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Updated: May 24, 2025

Kinase Inhibitor Screening In Self-assembled Human Protein Microarrays

Kinase Inhibitor Screening In Self-assembled Human Protein Microarrays

Published on: October 23, 2019

An antibody developability triaging pipeline exploiting protein language models.

James Sweet-Jones¹, Andrew C R Martin¹

¹Institute of Structural and Molecular Biology, Division of Biosciences, University College London, London, UK.

|March 5, 2025

Summary

This summary is machine-generated.

This study introduces a machine learning pipeline to identify therapeutic monoclonal antibodies (mAbs) with favorable developability features. The approach uses protein language models to select promising antibody candidates early in development, reducing costs and improving success rates.

Keywords:

Antibodies developability machine learning prediction protein language models

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Area of Science:

Biotechnology
Immunology
Computational Biology

Background:

Therapeutic monoclonal antibodies (mAbs) are a key class of biologic drugs.
While binding affinity is crucial, antibody sequence and structure significantly impact developability and clinical success.
Late-stage failures due to poor developability are common and costly.

Purpose of the Study:

To develop a computational pipeline for selecting developable antibody candidates from large libraries.
To leverage machine learning and protein language models for early-stage antibody screening.
To reduce the cost and improve the efficiency of therapeutic antibody development.

Main Methods:

Utilized paired human antibody sequence data.
Developed a machine learning pipeline employing protein language models.
Clustered antibody sequences based on similarity to antibodies with known clinical success.

Main Results:

Identified a method to predict antibody developability based on sequence features.
The pipeline successfully identifies antibodies likely to possess favorable developability characteristics.
Demonstrated a strategy to triage out antibodies with poor developability potential early.

Conclusions:

The proposed pipeline is a valuable tool for selecting therapeutic monoclonal antibody candidates.
This approach can significantly reduce the cost and time associated with antibody discovery.
Facilitates the pursuit of novel therapeutics by improving early-stage candidate selection.