Structure-based drug design with geometric deep learning
Abstract Structure-based drug design uses three-dimensional geometric information of
macromolecules, such as proteins or nucleic acids, to identify suitable ligands. Geometric …
macromolecules, such as proteins or nucleic acids, to identify suitable ligands. Geometric …
Equibind: Geometric deep learning for drug binding structure prediction
Predicting how a drug-like molecule binds to a specific protein target is a core problem in
drug discovery. An extremely fast computational binding method would enable key …
drug discovery. An extremely fast computational binding method would enable key …
Geometric deep learning of RNA structure
RNA molecules adopt three-dimensional structures that are critical to their function and of
interest in drug discovery. Few RNA structures are known, however, and predicting them …
interest in drug discovery. Few RNA structures are known, however, and predicting them …
Learning from protein structure with geometric vector perceptrons
Learning on 3D structures of large biomolecules is emerging as a distinct area in machine
learning, but there has yet to emerge a unifying network architecture that simultaneously …
learning, but there has yet to emerge a unifying network architecture that simultaneously …
Protein design with deep learning
M Defresne, S Barbe, T Schiex - International Journal of Molecular …, 2021 - mdpi.com
Computational Protein Design (CPD) has produced impressive results for engineering new
proteins, resulting in a wide variety of applications. In the past few years, various efforts have …
proteins, resulting in a wide variety of applications. In the past few years, various efforts have …
Independent se (3)-equivariant models for end-to-end rigid protein docking
Protein complex formation is a central problem in biology, being involved in most of the cell's
processes, and essential for applications, eg drug design or protein engineering. We tackle …
processes, and essential for applications, eg drug design or protein engineering. We tackle …
PeSTo: parameter-free geometric deep learning for accurate prediction of protein binding interfaces
LF Krapp, LA Abriata, F Cortés Rodriguez… - Nature …, 2023 - nature.com
Proteins are essential molecular building blocks of life, responsible for most biological
functions as a result of their specific molecular interactions. However, predicting their …
functions as a result of their specific molecular interactions. However, predicting their …
Geometrically equivariant graph neural networks: A survey
Many scientific problems require to process data in the form of geometric graphs. Unlike
generic graph data, geometric graphs exhibit symmetries of translations, rotations, and/or …
generic graph data, geometric graphs exhibit symmetries of translations, rotations, and/or …
Antibody-antigen docking and design via hierarchical structure refinement
Computational antibody design seeks to automatically create an antibody that binds to an
antigen. The binding affinity is governed by the 3D binding interface where antibody …
antigen. The binding affinity is governed by the 3D binding interface where antibody …
DeepRank: a deep learning framework for data mining 3D protein-protein interfaces
Abstract Three-dimensional (3D) structures of protein complexes provide fundamental
information to decipher biological processes at the molecular scale. The vast amount of …
information to decipher biological processes at the molecular scale. The vast amount of …