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Uncertainty Quantification Using Neural Networks for Molecular Property Prediction.

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Uncertainty quantification (UQ) methods for neural models in molecular property prediction show varied performance. No single UQ approach is best, highlighting the need for further research in drug discovery applications.

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Area of Science:

  • Computational chemistry
  • Machine learning
  • Drug discovery

Background:

  • Uncertainty quantification (UQ) is crucial for molecular property prediction in drug discovery.
  • Neural models are increasingly used but are difficult to interpret, increasing the need for UQ.
  • Existing UQ methods lack a clear consensus on their comparative performance.

Purpose of the Study:

  • To systematically evaluate and compare different UQ methods for regression tasks.
  • To assess the performance of UQ methods in the context of molecular property prediction.
  • To identify reliable UQ techniques for drug discovery applications.

Main Methods:

  • Evaluation of several UQ methods on five regression datasets.
  • Utilizing multiple complementary performance metrics for comprehensive assessment.
  • Comparative analysis of UQ method efficacy and error ranking capabilities.

Main Results:

  • No single UQ method demonstrated unequivocal superiority across all tested scenarios.
  • Existing methods did not reliably rank errors across diverse datasets.
  • Performance varied significantly depending on the specific UQ technique and dataset.

Conclusions:

  • Current UQ methods may be insufficient for all common molecular property prediction use cases.
  • Further research is necessary to develop more robust and reliable UQ techniques.
  • Practical recommendations are provided for selecting well-performing existing UQ methods.