Predicting in silico electron ionization mass spectra using quantum chemistry

• Main Authors: Shunyang Wang, Tobias Kind, Dean J. Tantillo, Oliver Fiehn
• Format: Article
• Language: English
• Published: BMC 2020-10-01
• Series: Journal of Cheminformatics
• Subjects: Quantum chemistry; Similarity score; Mass spectra; QCEIMS
• Online Access: http://link.springer.com/article/10.1186/s13321-020-00470-3

Abstract Compound identification by mass spectrometry needs reference mass spectra. While there are over 102 million compounds in PubChem, less than 300,000 curated electron ionization (EI) mass spectra are available from NIST or MoNA mass spectral databases. Here, we test quantum chemistry methods (QCEIMS) to generate in silico EI mass spectra (MS) by combining molecular dynamics (MD) with statistical methods. To test the accuracy of predictions, in silico mass spectra of 451 small molecules were generated and compared to experimental spectra from the NIST 17 mass spectral library. The compounds covered 43 chemical classes, ranging up to 358 Da. Organic oxygen compounds had a lower matching accuracy, while computation time exponentially increased with molecular size. The parameter space was probed to increase prediction accuracy including initial temperatures, the number of MD trajectories and impact excess energy (IEE). Conformational flexibility was not correlated to the accuracy of predictions. Overall, QCEIMS can predict 70 eV electron ionization spectra of chemicals from first principles. Improved methods to calculate potential energy surfaces (PES) are still needed before QCEIMS mass spectra of novel molecules can be generated at large scale.