DOI: 10.5281/zenodo.16438770
title: Enhancing automated drug substance impurity structure elucidation from tandem mass spectra through transfer learning and domain knowledge.

description: Liquid chromatography-tandem mass spectrometry (LC-MS/MS) is an essential analytical technique in the pharmaceutical industry, used particularly for elucidating the structure of unknown impurities in the synthesis of active pharmaceutical ingredients. However, the interpretation of mass spectra is challenging and time-consuming, requiring significant expertise. While recent computational tools aimed at automating this process have been developed, their accuracy in determining the chemical structure is limited. In this paper, we introduce a new method called SEISMiQ for elucidating unknown impurities from their MS/MS spectra. We are able to significantly improve elucidation accuracy by integrating domain experts’ knowledge, specifically the impurity sum formula and known substructure, into the model's training and inference process. Further performance improvements can be achieved through transfer learning from simulated MS/MS spectra of impurities from an in-house database. Finally, the need for any experimental data collection for finetuning can be circumvented by simulating the entire drug substance synthesis process in silico via reaction templates.
description: File Content Explanations:

checkpoints.zip : checkpoints for the pretrained model and a model finetuned on CASMI.test_datasets.zip : test datasets used for evaluation, including CASMI and a subset of MassSpecGymde_novo_results.zip : model predictions on the test datasets using only the spectrasirius_ranking.zip : the output of SIRIUS when ranking de novo seismiq predictions on CASMI datafragmentation_results.zip : model predictions on the test datasets when prompted with fragmentsseismiq_code_release.zip : source code for training and evaluation (as submitted for peer review)training_data.csv.gz : training data in csv formatsimulated_impurities.zip : example simulated impurities
description: Our dataset was compiled by aggregating spectra and molecules from a variety of sources acknowledged below, and the origin of each spectrum is explicitly indicated.Origins "FragGenie" and "cfm_id" are simulated spectra which we release under CC-BY 4.0.For all other sources, please check the individual licenses for commercial options and attribution requirements.GNPS - https://gnps.ucsd.eduMingxun Wang, Jeremy J. Carver, Vanessa V. Phelan, Laura M. Sanchez, Neha Garg, Yao Peng, Don Duy Nguyen, et al., Sharing and community curation of mass spectrometry data with Global Natural Products Social Molecular Networking. Nature Biotechnology, 34(8), 828–837 (2016).HMDB - https://hmdb.ca/citingWishart DS, Guo AC, Oler E, et al., HMDB 5.0: the Human Metabolome Database for 2022. Nucleic Acids Res. 2022. Jan 7;50(D1):D622–31. 34986597 SUMNER - https://sumnerlab.missouri.edu/download/Zhentian Lei, Li Jing, Feng Qiu, Hua Zhang, David Huhman, Zhiqin Zhou, and Lloyd W. Sumner. Construction of a UHPLC‐Tandem Mass Spectral Library of Plant Natural Products and Comparative Spectral Analyses. Analytical Chemistry, 2015, 87(14):7373‐7381. https://doi.org/10.1021/acs.analchem.5b01559MassBank - https://zenodo.org/records/15341877Horai H, Arita M, Kanaya S, et al. MassBank: a public repository for sharing mass spectral data for life sciences. J Mass Spectrom. 2010;45(7):703–714. DOI: 10.1002/jms.17771MoNA - https://mona.fiehnlab.ucdavis.edu/documentation/licenseVaniya, A., Mehta, S., Wohlgemuth, G., & Fiehn, O. (2019). MassBank of North America: using untargeted metabolomics and multistage fragmentation mass spectral libraries to annotate natural products in plants. Berichte aus dem Julius Kühn-Institut, (204).