AI Medical Compendium Journal:
Current opinion in structural biology

Showing 61 to 70 of 82 articles

Artificial intelligence in the experimental determination and prediction of macromolecular structures.

Current opinion in structural biology
Machine learning methods, in particular convolutional neural networks, have been applied to a variety of problems in cryo-EM and macromolecular crystallographic structure solution. However, they still have only limited acceptance by the community, ma...

Recent advances in predicting protein-protein interactions with the aid of artificial intelligence algorithms.

Current opinion in structural biology
Protein-protein interactions (PPIs) are essential in the regulation of biological functions and cell events, therefore understanding PPIs have become a key issue to understanding the molecular mechanism and investigating the design of drugs. Here we ...

Artificial intelligence approaches to human-microbiome protein-protein interactions.

Current opinion in structural biology
Host-microbiome interactions play significant roles in human health and disease. Artificial intelligence approaches have been developed to better understand and predict the molecular interplay between the host and its microbiome. Here, we review rece...

Deep learning methods for 3D structural proteome and interactome modeling.

Current opinion in structural biology
Bolstered by recent methodological and hardware advances, deep learning has increasingly been applied to biological problems and structural proteomics. Such approaches have achieved remarkable improvements over traditional machine learning methods in...

AI in 3D compound design.

Current opinion in structural biology
The success of Artificial Intelligence (AI) across a wide range of domains has fuelled significant interest in its application to designing novel compounds and screening compounds against a specific target. However, many existing AI methods either do...

Graph neural network approaches for drug-target interactions.

Current opinion in structural biology
Developing new drugs remains prohibitively expensive, time-consuming, and often involves safety issues. Accurate prediction of drug-target interactions (DTIs) can guide the drug discovery process and thus facilitate drug development. Non-Euclidian da...

Deep generative modeling for protein design.

Current opinion in structural biology
Deep learning approaches have produced substantial breakthroughs in fields such as image classification and natural language processing and are making rapid inroads in the area of protein design. Many generative models of proteins have been developed...

Artificial intelligence based methods for hot spot prediction.

Current opinion in structural biology
Proteins interact through their interfaces to fulfill essential functions in the cell. They bind to their partners in a highly specific manner and form complexes that have a profound effect on understanding the biological pathways they are involved i...

Artificial intelligence challenges for predicting the impact of mutations on protein stability.

Current opinion in structural biology
Stability is a key ingredient of protein fitness, and its modification through targeted mutations has applications in various fields, such as protein engineering, drug design, and deleterious variant interpretation. Many studies have been devoted ove...

Adaptive machine learning for protein engineering.

Current opinion in structural biology
Machine-learning models that learn from data to predict how protein sequence encodes function are emerging as a useful protein engineering tool. However, when using these models to suggest new protein designs, one must deal with the vast combinatoria...