  The current gold standard for unambiguous identification in metabolomics
analysis is based on comparing two or more orthogonal properties from the
analysis of authentic, pure reference materials (standards) to experimental
data acquired in the same laboratory with the same analytical methods. This
represents a significant limitation for comprehensive chemical identification
of small molecules in complex samples since this process is time-consuming and
costly, and the majority of molecules are not yet represented by standards,
leading to a need for standards-free identification. To address this need, we
are advancing chemical property calculations and developing multi-attribute
scoring and matching algorithms to utilize data from multiple analytical
platforms through the utilization and creation of the in silico Chemical
Library Engine (ISiCLE) and the Multi-Attribute Matching Engine (MAME). Here,
we describe our results in a blinded analysis of synthetic chemical mixtures as
part of the U.S. Environmental Protection Agency's (EPA) Non-Targeted Analysis
Collaborative Trial (ENTACT). The blinded false negative rate (FNR), false
discovery rate (FDR), and accuracy were 57%, 77%, and 91%, respectively. For
high confidence identifications, the FDR was 35%. After unblinding of the
sample compositions, we improved our approach by optimizing the scoring
parameters used to increase confidence. The final FNR, FDR, and accuracy were
67%, 53%, and 96%, respectively. For high confidence identifications, the FDR
was 10%. This study demonstrates that standards-free small molecule
identification and multi-attribute matching methods can significantly reduce
reliance on standards.
