Category 2: Best Identification LC/MS

Based on the full scan and tandem MS and/or MSn data, the goal is to determine the correct molecular structure using the spectral data and the additional information provided, where available. Not all compounds may be in any compound database(s)!

Category 4: Best Identification GC/MS

Here, the goal is to identify the correct structure using GC/MS data and the additional information provided, where available. Not all compounds may be in any compound database(s)!

Additional Information

Where available, we have also provided more information on the experimental background (e.g. biological sample, experimental factors, or possibly related compounds in the same sample), the retention time, information on the chromatographic system, and the retention times for a set of standards for each chromatographic system. Whether you use this information or not is up to you!

Submission format

For each unknown compound, we expect a plain text, tab separated file with at least two columns. The first column should contain the representation of the structure as the (standard) InChI or the SMILES code. The second column should contain the score. The score should be non-negative with a higher score representing a better candidate for the evaluation script to work properly. Any further information can be contained in additional columns (but will be ignored). Please do not include headers or row names. Again, only neutral compounds should be reported. Remember to save the file name in the format <participant>-<category>-<challenge>.txt .

Together with your submission, we will ask whether you used an automatic pipeline, i.e. whether it is possible to provide the spectral input data and all parameters, and obtain the ranked result list without manual intermediate assignments/selections or other interventions (separate, manual conversion steps to convert the input data and generate the expected submission format are of course allowed).

Furthermore, we ask whether any spectral libraries were used, either directly or indirectly to perform e.g. machine learning methods for structural classifiers which associate m/z and (fragment) structures. The optimization of other software parameters based on spectral libraries is allowed.