AIMC Topic: Proteins

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The opportunities and challenges posed by the new generation of deep learning-based protein structure predictors.

Current opinion in structural biology
The function of proteins can often be inferred from their three-dimensional structures. Experimental structural biologists spent decades studying these structures, but the accelerated pace of protein sequencing continuously increases the gaps between...

Geometric Interaction Graph Neural Network for Predicting Protein-Ligand Binding Affinities from 3D Structures (GIGN).

The journal of physical chemistry letters
Predicting protein-ligand binding affinities (PLAs) is a core problem in drug discovery. Recent advances have shown great potential in applying machine learning (ML) for PLA prediction. However, most of them omit the 3D structures of complexes and ph...

Prediction of drug protein interactions based on variable scale characteristic pyramid convolution network.

Methods (San Diego, Calif.)
MOTIVATION: In the process of drug screening, it is significant to improve the accuracy of drug-target binding affinity prediction. A multilayer convolutional neural network is one of the most popular existing methods for predicting affinity based on...

Machine Learning Advances in Predicting Peptide/Protein-Protein Interactions Based on Sequence Information for Lead Peptides Discovery.

Advanced biology
Peptides have shown increasing advantages and significant clinical value in drug discovery and development. With the development of high-throughput technologies and artificial intelligence (AI), machine learning (ML) methods for discovering new lead ...

Assessing protein homology models with docking reproducibility.

Journal of molecular graphics & modelling
Results of the recent Critical Assessment of Protein Structure (CASP) competitions demonstrate that protein backbones can be predicted with very high accuracy. In particular, the artificial intelligence methods of AlphaFold 2 from DeepMind were able ...

A machine learning strategy with clustering under sampling of majority instances for predicting drug target interactions.

Molecular informatics
Drug Target Interactions (DTIs) are crucial in drug discovery as it reduces the range of candidate searches, speeding up the drug screening process. Considering in vitro and in vivo experimentations are time and cost-expensive, there has been a surge...

Deep learning for reconstructing protein structures from cryo-EM density maps: Recent advances and future directions.

Current opinion in structural biology
Cryo-Electron Microscopy (cryo-EM) has emerged as a key technology to determine the structure of proteins, particularly large protein complexes and assemblies in recent years. A key challenge in cryo-EM data analysis is to automatically reconstruct a...

Simulation-based inference for non-parametric statistical comparison of biomolecule dynamics.

PLoS computational biology
Numerous models have been developed to account for the complex properties of the random walks of biomolecules. However, when analysing experimental data, conditions are rarely met to ensure model identification. The dynamics may simultaneously be inf...

GB-score: Minimally designed machine learning scoring function based on distance-weighted interatomic contact features.

Molecular informatics
In recent years, thanks to advances in computer hardware and dataset availability, data-driven approaches (like machine learning) have become one of the essential parts of the drug design framework to accelerate drug discovery procedures. Constructin...

Deep learning for protein complex structure prediction.

Current opinion in structural biology
Recent developments in the structure prediction of protein complexes have resulted in accuracies rivalling experimental methods in many cases. The high accuracy is mainly observed in dimeric complexes and other problems such as protein disorder and p...