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A machine learning-based workflow for transaminase selection.

Alexander J Rago¹, Priyanka Raghavan², Lisandra Santiago-Capeles¹

¹Small Molecule Chemistry Technologies, AbbVie, Inc. 1 N Waukegan Rd North Chicago IL 60064 USA alex.rago@abbvie.com wang.ying@abbvie.com.

Chemical Science

|May 22, 2026

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This study introduces machine learning models to predict transaminase enzyme activity and selectivity, streamlining chiral amine synthesis for medicinal chemistry. These models improve enzyme selection accuracy, reducing the need for extensive experimental screening.

Area of Science:

Biocatalysis and Enzyme Engineering
Medicinal Chemistry and Drug Discovery
Machine Learning in Chemistry

Background:

Transaminases are valuable biocatalysts for synthesizing chiral amines, essential building blocks in medicinal chemistry.
Selecting the optimal transaminase variant for specific substrates is challenging due to limited reactivity data.
Current methods often require empirical screening or chiral preparative separations, which are inefficient.

Purpose of the Study:

To develop predictive machine learning models for transaminase activity and selectivity.
To create a high-throughput experimentation (HTE) dataset for training and validating machine learning models.
To demonstrate the utility of these models in selecting appropriate transaminases for synthesizing relevant chiral amines.

Main Methods:

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Published on: March 1, 2024

Construction of a dataset comprising 336 transaminase reactions utilizing high-throughput experimentation (HTE).
Development and application of machine learning (ML) models to predict enzyme performance (activity and selectivity).
Validation of model predictions against baseline experimental results and prospective application to a held-out dataset.

Main Results:

Machine learning models accurately predicted substrate conversions and top-k enzyme selection for selectivity.
The developed models outperformed traditional baseline experimental approaches in predictive accuracy.
Successful prospective application of the modeling workflow for selecting transaminases for cyclic ketone substrates.

Conclusions:

Machine learning-guided enzyme selection significantly enhances the efficiency of chiral amine synthesis using transaminases.
The developed HTE-ML workflow provides a powerful tool for accelerating drug discovery by optimizing biocatalytic routes.
This approach alleviates the need for laborious chiral separations, offering a more sustainable and cost-effective synthesis strategy.