Category 1: Best Structure Identification on Natural Products

The challenges for Category 1 are natural products from several organisms of different possible origin (plants, fungi, marine sponges, algae or micro-algae), acquired on QToF instruments from Waters and Agilent.

Please note that a third data set for Category 1 will be available in early February.

Based on the MS and MS/MS and other data, the goal is to determine the correct molecular structure at the given retention time using the spectral data and the additional information provided. The additional information may include sample background, experimental factors and retention information in some cases. Whether you use this information or not is up to you. The data is available here.

All kinds of approaches, manual and automatic, are allowed and encouraged!

Category 2: Best Automatic Structural Identification - In Silico Fragmentation Only

The Category 2 files consist of several mixes measured together in one run on an Orbitrap Q Exactive. Each mix contains compounds that are "known" to the contestants, as well as several unknowns, which must be identified by the contestants. The data will be available soon.

Based on the MS/MS data, the goal is to determine the correct molecular structure by the first block of the InChIKey from among the candidates provided for that challenge, using in silico fragmentation techniques alone (i.e. neither retention time, nor spectral library lookup nor additional metadata should be considered). The "knowns" can be used to determine appropriate parameters. Mass spectral libraries can only be used for training of prediction models, but not to solve the challenge by querying with the peaklist against the library.

The aim of this category is to compare different fragmentation approaches, ranging from combinatorial, to rule-based, to simulations; the number of challenges means this is aimed specifically at automatic approaches. The abstract should describe the computational method, including the parameters used for the submission.

Category 3: Best Automatic Structural Identification - Full Information

This category uses the same data files and candidate lists as for Category 2, but in Category 3 any form of additional information can be used (retention time information, mass spectral libraries, patents, reference count, …). This allows to demonstrate whether/how much additional information can improve the results of the unknown annotation. The approach(es) used should be well documented in the abstract submitted with the results file.