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Updated: Jan 9, 2026

Applying Cheminformatics to Develop a Structure Searchable Database of Analytical Methods

Applying Cheminformatics to Develop a Structure Searchable Database of Analytical Methods

Published on: June 6, 2025

SERS Cheminformatics: Opportunities for Data-Driven Discovery and Applications.

Emily Xi Tan^1,2, Lam Bang Thanh Nguyen^1,2, Yubin Jin²

¹Key Laboratory of Synthetic and Biological Colloids, Ministry of Education's International Joint Research Laboratory for Nano Energy Composites, School of Chemical and Material Engineering, Jiangnan University, Wuxi, P. R. China 214122.

ACS Central Science

|December 4, 2025

Summary

This summary is machine-generated.

Integrating cheminformatics with surface-enhanced Raman scattering (SERS) enhances analytical reliability. This approach uses computational tools to manage complex SERS data, improving chemical analysis and discovery.

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Area of Science:

Analytical Chemistry
Computational Chemistry
Spectroscopy

Background:

Surface-enhanced Raman scattering (SERS) provides ultrasensitive molecular fingerprinting but faces challenges with spectral complexity, noise, and variability.
The increasing volume of complex SERS data necessitates advanced computational tools for effective management and interpretation.

Purpose of the Study:

To propose a vision for integrating SERS with cheminformatics to improve the reliability of biochemical analysis and discovery.
To outline a conceptual framework for uniting these fields to address current limitations in SERS data analysis.

Main Methods:

Developing a framework based on four pillars: centralized SERS databases, molecular modeling, machine learning for spectral analysis, and AI for expanding the SERS chemical space.
Leveraging cheminformatics for spectral preprocessing, database management, molecular modeling, pattern recognition, and multimodal data integration.

Main Results:

The proposed integration creates a dynamic, feedback-driven system enhancing SERS data interpretability and accelerating discovery.
This synergy facilitates real-time SERS analysis and expands the chemical space accessible through SERS.

Conclusions:

The combination of SERS and cheminformatics offers a powerful, data-driven approach for chemical research.
This integration holds transformative potential for applications in materials science, catalysis, biomedical diagnostics, and environmental monitoring.