Critical Assessment of Small Molecule Identification 2016: Automated Methods

Critical Assessment of Small Molecule Identification 2016: Automated Methods. Schymanski, E. L., Ruttkies, C., Krauss, M., Brouard, C., Kind, T., Dührkop, K., Allen, F. R., Vaniya, A., Verdegem, D., Böcker, S., Rousu, J., Shen, H., Tsugawa, H., Sajed, T., Fiehn, O., Ghesquière, B., & Neumann, S. J Cheminf, 9:22, 2017.
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Background: The fourth round of the Critical Assessment of Small Molecule Identification (CASMI) Contest (www.casmi-contest.org) was held in 2016, with two new categories for automated methods. This article covers the 208 challenges in Categories 2 and 3, without and with metadata, from organization, participation, results and post-contest evaluation of CASMI 2016 through to perspectives for future contests and small molecule annotation/identification. Results: The Input Output Kernel Regression (CSI:IOKR) machine learning approach performed best in "Category 2: Best Automatic Structural Identification–In Silico Fragmentation Only", won by Team Brouard with 41% challenge wins. The winner of "Category 3: Best Automatic Structural Identification–Full Information" was Team Kind (MS-FINDER), with 76% challenge wins. The best methods were able to achieve over 30% Top 1 ranks in Category 2, with all methods ranking the correct candidate in the Top 10 in around 50% of challenges. This success rate rose to 70% Top 1 ranks in Category 3, with candidates in the Top 10 in over 80% of the challenges. The machine learning and chemistry-based approaches are shown to perform in complementary ways. Conclusions: The improvement in (semi-)automated fragmentation methods for small molecule identification has been substantial. The achieved high rates of correct candidates in the Top 1 and Top 10, despite large candidate numbers, open up great possibilities for high-throughput annotation of untargeted analysis for "known unknowns". As more high quality training data becomes available, the improvements in machine learning methods will likely continue, but the alternative approaches still provide valuable complementary information. Improved integration of experimental context will also improve identification success further for "real life" annotations. The true "unknown unknowns" remain to be evaluated in future CASMI contests.

@Article{schymanski17critical,
  author    = {Emma Louise Schymanski and Christoph Ruttkies and Martin Krauss and C\'{e}line Brouard and Tobias Kind and Kai D\"{u}hrkop and Felicity Ruth Allen and Arpana Vaniya and Dries Verdegem and Sebastian B\"{o}cker and Juho Rousu and Huibin Shen and Hiroshi Tsugawa and Tanvir Sajed and Oliver Fiehn and Bart Ghesqui\`{e}re and Steffen Neumann},
  title     = {{Critical} {Assessment} of {Small} {Molecule} {Identification} 2016: Automated Methods},
  journal   = {J Cheminf},
  year      = {2017},
  volume    = {9},
  pages     = {22},
  abstract  = {Background: The fourth round of the Critical Assessment of Small Molecule Identification (CASMI) Contest (www.casmi-contest.org) was held in 2016, with two new categories for automated methods. This article covers the 208 challenges in Categories 2 and 3, without and with metadata, from organization, participation, results and post-contest evaluation of CASMI 2016 through to perspectives for future contests and small molecule annotation/identification.
Results: The Input Output Kernel Regression (CSI:IOKR) machine learning approach performed best in "Category 2: Best Automatic Structural Identification--In Silico Fragmentation Only", won by Team Brouard with 41% challenge wins. The winner of "Category 3: Best Automatic Structural Identification--Full Information" was Team Kind (MS-FINDER), with 76% challenge wins. The best methods were able to achieve over 30% Top 1 ranks in Category 2, with all methods ranking the correct candidate in the Top 10 in around 50% of challenges. This success rate rose to 70% Top 1 ranks in Category 3, with candidates in the Top 10 in over 80% of the challenges. The machine learning and chemistry-based approaches are shown to perform in complementary ways.
Conclusions: The improvement in (semi-)automated fragmentation methods for small molecule identification has been substantial. The achieved high rates of correct candidates in the Top 1 and Top 10, despite large candidate numbers, open up great possibilities for high-throughput annotation of untargeted analysis for "known unknowns". As more high quality training data becomes available, the improvements in machine learning methods will likely continue, but the alternative approaches still provide valuable complementary information. Improved integration of experimental context will also improve identification success further for "real life" annotations. The true "unknown unknowns" remain to be evaluated in future CASMI contests.},
  doi       = {10.1186/s13321-017-0207-1},
  keywords  = {jena; CASMI; MS; metabolites; tandem MS; MS/MS;},
  owner     = {Sebastian},
  pmid      = {29086042},
  timestamp = {2016.12.15},
}

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