AI Medical Compendium Journal:
Current opinion in structural biology

Showing 31 to 40 of 82 articles

Revolutionizing protein-protein interaction prediction with deep learning.

Current opinion in structural biology
Protein-protein interactions (PPIs) are pivotal for driving diverse biological processes, and any disturbance in these interactions can lead to disease. Thus, the study of PPIs has been a central focus in biology. Recent developments in deep learning...

AI for targeted polypharmacology: The next frontier in drug discovery.

Current opinion in structural biology
In drug discovery, targeted polypharmacology, i.e., targeting multiple molecular targets with a single drug, is redefining therapeutic design to address complex diseases. Pre-selected pharmacological profiles, as exemplified in kinase drugs, promise ...

Generative AI for graph-based drug design: Recent advances and the way forward.

Current opinion in structural biology
Discovering new promising molecule candidates that could translate into effective drugs is a key scientific pursuit. However, factors such as the vastness and discreteness of the molecular search space pose a formidable technical challenge in this qu...

Bonds and bytes: The odyssey of structural biology.

Current opinion in structural biology
Characterizing structural and dynamic properties of proteins and large macromolecular assemblies is crucial to understand the molecular mechanisms underlying biological functions. In the field of structural biology, no single method comprehensively r...

Artificial intelligence approaches for molecular representation in drug response prediction.

Current opinion in structural biology
Drug response prediction is essential for drug development and disease treatment. One key question in predicting drug response is the representation of molecules, which has been greatly advanced by artificial intelligence (AI) techniques in recent ye...

Folding and functions of knotted proteins.

Current opinion in structural biology
Topologically knotted proteins have entangled structural elements within their native structures that cannot be disentangled simply by pulling from the N- and C-termini. Systematic surveys have identified different types of knotted protein structures...

Finding functional motifs in protein sequences with deep learning and natural language models.

Current opinion in structural biology
Recently, prediction of structural/functional motifs in protein sequences takes advantage of powerful machine learning based approaches. Protein encoding adopts protein language models overpassing standard procedures. Different combinations of machin...

Machine learning methods for predicting protein structure from single sequences.

Current opinion in structural biology
Recent breakthroughs in protein structure prediction have increasingly relied on the use of deep neural networks. These recent methods are notable in that they produce 3-D atomic coordinates as a direct output of the networks, a feature which present...

Application of message passing neural networks for molecular property prediction.

Current opinion in structural biology
Accurate molecular property prediction, as one of the classical cheminformatics topics, plays a prominent role in the fields of computer-aided drug design. For instance, property prediction models can be used to quickly screen large molecular librari...