MRM Prediction

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 a user-defined training set of chemical structures. Using machine learning, it employs a Learning-to-rank model to predict product ions, eliminating resource-intensive experimental optimization.

Leverage your own MRM data to build a model that is relevant to your research

The software incorporates user data to build a predictive model based on 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.”