[HTML][HTML] Protein–protein interaction prediction with deep learning: A comprehensive review
Most proteins perform their biological function by interacting with themselves or other
molecules. Thus, one may obtain biological insights into protein functions, disease …
molecules. Thus, one may obtain biological insights into protein functions, disease …
Protein–RNA interaction prediction with deep learning: structure matters
Protein–RNA interactions are of vital importance to a variety of cellular activities. Both
experimental and computational techniques have been developed to study the interactions …
experimental and computational techniques have been developed to study the interactions …
Diffdock-pp: Rigid protein-protein docking with diffusion models
Understanding how proteins structurally interact is crucial to modern biology, with
applications in drug discovery and protein design. Recent machine learning methods have …
applications in drug discovery and protein design. Recent machine learning methods have …
Machine learning solutions for predicting protein–protein interactions
Proteins are “social molecules.” Recent experimental evidence supports the notion that
large protein aggregates, known as biomolecular condensates, affect structurally and …
large protein aggregates, known as biomolecular condensates, affect structurally and …
Evaluating representation learning on the protein structure universe
We introduce ProteinWorkshop, a comprehensive benchmark suite for representation
learning on protein structures with Geometric Graph Neural Networks. We consider large …
learning on protein structures with Geometric Graph Neural Networks. We consider large …
Graphein-a Python library for geometric deep learning and network analysis on protein structures and interaction networks
Geometric deep learning has well-motivated applications in the context of biology, a domain
where relational structure in datasets can be meaningfully leveraged. Currently, efforts in …
where relational structure in datasets can be meaningfully leveraged. Currently, efforts in …
Knowledge-augmented Graph Machine Learning for Drug Discovery: From Precision to Interpretability
Conventional Artificial Intelligence models are heavily limited in handling complex
biomedical structures (such as 2D or 3D protein and molecule structures) and providing …
biomedical structures (such as 2D or 3D protein and molecule structures) and providing …
Graphein-a python library for geometric deep learning and network analysis on biomolecular structures and interaction networks
Geometric deep learning has broad applications in biology, a domain where relational
structure in data is often intrinsic to modelling the underlying phenomena. Currently, efforts …
structure in data is often intrinsic to modelling the underlying phenomena. Currently, efforts …
Overview of methods for characterization and visualization of a protein–protein interaction network in a multi-omics integration context
V Robin, A Bodein, MP Scott-Boyer… - Frontiers in Molecular …, 2022 - frontiersin.org
At the heart of the cellular machinery through the regulation of cellular functions, protein–
protein interactions (PPIs) have a significant role. PPIs can be analyzed with network …
protein interactions (PPIs) have a significant role. PPIs can be analyzed with network …
Exploiting hierarchical interactions for protein surface learning
Predicting interactions between proteins is one of the most important yet challenging
problems in structural bioinformatics. Intrinsically, potential function sites in protein surfaces …
problems in structural bioinformatics. Intrinsically, potential function sites in protein surfaces …