SERS Cheminformatics: Opportunities for Data-Driven Discovery and Applications
Emily Xi Tan, Lam Bang Thanh Nguyen, Yubin Jin, Yan Lv, In Yee Phang, Xing Yi Ling
Abstract
Surface-enhanced Raman scattering (SERS) is a powerful analytical technique offering ultrasensitive, nondestructive molecular fingerprinting. However, challenges such as spectral overlap, noise, and signal variability, especially in complex mixtures, limit its reliability and reproducibility. With increasing volumes of complex SERS data, there is a pressing need for advanced tools to manage and interpret this information. Cheminformatics amalgamates chemical knowledge with computational methods to deliver solutions for spectral preprocessing, database management, molecular modeling, pattern recognition, and multimodal data integration. This Outlook presents a vision for uniting SERS and cheminformatics to enhance the reliability of (bio)-chemical analysis and discovery. We propose a conceptual framework built upon four interconnected pillars: (1) centralized SERS databases, (2) molecular modeling for mechanistic insights, (3) machine learning (ML) for spectral analysis, and (4) automation and artificial intelligence for expanding the SERS chemical space. Together, these four pillars form a dynamic, feedback-driven system that enhances interpretability, accelerates data-driven discovery, and facilitates real-time SERS analysis. The symbiotic relationship between SERS and cheminformatics positions this integration at the forefront of data-driven chemical research with transformative applications in materials science, catalysis, biomedical diagnostics, and environmental monitoring.