Doan Vu Hoang Minh, Ly Cao Duong, Mondal Sudip, Truong Thi Thuy, Nguyen Tan Dung, Choi Jaeyeop, Lee Byeongil, Oh Junghwan
Smart Gym-Based Translational Research Center for Active Senior's Healthcare, Pukyong National University, Busan 48513, Republic of Korea.
Research and Development Department, Senior AI Research Engineer, Vision-in Inc., Seoul 08505, Republic of Korea.
Anal Chem. 2024 Jul 15. doi: 10.1021/acs.analchem.4c01622.
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.
