PredMS

PredMS predicts the metabolic stability of small compounds in human liver microsomes to support early-stage drug discovery and compound prioritization.

Key Features:

• Model Basis: PredMS employs a random forest algorithm, leveraging its robustness for classification tasks.
• Training Data: The model was trained on an in-house database of metabolic stability measurements from 1,917 compounds.
• Prediction Categories: PredMS classifies small compounds as either stable or unstable with respect to metabolic stability in human liver microsomes.

Scientific Applications:

• Early-stage screening: Predicting metabolic stability in human liver microsomes to inform compound selection in drug discovery.
• Candidate prioritization: Prioritizing compounds for experimental testing and progression decisions based on predicted stability.
• Reducing experimental burden: Minimizing costly and time-consuming in vitro metabolic stability evaluations by providing predictive assessments.

Methodology:

PredMS was developed by training a random forest classification model on an in-house metabolic stability dataset of 1,917 compounds.