Library annotation is the cornerstone of reliable small‑molecule MS/MS analysis. However, public spectral libraries can contain errors that lead to incorrect or misleading annotations. Learn about our automated library‑cleaning pipeline that systematically validates structures, masses, adducts, and metadata in public libraries.

Natural products and their derivatives make up the majority of FDA-approved small molecule drugs, yet natural products discovery still plays a small role in pharmaceutical R&D. Learn how Ometa Flow can accelerate discovery and overcome barriers to generating and producing new lead compounds.

The environmental persistence of per- and polyfluoroalkyl substances (PFAS) creates unique challenges for detection and regulation of this large class of synthetic chemicals. Learn how MassQL and molecular networking can rapidly identify novel PFAS in mass spectrometry data.

Quality control (QC) is a crucial component of manufacturing products. This application note demonstrates how untargeted metabolomics can rapidly identify novel contaminants and pinpoint sources of contamination.

Flavonoids are a diverse group of polyphenolic compounds that play crucial roles in plant metabolism and have potent anti-inflammatory properties. This application note highlights how MassQL and molecular networking can rapidly identify novel flavonoids in MS/MS data.

Understanding how a drug is modified in the human body is crucial for assessing its safety and efficacy. Every new drug candidate must undergo metabolite identification (MetID) studies to determine which metabolites are formed and how they affect the absorption, distribution, metabolism, and excretion (ADME) and pharmacokinetic/pharmcodynamic (PK/PD) parameters of the drug. This application note highlights how untargeted metabolomics and molecular networking can rapidly identify drug metabolites, even when their potential biotransformations are not known in advance.

Feature annotation is one of the major hurdles in analyzing and interpreting metabolomics data. A single untargeted metabolomics sample can produce tens of thousands of metabolic features, most of which do not match existing spectral libraries. Learn how reverse metabolomics and massQL can be used for high-throughput spectral library generation.