News

SIRIUS Outlook: Retention time prediction coming soon

Every LC–MS lab runs its own column, gradient and instrument. As a result, retention times rarely transfer between labs, and this valuable second line of evidence is usually ignored when annotating unknowns.

This new method changes the game. Instead of predicting absolute retention times, it predicts a Retention Order Index (ROI)—a robust, setup-independent ranking of how compounds elute. The result? Highly accurate RT predictions that actually transfer across different experimental setups with minimal calibration. No training data from your setup required.

We are already working on integrating this feature directly into SIRIUS.

Combining SIRIUS molecular structure annotation with transferable retention time prediction will take compound annotation confidence to a whole new level.

F. Kretschmer et al. Nat Methods (2026) 10.1038/s41592-026-03243-2

SIRIUS API Tutorial

Have you ever tried using the SIRIUS API to analyse your untargeted data? Together with the Böcker Lab, we released a step-by step guide on how to use PySirius for untargeted differential LC-MS/MS analysis.

J. A. Emmert et al. STAR Protoc. (2026) doi: 10.1016/j.xpro.2026.104771

SIRIUS 6.5 is here…

…and brings along some great new features:

Download SIRIUS 6.5 now.

How can AI accelerate plant drug discovery?

Is it time for a paradigm shift to solve the pharmaceutical “supply crisis” caused by the industrial instability of plant cell cultures?

Decoupling drug discovery from manufacturing.
Instead of using plants for production, researchers should harness the “productive chaos” of plant stress responses solely to discover new therapeutic compounds. Once discovered, these complex biosynthetic pathways can be transferred to stable microbial hosts or engineered cell factories for reliable, large-scale manufacturing.

Artificial intelligence and machine learning serve as the core engine for this shift by streamlining plant metabolic data into predictive pipelines. AI tools like SIRIUS quickly decode unknown molecular “dark matter,” allowing researchers to annotate, prioritize, and scale novel bioactive candidates far faster than traditional timelines allow.

D A Mosoh et al. Front. Plant Sci. (2026) doi: 10.3389/fpls.2026.1771802

What’s new in SIRIUS 6.3.4

Have you tried the latest version of SIRIUS and tested our new features yet?

✨ Directly integrate our curated spectral library containing high-quality public spectra from major repositories, including GNPS, MassBank, and MsnLib
✨ Export BioTransformer outputs as a structured reference databases
✨ Export publication-ready figures of fragmentation patterns as high-quality vector graphics in SVG format
✨ Directly navigate (jump) via right-click context menu to related features
✨ Get a warning if your manually modified structure is already in the candidate list
✨ Manually force to override automatic tool selection if you already have intermediate results and wish to avoid re-computation of computationally intensive jobs
✨ Retain high-resolution MS/MS spectra while excluding low-resolution or incompatible GC-MS data when importing public or custom spectral libraries

Download SIRIUS now and let us know what you think!

Unknown PFAS annotation in complex samples with SIRIUS

Want to have a peek at the newest SIRIUS developments and upcoming software features at ASMS 2026?
Visit us at our Poster WP 432 on Wednesday, June 03.

Identifying unknown PFAS in complex samples usually feels like a trade-off:
❌ Specialized tools often suffer from high false-positive rates.
❌ General tools often filter out high-fluorine formulas just to save on computation time.

✨ We present the integration of a specialized PFAS detection strategy directly into SIRIUS to merge it with general small molecule annotation.

Stop by at our poster to discuss with Kai Dührkop how our newest approach will accelerate your PFAS detection workflow.

Download SIRIUS now!

SIRIUS Community Contributions at ASMS 2026

✨ We are proud to see the SIRIUS Community covering so many different research fields at ASMS 2026. From toxicology, food safety, natural products to E&Ls and even neuroscience. It is incredibly rewarding to see our software powering such diverse and impactful science.

If you’re attending ASMS, make sure to add the community highlights to your schedule.

ASMS Advance Registration Deadline

ASMS 2026 is just around the corner! Don’t miss the Advance Registration Deadline on April 30.

We can’t wait to see you there to show you SIRIUS 6 in action.
📍 Stop by Booth 606 to say hi!

Spectral denoising affecting compound identification

A critical look at how spectral denoising affects compound identification and molecular network reliability.
A study found that applying a standard 5% intensity cutoff was often too aggressive, resulting in the loss of nearly 50% of the ions explained by SIRIUS.

N. Dalla Valle et al. Anal. Chem. (2025) doi: 10.1021/acs.analchem.5c02109

Downloadable Libraries & Transformation Product Database Export

⬇️ Downloadable Libraries: You can now download our integrated spectral library with curated spectra from GNPS, MassBank and MsnLib.

⬆️ Transformation Product Database Export: We now offer the export of the entire transformation product database generated with BioTransformer during your custom database import, as users love the simplicity of generating transformation products within SIRIUS.

Download the latest SIRIUS update now.

Analytica 2026 Reminder

The largest gathering for laboratory technology and analysis is just days away, and we are geeting ready to meeting you in Munich! See SIRIUS 6 in action—from automated LC-MS/MS preprocessing to de novo structure generation of unknowns.

Meet us at Analytica 2026

Stop by our booth to see SIRIUS in action. Whether you are tackling drug discovery, E&L detection, PFAS analysis, or natural product dereplication, our latest machine learning models are designed to accelerate your structure elucidation workflows.