About us

eScience Center visit 2022The NPLinker eScience project team during the first live meeting a few months after the kickoff. The team discussed data flows in genome and metabolome mining for omics-based natural products discovery. More info? Click  here.
Our research vision is to close the gap between what we can see in metabolomics and what we can actually learn from it.
In our group, we develop approaches and tools to support structural and functional annotation in untargeted metabolomics workflows. To do this together in a responsible and respective way, we held the following shared team values in high regard: open-mindedness, pragmatism, and collegiality. Doing so, we strive to create the environment and atmosphere in which each team member can equally contribute and commit in a fair manner, and where we feel accountable for our contributions. We will encourage each group member to develop at their own pace and regularly flourish. As a group, we support, encourage, and apply open science following the FAIR principles as much as possible. This applies both to the data we use and the scripts and code we produce.
We aim to create tools that will enable biochemical interpretation of spectral data. This will enable biochemical interpretation of spectral data obtained from complex metabolite mixtures through structural and functional annotations. This will depend on finding out: i) which structural information is encoded in metabolomics data; ii) how novel chemistry can be recognised in spectral data, and iii) how to effectively identify relevant metabolite groups in metabolomics profiles of complex metabolite mixtures?
The Van der Hooft Group develops computational metabolomics approaches inspired by two other fields - that of natural language processing (NLP) and genomics. For example, Justin has pioneered the use of topic modeling and word embedding NLP algorithms to discover substructures and structural relationships in metabolomics profiles.
The Van der Hooft Group uses the plant root microbiome and human food metabolome as prime applications since they represent complex metabolite mixtures full of yet unknown metabolic matter that once elucidated will boost our insights in molecular mechanisms underpinning the regulation of growth, development, and health.
Research pillar I
Machine learning to read metabolite structure from spectra
Research pillar I
Research pillar II
Chemically-informed comparative metabolomics to prioritize chemistry
Research pillar II
Research pillar III
Linked omics and activity profiles to gain functional insights
Research pillar III


The origin of MS2Query
By Niek de Jonge
The origin of MS2Query


Lucina-May Nollen successfully defended her MSc thesis
Koen van Ingen started as BSc student
Preprint out on CineMol
Kevin Mildau started as postdoc
Ids Willemsen started as MSc student


Netherlands eScience Center
iOMEGA project - Accelerating Scientific Discoveries 2018 grant; NPLinker 2.0 project - eScience Open Call 2021 grant