AI Medical Compendium Journal:
Current opinion in structural biology

Showing 1 to 10 of 82 articles

Machine learning methods to study sequence-ensemble-function relationships in disordered proteins.

Current opinion in structural biology
Recent years have seen tremendous developments in the use of machine learning models to link amino-acid sequence, structure, and function of folded proteins. These methods are, however, rarely applicable to the wide range of proteins and sequences th...

Recent advances in AI-driven protein-ligand interaction predictions.

Current opinion in structural biology
Structure-based drug discovery is a fundamental approach in modern drug development, leveraging computational models to predict protein-ligand interactions. AI-driven methodologies are significantly improving key aspects of the field, including ligan...

Teaching AI to speak protein.

Current opinion in structural biology
Large Language Models for proteins, namely protein Language Models (pLMs), have begun to provide an important alternative to capturing the information encoded in a protein sequence in computers. Arguably, pLMs have advanced importantly to understandi...

Toward deep learning sequence-structure co-generation for protein design.

Current opinion in structural biology
Deep generative models that learn from the distribution of natural protein sequences and structures may enable the design of new proteins with valuable functions. While the majority of today's models focus on generating either sequences or structures...

From part to whole: AI-driven progress in fragment-based drug discovery.

Current opinion in structural biology
Fragment-based drug discovery is a technique that finds potent binding fragments to the binding hotspots and makes them a hit compound. The combination of fragments allows us to explore the large chemical space. Thus, it becomes an effective methodol...

Deep learning for RNA structure prediction.

Current opinion in structural biology
Predicting RNA structures from sequences with computational approaches is of vital importance in RNA biology considering the high costs of experimental determination. AI methods have revolutionized this field in recent years, enabling RNA structure p...

Modeling Boltzmann-weighted structural ensembles of proteins using artificial intelligence-based methods.

Current opinion in structural biology
This review highlights recent advances in AI-driven methods for generating Boltzmann-weighted structural ensembles, which are crucial for understanding biomolecular dynamics and drug discovery. With the rise of deep learning models such as AlphaFold2...

Protein ligand structure prediction: From empirical to deep learning approaches.

Current opinion in structural biology
Protein-ligand structure prediction methods, aiming to predict the three-dimensional complex structure and binding energy of a compound and target protein, are essential in many structure-based drug discovery pipelines, including virtual screening an...

Modern machine learning methods for protein property prediction.

Current opinion in structural biology
Recent progress and development of artificial intelligence and machine learning (AI/ML) techniques have enabled addressing complex biomolecular problems. AI/ML models learn the underlying distribution of data they are trained on and when exposed to n...

AI-based methods for biomolecular structure modeling for Cryo-EM.

Current opinion in structural biology
Cryo-electron microscopy (Cryo-EM) has revolutionized structural biology by enabling the determination of macromolecular structures that were challenging to study with conventional methods. Processing cryo-EM data involves several computational steps...