A mass spectral library search algorithm that identifies compounds that differ from library compounds by a single "inert" structural component is described. This algorithm, the Hybrid Similarity Search, generates a similarity score based on matching both fragment ions and neutral losses. It employs the parameter DeltaMass, defined as the mass difference between query and library compounds, to shift neutral loss peaks in the library spectrum to match corresponding neutral loss peaks in the query spectrum. When the spectra being compared differ by a single structural feature, these matching neutral loss peaks should contain that structural feature. This method extends the scope of the library to include spectra of "nearest-neighbor" compounds that differ from library compounds by a single chemical moiety. Additionally, determination of the structural origin of the shifted peaks can aid in the determination of the chemical structure and fragmentation mechanism of the query compound. A variety of examples are presented, including the identification of designer drugs and chemical derivatives not present in the library.
Facing increasing
caseloads and an everchanging drug landscape,
forensic laboratories have been implementing new analytical tools.
Direct analysis in real time mass spectrometry (DART-MS) is often
one of these tools because it provides a wealth of information from
a rapid, simple analysis. The data produced by these systems, while
extremely useful, can be difficult to interpret, especially in the
case of complex mixtures, and therefore, mass spectral databases are
often used to assist in interpretation of data. Development of these
databases can be expensive and time-consuming and often relies on
manual evaluation of the underlying data. The National Institute of
Standards and Technology (NIST) released an initial DART-MS in-source
collisional-induced dissociation mass spectral database for seized
drugs in the early 2010s but it has not been updated to reflect the
increasing prevalence of novel psychoactive substances. Recently,
efforts to update the database have been undertaken. To assist in
development of the database, an automated data evaluation process
was also created. This manuscript describes the new NIST DART-MS Forensics
Database and the steps taken to automate the data evaluation process.
and parameter set perform well in ensuring enthalpy predictions are thermodynamically consistent, however, extrapolated melting temperatures appear unreliable. Developing models and parameter sets that ensure thermodynamic consistency is a priority with future TPC iterations.
This manuscript outlines a straightforward procedure for generating a map of similarity between spectra of a set. When applied to a reference set of spectra for Type I fentanyl analogs (molecules differing from fentanyl by a single modification), the map illuminates clustering that is applicable to automated structure assignment of unidentified molecules. An open-source software implementation that generates mass spectral similarity mappings of unknowns against a library of Type I fentanyl analog spectra is available at http://github.com/asm3nist/FentanylClassifier.
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