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Related Experiment Video

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Multimodal Optical Imaging Platform for Studying Cellular Metabolism

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Published on: June 6, 2025

MetStabOn-Online Platform for Metabolic Stability Predictions.

Sabina Podlewska¹, Rafał Kafel²

¹Institute of Pharmacology, Polish Academy of Sciences, Department of Medicinal Chemistry, Smętna Street 12, 31-343 Kraków, Poland. smusz@if-pan.krakow.pl.

International Journal of Molecular Sciences

|March 31, 2018

Summary

A new in silico platform predicts metabolic stability for drug design using machine learning. It classifies compounds as low, medium, or high stability, aiding in optimizing new active compounds.

Keywords:

ChEMBL database classification machine learning metabolic stability regression

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Area of Science:

Computational chemistry
Pharmacology
Drug discovery

Background:

Metabolic stability is crucial for drug development but challenging to predict due to complex biological pathways.
Optimizing metabolic stability while maintaining compound activity is a significant hurdle in medicinal chemistry.

Purpose of the Study:

To develop an in silico platform for qualitative evaluation of metabolic stability (half-life and clearance).
To create machine learning models for predicting metabolic stability in humans, rats, and mice.
To provide a tool for researchers to assess and improve the metabolic profiles of new chemical entities.

Main Methods:

Development of machine learning models including Sequential Minimal Optimization (SMO), k-nearest neighbor (IBk), and Random Forest.
Qualitative evaluation through regression (predicting half-life) and classification (direct stability assessment).
Identification and provision of 10 similar structures from the training set for manual inspection.

Main Results:

Models demonstrated good predictive power with accuracy over 0.7 for SMO, IBk, and Random Forest algorithms.
Validation against an external dataset using GUSAR software showed good consistency for SMOreg and Naïve Bayes (~0.8 average).
The platform provides both classification and regression predictions for metabolic stability.

Conclusions:

The developed in silico platform offers a reliable method for assessing metabolic stability during compound design.
Machine learning models show significant predictive capability for metabolic stability across species.
The tool is accessible online, facilitating faster and more efficient drug discovery processes.