The SIRIUS team partners with leading research groups and industry partners to integrate the latest scientific advancements, ensuring SIRIUS remains the most versatile and powerful tool for your analyses.
mzio GmbH and Bright Giant GmbH announce a new integration between mzmine and SIRIUS. This collaboration enables researchers to launch SIRIUS analysis directly from mzmine feature tables, automating result handling and eliminating the need for manual data transfer to streamline small molecule discovery workflows.
Our recent study co-authored by researchers at Bright Giant, FSU Jena, Leiden University and Oncode Institute introduces a major leap forward in affinity selection screening for early drug discovery: Self-Encoded Libraries. Our approach uses advanced mass spectrometry to screen hundreds of thousands of small molecules in a single experiment, bypassing the significant limitations of traditional high-throughput screening as well as affinity selection with barcoded libraries. It allows drug discovery teams to identify high-affinity drug candidates faster, more affordably, and against targets previously inaccessible to common screening methods.
Unlocking the chemical ‘dark matter’ in metabolomics is a persistent challenge. A new approach addresses this by integrating derivatisation reactions for chemical labeling directly into the mass spectrometry workflow. It provides crucial structural information which is fed into small molecule annotation tools like SIRIUS to significantly constrain the molecular structure search space and boost annotation accuracy, even for previously undiscovered compounds. This powerful approach offers a scalable solution to unlock the vast, uncharted chemical space of the metabolome.
Untargeted mass spectrometry is a powerful tool for analyzing the immense chemical complexity of natural environments. However, interpreting such large datasets remains a significant challenge. To overcome this, researchers have developed an innovative approach using SIRIUS that prioritizes chemical profiling over exhaustive identification. This method allows for more effective comparisons of (micro-)biomes, providing deeper insights into biochemical diversity across different environments.
Machine learning is transforming analytical chemistry by enabling predictions of small molecule properties, crucial for drug development and other applications. However, ensuring reliable results requires careful selection of training data to avoid biases that can mislead models. Here, we explain why it was important to prepare high-quality training datasets for the machine learning methods in SIRIUS, especially given that many widely used datasets fail to evenly represent the diversity of biomolecular structures.
This project aims to develop robust computational methods and a user-friendly software to efficiently identify new bioactive small molecules from synthetic libraries using affinity selection mass spectrometry, significantly reducing the time and cost associated with drug discovery.
Bright Giant GmbH and Agilent Technologies are excited to announce their new product development collaboration, designed to enhance non-targeted metabolomics workflows using AI-based methods and
