News

New understanding of protein processing could have implications for Alzheimer's and some cancers
Healthy cells are constantly breaking down proteins and building up new ones with the help of enzymes aptly named unfoldases, ...
A new method for studying mechanical proteins and their involvement in muscular disorders
A team at the Centro Nacional de Investigaciones Cardiovasculares (CNIC), led by Jorge Alegre-Cebollada, has developed an ...
Algorithm maps the connections between the brain's structure and function
Using an algorithm they call the Krakencoder, researchers at Weill Cornell Medicine are a step closer to unraveling how the ...
Enhancing molecular machine learning with quantum-chemical insight
Molecular machine learning (ML) underpins critical workflows in drug discovery, material science, and catalyst optimization by rapidly predicting molecular interactions and properties. For instance, ...
C&EN16d
A guide to navigating AI chemistry hype
If you plan to use machine learning for research, consider ChatGPT’s shortcomings and inquire about AI tools’ training data and benchmarking performance ...
IEEE20d
Deep Contextual Representation Learning for Identifying Essential Proteins via Integrating Multisource Protein Features
In this work, we propose an end-to-end deep contextual representation ... attributes of each protein node in the protein interaction network, and then automatically learns topological features from ...
C&EN23d
Advancements in Ligand-Based Virtual Screening through the Synergistic Integration of Graph Neural Networks and Expert-Crafted Descriptors
Department of Computer Science, Vanderbilt University, 2201 West End Ave, Nashville, Tennessee 37235, United States ...
IEEE29d
Protein fold families prediction based on graph representations and machine learning methods
and graph edges connect amino acids that are close to each other under a given threshold. In this way we can get very descriptive features related to spatial and physical-chemical properties of the ...