MANA symposium Sept. 4 at UC Davis: Computational Methods for Compound Identification and Quantum Chemistry

Compound identification is the most critical step in metabolomics and exposome research. While recent years have brought improvements in informatics tools, in-silico databases and experimental mass spectral libraries, there are still important bottlenecks to be resolved.

For the Metabolomics Association of North America (MANA), the West Coast Metabolomics Center at UC Davis is therefore hosting a one-day symposium on “Computational Methods for Compound Identification and Quantum Chemistry”. This symposium will present developments and recent breakthroughs from 11 invited speakers, with ample time for discussion about prospects for future perspectives. If you want to present your research, we invite you to write a short (max. 300 word) abstract and present a poster. See the MANA website for program abstracts, registration and abstract submission.

The symposium will take place

  • Wednesday Sept. 4, 2019 from 9 a.m. to 7 p.m.
  • At the UC Davis Genome Center, 451 Health Sci. Drive, Davis CA 95616, auditorium, GBSF1005.

Seating is limited, so register now!
Breakfast, lunch and dinner reception (for poster session) will be provided.

For researchers who need travel support, we can offer stipends through the NSF research coordination network, based on need. Please contact Prof. Oliver Fiehn (

Invited speakers are:

  • Prof. Stefan Grimme, University Bonn / Germany,  “Quantum Chemistry prediction of NMR and EI mass spectra”
  • Dr. Yannick Djoumbou Feunang, Corteva Agriscience, “Cheminformatic tools for enabling metabolomics”
  • Prof. Ryan Renslow, PNNL, “ISiCLE ion mobility prediction, deep learning, and quantum chemistry by density function theory”
  • Dr. Stephen E. Stein, NIST, “Spectral library based methods for identifying compounds that are not in the library”
  • Dr. Jennifer Wei, Google Brain, “Neural networks for predicting EI mass spectra”
  • Prof. Lloyd W. Sumner, University Missouri “Development of integrated computational and empirical tools to address the metabolomics grand challenges of confident metabolite ID and increased depth-of-coverage”
  • Prof. Xiuxia Du, UNC Charlotte “Spectral deconvolution for constructing pure mass spectra for compound identification”
  • Prof. Pieter Dorrestein, UC San Diego “Mass spectrometry annotation and analysis at the repository scale”
  • Prof. Zheng-Jiang Zhu, CAS, Shanghai / China “MetDNA: metabolic reaction network-based recursive metabolite annotation for untargeted metabolomics”
  • Prof. David Grant, University of Connecticut “Predicting retention indices, ecom50 and IR spectra”
  • Dr. Ivana Blazenovic, DiscernDX, “Confidence in compound ID: software, databases and tools”.