Break free from limitations with AI Quantitation software
Leveraging state-of-the-art artificial intelligence and machine learning technologies, AI Quantitation software empowers users to effortlessly transform compatible datasets obtained from SCIEX Triple Quad, high-resolution accurate mass and Echo® MS+ systems into clear and actionable insights.
This robust software streamlines time-consuming steps in workflows and method development, resulting in heightened productivity and faster time to results. By enabling users to allocate their resources more effectively, AI Quantitation software drives projects further, faster, and more efficiently.
Working with wiff2 data generated by SCIEX OS software, AI Quantitation software offers:
Leveraging SCIEX SWATH acquisition or MRM-HR:
Kevin Bateman, Retired Pharmacokinetics, Pharmacodynamics and Drug Metabolism Expert
MRM compound optimization can be a complex and time-consuming exercise. In particular, for in vitro ADME screening where hundreds of compounds need to be optimized daily, the conventional approach of optimizing compounds for MRM analysis can represent a significant bottleneck.
AI Quantitation software leverages MRM transition prediction to address these challenges, revolutionizing transition selection and method development. The software provides an MRM prediction model that predicts the product ions of compounds based on their chemical structure. Using machine learning, it employs a Learning-to-rank model to predict product ions, eliminating resource-intensive experimental optimization.
Easily Improve prediction accuracy with user data
The model can incorporate additional user data to increase prediction accuracy for the compounds that are most relevant to your workflow. Simply import chemical structure files and associated MRM data to improve performance of the predictive model.
Model performance
This model was originally developed via a collaborative effort between Mass Analytica and BMS in the publication "Development of a Predictive Multiple Reaction Monitoring (MRM) Model for High-Throughput ADME Analyses Using Learning-to-Rank (LTR) Techniques." This model, using a dataset comprised of 5757 compounds provided by BMS, was applied to real-world HT-ADME samples. “Valid stability and permeability data were generated for 97% of compounds when employing predicted transitions.”
As vast quantities of high-throughput data are generated from Echo® MS+ workflows, AI Quantitation software provides an interactive well-plate view of the trending data, enabling you to gain valuable insights and make informed decisions.
AI Quantitation software provides tools to automate the data processing workflow. Using an API and scripting, the software can be configured to scan a file folder and wait for new SCIEX .wiff2 data files. When a new file appears, the software will automatically start processing the data according to a predefined workflow, storing the results in a database for subsequent review.
Register here to discover how AI Quantitation will serve your lab and smash data processing bottlenecks. Sign up now for more information and/or to arrange a demonstration of the software.