AIMC Topic: Catalysis

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Optimizing Vanadium-Catalyzed Epoxidation Reactions: Machine-Learning-Driven Yield Predictions and Data Augmentation.

Journal of chemical information and modeling
Catalytic epoxidations are key chemical processes serving as essential steps in the synthesis of commercially valuable compounds. This study presents an innovative supervised machine learning (ML) model to predict the reaction yield of the vanadium-c...

Catalytic mechanism and engineering of aromatic prenyltransferase: A review.

International journal of biological macromolecules
The prenylation of aromatic compounds significantly enhances their metabolic stability and bioactivity. Prenyltransferases, as essential biocatalysts, facilitate the regioselective transfer of prenyl groups from donors to aromatic substrates. This re...

Machine learning approach for photocatalysis: An experimentally validated case study of photocatalytic dye degradation.

Journal of environmental management
In this study, machine learning (ML) models coupled with genetic algorithm (GA) and particle swarm optimization (PSO) were applied to predict the relative influence of experimental parameters of photocatalytic dye removal. Specifically, the impact of...

Enhancing photocatalytic degradation of hazardous pollutants with green-synthesized catalysts: A machine learning approach.

Journal of environmental management
The effective removal of organic pollutants from wastewater necessitates the development of advanced photocatalytic materials. This study explores the application of machine learning algorithms to predict the degradation efficiency of PRM using green...

Harnessing AI to revolutionize photocatalytic degradation of Tetracycline via optimized UV/ZrO/NaOCl reaction pathways.

Scientific reports
This paper assesses the presentation of Gradient Boosting Regression (GBR), Ridge Regression (RR), and Particle Swarm Optimization (PSO) models in improving the photocatalytic destruction of antibiotic utilizing a UV/ZrO₂/NaOCl system. The GBR model ...

NNKcat: deep neural network to predict catalytic constants (Kcat) by integrating protein sequence and substrate structure with enhanced data imbalance handling.

Briefings in bioinformatics
Catalytic constant (Kcat) is to describe the efficiency of catalyzing reactions. The Kcat value of an enzyme-substrate pair indicates the rate an enzyme converts saturated substrates into product during the catalytic process. However, it is challengi...

Tackling a textbook example of multistep enzyme catalysis with deep learning-driven design.

Molecular cell
Enzyme design has struggled to emulate the complexity and catalytic proficiency of natural enzymes. Lauko et al. show that with the help of deep learning, the design of serine hydrolases that rival nature's ingenuity is possible.

DLTKcat: deep learning-based prediction of temperature-dependent enzyme turnover rates.

Briefings in bioinformatics
The enzyme turnover rate, ${k}_{cat}$, quantifies enzyme kinetics by indicating the maximum efficiency of enzyme catalysis. Despite its importance, ${k}_{cat}$ values remain scarce in databases for most organisms, primarily because of the cost of exp...

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...

Leveraging Machine Learning for Enantioselective Catalysis: From Dream to Reality.

Chimia
Catalyst optimization for enantioselective transformations has traditionally relied on empirical evaluation of catalyst properties. Although this approach has been successful in the past it is intrinsically limited and inefficient. To address this pr...