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Open access Jul 2026

Machine Learning-Assisted Evolution of Broadly Functional Enzyme Libraries

Results indicate that supervised machine learning can help guide the construction of high-value enzyme libraries with expanded catalytic scope, and suggest that supervised machine learning can help guide the construction of high-value enzyme libraries with expanded catalytic scope.

Ravi G. Lal, Jason Yang, Ziyan Zhang et al. · 0 citations
Open access Jul 2026

Combining Stability-Centered Atomistic Design with Machine Learning for Targeted Enzyme Optimization

A machine-learning-assisted enzyme-engineering (MLEE) workflow that adds substrate-specific functional information to htFuncLib through an initial screening and sequencing round that may bypass the need for transition-state models and reduce the effort required for obtaining high-activity variants.

Li Wan, Mahdi Bagherpoor Helabad, Lena Fraedrich et al. · 0 citations
Open access Jul 2026

An enzyme-specific protein language model for catalytic property prediction

This manuscript introduces EnzGFM, an enzyme-specific hybrid model that improves both accuracy and efficiency across multiple prediction tasks and, together with the EnzGFM-Agent pipeline, demonstrates the ability to identify experimentally validated beneficial variants while reducing screening effort.

Chong Wang, Mengyao Li, Shaolei Geng et al. · 0 citations
Review Aug 2026

Enzyme Engineering: From Classical Strategies to AI-Driven Biocatalyst Design.

This review examines enzyme engineering from classical methods to AI-assisted biocatalyst development, highlighting key advances, challenges, and emerging trends in autonomous laboratories, sustainable biocatalysis, and computational protein design.

Mati Ullah, Muhammad Rizwan, Vivian Andoh et al. · 0 citations
Open access Aug 2026

Coevolution-informed Bayesian optimization for sample-efficient protein design

This work introduces ALSEBO (Active Learning Sequence Exploration via Bayesian Optimization), which couples a generative latent sequence landscape to Bayesian optimization and featurizes candidates with direct-coupling-analysis (DCA) coevolutionary statistics.

D. P. Kulathunga, Divyanshu Shukla, D. Potoyan · 0 citations
Open access Jul 2026

AUKAT: Conditional VAE-Driven Augmentation and Neural Modeling of Enzyme Turnover Numbers

AUKAT, an integrated framework that combines conditional generative modeling with deep neural prediction to improve kcat estimation, provides a scalable approach for enzyme kinetics prediction and offers a practical solution to data scarcity in biochemical modeling.

Mengmeng Liu, Xialong Ni, Michal Brylinski · 0 citations