Deciphering key factors of active learning performance in biomolecular design

Area of Science:

• Biomolecular engineering
• Computational biology
• Machine learning

Background:

• Machine learning (ML) and active learning (AL) are increasingly used for efficient biomolecule design.
• Challenges in genetic engineering include complex, noisy, and sparse sequence-fitness relationships, hindering reliable AL evaluation.
• A comprehensive benchmark is needed to assess AL performance determinants in biomolecular design.

Purpose of the Study:

• To benchmark active learning (AL) algorithms for biomolecular sequence design.
• To identify key factors influencing AL efficiency in optimizing sequence fitness.
• To propose metrics for selecting optimal AL strategies in genetic engineering.

Main Methods:

• Developed a benchmark using large-scale protein and DNA regulatory sequence libraries.
• Evaluated uncertainty quantification (UQ) algorithms for calibration and accuracy.
• Assessed various sampling strategies and factors affecting optimization efficiency.

Main Results:

• Ensemble-based UQ algorithms demonstrated robustness and generality.
• No single sampling strategy was universally optimal; greedy iterative strategies showed practical utility.
• Optimization efficiency was primarily influenced by initial settings, distribution sparsity, and sequence similarity, not the specific AL algorithm.

Conclusions:

• Biomolecular sequence design efficiency using AL is more dependent on data characteristics and initial setup than the chosen AL algorithm.
• Proposed quantifiable metrics can aid in selecting appropriate AL strategies for practical applications.
• Findings provide valuable insights for implementing AL pipelines in genetic engineering.