Best practices for machine learning in antibody discovery and development.

Area of Science:

• Biotechnology
• Computational Biology
• Immunology

Background:

• Therapeutic antibody discovery has advanced significantly over the last 40 years.
• Machine learning (ML) presents opportunities to expedite this process, reducing costs and experimental iterations.
• Current ML applications in antibody design and development (D&D) face challenges due to data and evaluation method diversity.

Purpose of the Study:

• To critically review current practices in ML-guided therapeutic antibody D&D.
• To identify common pitfalls hindering the effective application of ML in this field.
• To propose guidelines for method development and evaluation to improve ML utility and adoption.

Main Methods:

• Review of existing literature and practices in ML-based antibody D&D.
• Analysis of challenges related to data diversity and evaluation metrics.
• Development of best practice recommendations for each stage of the ML process.

Main Results:

• ML has the potential to significantly improve the efficiency of therapeutic antibody D&D.
• Lack of standardized datasets and evaluation methods impedes progress and comparability.
• Identification of specific pitfalls in ML implementation across the D&D pipeline.

Conclusions:

• Establishing clear standards and guidelines is essential for the widespread adoption and advancement of ML in therapeutic antibody D&D.
• Implementing recommended best practices will enhance reproducibility and accelerate progress in the field.
• Standardized approaches are key to unlocking the full potential of ML for novel antibody therapeutics.