2018/11/17 by Jing Shan Lim, Lim, Jing, Joshua M. Wong +11 · 1 citation
Biochemistry, Genetics and Molecular Biology · Computer Science · Engineering · #Advanced Chemical Sensor Technologies #Chemical Physics (physics.chem-ph) #Computational Drug Discovery Methods #FOS: Computer and information sciences #FOS: Physical sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Metabolomics and Mass Spectrometry Studies
paper · pdf · doi:10.48550/arxiv.1811.07886
openalex publication_date 2018/11/17 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Chemical structure elucidation is a serious bottleneck in analytical chemistry today. We address the problem of identifying an unknown chemical threat given its mass spectrum and its chemical formula, a task which might take well trained chemists several days to complete. Given a chemical formula, there could be over a million possible candidate structures. We take a data driven approach to rank these structures by using neural networks to predict the presence of substructures given the mass spectrum, and matching these substructures to the candidate structures. Empirically, we evaluate our approach on a data set of chemical agents built for unknown chemical threat identification. We show that our substructure classifiers can attain over 90% micro F1-score, and we can find the correct structure among the top 20 candidates in 88% and 71% of test cases for two compound classes.