The Apex software is designed to answer a simple question: Is my molecule in my sample?  By comparing spectral features predicted from molecular structure with experimental MS and MS/MS data, Apex can quickly analyze entire datasets to determine which of a set of structures is present in the sample.  Application areas include metabolite ID, combinatorial library deconvolution, confirmation of synthesis, target compound analysis, competitive drug binding assays, or any other structure confirmation application.


  • Raw data import directly from vendor format files
  • Structure import from MOL and SDF files with an arbitrary number of molecules
  • Support for combined MS and MS/MS experiments, with or without chromatographic separation
  • Simultaneous use of MS+ and MS- polarities
  • Full support for high resolution and mass accuracy measurements
  • Extensive parameterization to support multiple adducts, neutral losses, oligomers, multiple charge states
  • Batch processing of sample lists

For more information: 

As of January 2022, Apex is now a product of Mestrelab Research, our long-time collaborators headquartered in Santiago de Compostela, Spain, developers of the popular Mnova analytical chemistry software suite.  Please contact their sales department at sales@mestrelab.com for more information.

Publications featuring Apex:

Identification of In-Vitro Metabolites of Indinavir using Automated LC/MS/MS Acquisition, In-Silico Prediction, and Structure-Based Data Analysis
Casey Hao, Scott Campbell, David Stranz, and Nicole McSweeney, 2004

Structure-Based Confirmation of Small Molecules in LC/MS Datasets: Application to Metabolite Identification
David Stranz, Scott Campbell, and George Maydwell, 2004

Searching for In-Vitro Drug Metabolites in LC/MS Chromatograms Without the Aid of Radiolabels
Peter L. Jacobs, Joop C. M. Waterval, 2005

Solution-Based Indirect Affinity Selection Mass Spectrometry – A General Tool For High-Throughput Screening Of Pharmaceutical Compound Libraries
Thomas N. O’Connell, Jason Ramsay, Steven F. Rieth, Michael J. Shapiro, and Justin G. Stroh, 2014