A hybrid framework for improving uncertainty quantification in deep learning-based QSAR regression modeling.

Area of Science:

• Computational chemistry
• Cheminformatics
• Drug discovery

Background:

• Accurate uncertainty quantification (UQ) is vital for statistical models in drug design, where errors are costly.
• Existing distance-based and Bayesian methods struggle with overconfidence on out-of-distribution data.
• Developing robust UQ methods is essential for real-world applications in medicinal chemistry.

Purpose of the Study:

• To investigate consensus strategies for combining distance-based and Bayesian approaches for enhanced UQ in QSAR regression.
• To improve the reliability of uncertainty estimates in predictive modeling for drug discovery.
• To address the challenge of overconfidence in out-of-distribution predictions.

Main Methods:

• Developed a hybrid framework integrating distance-based and Bayesian methods.
• Incorporated post-hoc calibration techniques for improved uncertainty estimates.
• Evaluated model performance using criteria for ranking and calibration ability.
• Conducted experiments on 24 diverse bioactivity datasets.

Main Results:

• The proposed hybrid framework significantly enhances the ability to rank absolute errors.
• Post-hoc calibration further improves uncertainty quantification, particularly in domain shift scenarios.
• The combined approach demonstrates robust performance across multiple datasets.
• Conceptual analysis highlights the complementarity of different UQ methods.

Conclusions:

• A hybrid approach combining distance-based, Bayesian methods, and post-hoc calibration offers superior uncertainty quantification for QSAR models.
• This framework effectively addresses challenges posed by domain shifts in drug discovery.
• The findings support the use of ensemble strategies for reliable UQ in cheminformatics and related fields.