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Biocatalysis

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Accelerating the optimization of enzyme-catalyzed synthesis conditions machine learning and reactivity descriptors.

Organic & biomolecular chemistry
Enzyme-catalyzed synthesis reactions are of crucial importance for a wide range of applications. An accurate and rapid selection of optimal synthesis conditions is crucial and challenging for both human knowledge and computer predictions. In this wor...

Machine Learning and Chemical Imaging to Elucidate Enzyme Immobilization for Biocatalysis.

Analytical chemistry
Biocatalysis has rapidly become an essential tool in the scientific and industrial communities for the development of efficient, safe, and sustainable chemical syntheses. Immobilization of the biocatalyst, typically an engineered enzyme, offers signi...

Machine Learning for Prediction, Classification, and Identification of Immobilized Enzymes for Biocatalysis.

Pharmaceutical research
BACKGROUND: Enzyme immobilization is a beneficial component involved in biocatalytic strategies. Understanding and evaluating the enzyme immobilization system plays an important role in the successful development and implementation of the biocatalysi...

Data-driven enzyme engineering to identify function-enhancing enzymes.

Protein engineering, design & selection : PEDS
Identifying function-enhancing enzyme variants is a 'holy grail' challenge in protein science because it will allow researchers to expand the biocatalytic toolbox for late-stage functionalization of drug-like molecules, environmental degradation of p...

De novo design of luciferases using deep learning.

Nature
De novo enzyme design has sought to introduce active sites and substrate-binding pockets that are predicted to catalyse a reaction of interest into geometrically compatible native scaffolds, but has been limited by a lack of suitable protein structur...

Enzyme function prediction using contrastive learning.

Science (New York, N.Y.)
Enzyme function annotation is a fundamental challenge, and numerous computational tools have been developed. However, most of these tools cannot accurately predict functional annotations, such as enzyme commission (EC) number, for less-studied protei...

Raman spectroscopy and one-dimensional convolutional neural network modeling as a real-time monitoring tool for in vitro transaminase-catalyzed synthesis of a pharmaceutically relevant amine precursor.

Biotechnology progress
Raman spectroscopy has been used to measure the concentration of a pharmaceutically relevant model amine intermediate for positive allosteric modulators of nicotinic acetylcholine receptor in a ω-transaminase-catalyzed conversion. A model based on a ...

ALDELE: All-Purpose Deep Learning Toolkits for Predicting the Biocatalytic Activities of Enzymes.

Journal of chemical information and modeling
Rapidly predicting enzyme properties for catalyzing specific substrates is essential for identifying potential enzymes for industrial transformations. The demand for sustainable production of valuable industry chemicals utilizing biological resources...

Artificial Intelligence Methods and Models for Retro-Biosynthesis: A Scoping Review.

ACS synthetic biology
Retrosynthesis aims to efficiently plan the synthesis of desirable chemicals by strategically breaking down molecules into readily available building block compounds. Having a long history in chemistry, retro-biosynthesis has also been used in the fi...

Navigating the landscape of enzyme design: from molecular simulations to machine learning.

Chemical Society reviews
Global environmental issues and sustainable development call for new technologies for fine chemical synthesis and waste valorization. Biocatalysis has attracted great attention as the alternative to the traditional organic synthesis. However, it is c...