AI Medical Compendium Topic:
Models, Molecular

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DeepEnzyme: a robust deep learning model for improved enzyme turnover number prediction by utilizing features of protein 3D-structures.

Briefings in bioinformatics
Turnover numbers (kcat), which indicate an enzyme's catalytic efficiency, have a wide range of applications in fields including protein engineering and synthetic biology. Experimentally measuring the enzymes' kcat is always time-consuming. Recently, ...

Machine learning-assisted substrate binding pocket engineering based on structural information.

Briefings in bioinformatics
Engineering enzyme-substrate binding pockets is the most efficient approach for modifying catalytic activity, but is limited if the substrate binding sites are indistinct. Here, we developed a 3D convolutional neural network for predicting protein-li...

Comparative analysis of RNA 3D structure prediction methods: towards enhanced modeling of RNA-ligand interactions.

Nucleic acids research
Accurate RNA structure models are crucial for designing small molecule ligands that modulate their functions. This study assesses six standalone RNA 3D structure prediction methods-DeepFoldRNA, RhoFold, BRiQ, FARFAR2, SimRNA and Vfold2, excluding web...

Knot or not? Identifying unknotted proteins in knotted families with sequence-based Machine Learning model.

Protein science : a publication of the Protein Society
Knotted proteins, although scarce, are crucial structural components of certain protein families, and their roles continue to be a topic of intense research. Capitalizing on the vast collection of protein structure predictions offered by AlphaFold (A...

MvMRL: a multi-view molecular representation learning method for molecular property prediction.

Briefings in bioinformatics
Effective molecular representation learning is very important for Artificial Intelligence-driven Drug Design because it affects the accuracy and efficiency of molecular property prediction and other molecular modeling relevant tasks. However, previou...

Evidence from Machine Learning, Diagnostic Hub Genes in Sepsis and Diagnostic Models based on Xgboost Models, Novel Molecular Models for the Diagnosis of Sepsis.

Current medicinal chemistry
BACKGROUND: Systemic multi-organ dysfunction resulting from dysregulated immune responses in the host triggered by microbial infection or other factors is a major cause of death in sepsis, and secretory pathways play an important role in it.

Recent Advances in Protein Folding Pathway Prediction through Computational Methods.

Current medicinal chemistry
The protein folding mechanisms are crucial to understanding the fundamental processes of life and solving many biological and medical problems. By studying the folding process, we can reveal how proteins achieve their biological functions through spe...

Computational Protein Design - Where it goes?

Current medicinal chemistry
Proteins have been playing a critical role in the regulation of diverse biological processes related to human life. With the increasing demand, functional proteins are sparse in this immense sequence space. Therefore, protein design has become an imp...

Integrating AlphaFold and deep learning for atomistic interpretation of cryo-EM maps.

Briefings in bioinformatics
Interpretation of cryo-electron microscopy (cryo-EM) maps requires building and fitting 3D atomic models of biological molecules. AlphaFold-predicted models generate initial 3D coordinates; however, model inaccuracy and conformational heterogeneity o...