-and-error experimentation, often leading to inefficiency, errors, and missed opportunities. Here, we design a chemistry-informed SERS receptor recommender system (RS) powered by a three-stage ‘identify, rank, and recommend’ XGBoost framework. Our RS predicts the best receptors to interact with structurally similar haloanisoles and generate distinct SERS superprofiles for enhanced differentiation, attaining over 95% accuracy. Leveraging collaborative filtering with our RS database, we further showcase receptor recommendations for an unidentified haloanisole based on its molecular structure and chemical reactivity, even before experimentally collecting its SERS data. This systematic and data-driven methodology based on cheminformatics represents a shift from empirical discovery to an efficient approach for receptor optimization for complex sensing applications involving structurally similar analytes.