Fcg-Former: Identification of Functional Groups in FTIR Spectra Using Enhanced Transformer-Based Model
Vu Hoang Minh Doan, Cao Duong Ly, Sudip Mondal, Thi Thuy Truong, Tuan Dung Nguyen, Jaeyeop Choi, Byeongil Lee, Junghwan Oh
Abstract
Deep learning (DL) is becoming more popular as a useful tool in various scientific domains, especially in chemistry applications. In the infrared spectroscopy field, where identifying functional groups in unknown compounds poses a significant challenge, there is a growing need for innovative approaches to streamline and enhance analysis processes. This study introduces a transformative approach leveraging a DL methodology based on transformer attention models. With a data set containing approximately 8677 spectra, our model utilizes self-attention mechanisms to capture complex spectral features and precisely predict 17 functional groups, outperforming conventional architectures in both functional group prediction accuracy and compound-level precision. The success of our approach underscores the potential of transformer-based methodologies in enhancing spectral analysis techniques.