Application of Machine Learning Models for Property Prediction in Targeted Protein Degraders

Tuesday, 9 July 2024, 22:40

Machine learning systems can predict properties of targeted protein degraders (TPDs) with high accuracy. The study evaluates the performance of ML models in predicting various properties such as passive permeability, metabolic clearance, and cytochrome P450 inhibition for TPD molecules. Transfer learning strategies improve predictions for different TPD sub-modalities, highlighting the potential of ML in drug discovery.
Nature
Application of Machine Learning Models for Property Prediction in Targeted Protein Degraders

Introduction

Machine learning (ML) systems can model quantitative structure-property relationships (QSPR) using existing experimental data and predict properties for new molecules. With the rise of targeted protein degraders (TPDs), the applicability of ML models for TPD-centric projects is explored.

Key Points

  • Performance on TPDs: ML models show comparable performance for TPD predictions in various properties.
  • Predictive Errors: Different predictive errors observed for sub-modalities such as glues and heterobifunctionals.
  • Transfer Learning: Strategies enhance predictions for heterobifunctionals.

Conclusion

ML-based QSPR models are effective in predicting ADME and physicochemical properties of TPD molecules, supporting their use in drug discovery for TPDs.


This article was prepared using information from open sources in accordance with the principles of Ethical Policy. The editorial team is not responsible for absolute accuracy, as it relies on data from the sources referenced.


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