Drug Discovery
Dereplication at screening throughput.
Hundreds of extracts a week, with library generation, interactive molecular networking and database search in the same environment your chemists already use.
The bottleneck
Rediscovering a known compound is the expensive failure
In natural product discovery the cost is not in the screen, it is in chasing a hit that turns out to be a known metabolite. Dereplication has to happen at the speed of acquisition, not weeks later.
- Known compounds re-isolated because dereplication lagged the screen
- In-house libraries trapped in spreadsheets and personal folders
- Networking run in one tool, annotation in another, quantitation in a third
The workflow
From raw files to something you can publish.
Every step ships with default settings. You may customise each one, but you don’t have to.
Import
Hundreds of extracts as one batch, from any major vendor format.
Detect and resolve
Feature detection and deconvolution across the whole screen at once.
Group the ions
Adducts and in-source fragments collapsed so a cluster counts compounds, not signals.
Network
MS2 similarity explored interactively, without exporting to a separate tool.
Dereplicate
Public and in-house libraries queried in one pass to find the knowns early.
Build the library
Confirmed identifications versioned into a reference set the next screen starts from.
Capabilities
What you get out of the box.
Library generation
Build and version an in-house spectral library from confirmed identifications.
Interactive molecular networking
Explore the network without exporting to a separate tool.
Database search
Public and proprietary libraries queried in one pass.
Screening-scale batches
Hundreds of complex extracts processed as a single reproducible job.
In the software
The molecular network.
Twenty-four rows left, out of 9,526. The pie on each node shows which sample groups the feature turned up in, so anything that is only medium background is gone before a single spectrum is opened. Ion identity edges collapse the adducts of one molecule onto one node first, so a cluster counts compounds rather than the same compound three times over, and each annotation names the library entry behind it, down to the accession and the collision energy it was measured at.
In production
Labs already running this.
“As part of the R&D scent team at IFF, working with complex and diverse natural product extracts, efficient data processing, deconvolution, annotation, chemometrics, and interactive visualization are crucial for high-throughput understanding. mzmine Pro has provided us with not only a powerful and efficient GC-MS workflow to investigate volatile compounds but also a highly advanced LC-MS workflow, perfectly aligned with our aspirations. These tools are essential for enhancing our work, fostering innovation, and deepening our understanding of complex natural matrices.”
“As part of our drug discovery workflow, we analyze hundreds of complex fungal samples every week. mzmine PRO provides the necessary processing power and innovative compound discovery tools, such as library generation, interactive molecular networking, and database search to swiftly accelerate our daily routine. Data processing is no longer a bottleneck.”
The science
The methods behind this workflow, peer-reviewed.
Resources
Go deeper on Drug Discovery
We have written this up in more detail. There is one paper. One short form and all of it unlocks.
Get started
See it on your own data.
Send us a few representative files. We will build the drug discovery workflow and show you the result before you commit to anything.