ArticleNature communications2025
Chemistry-informed recommender system to predict optimal molecular receptors in SERS nanosensors.
Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
3 citing papers in PubMed.
- Interfacial Regulation-Driven Dual-Enrichment SERS Coupled With Deep Learning Enable Ultrasensitive and In Situ Identification of Microplastics in Natural Waters.Small (Weinheim an der Bergstrasse, Germany) · 2026Article
- High-dimensional CRISPR-SERS interactomics tracks the topological evolution of plant viral pathogenesis and therapeutic response.Chemical science · 2026Article
- Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
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.
Identifiers
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.