[HTML][HTML] 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 …
Multimodal learning with graphs
Artificial intelligence for graphs has achieved remarkable success in modelling complex
systems, ranging from dynamic networks in biology to interacting particle systems in physics …
systems, ranging from dynamic networks in biology to interacting particle systems in physics …
Diffdock: Diffusion steps, twists, and turns for molecular docking
Predicting the binding structure of a small molecule ligand to a protein--a task known as
molecular docking--is critical to drug design. Recent deep learning methods that treat …
molecular docking--is critical to drug design. Recent deep learning methods that treat …
Tankbind: Trigonometry-aware neural networks for drug-protein binding structure prediction
Illuminating interactions between proteins and small drug molecules is a long-standing
challenge in the field of drug discovery. Despite the importance of understanding these …
challenge in the field of drug discovery. Despite the importance of understanding these …
Interactiongraphnet: A novel and efficient deep graph representation learning framework for accurate protein–ligand interaction predictions
Accurate quantification of protein–ligand interactions remains a key challenge to structure-
based drug design. However, traditional machine learning (ML)-based methods based on …
based drug design. However, traditional machine learning (ML)-based methods based on …
Boosting protein–ligand binding pose prediction and virtual screening based on residue–atom distance likelihood potential and graph transformer
The past few years have witnessed enormous progress toward applying machine learning
approaches to the development of protein–ligand scoring functions. However, the robust …
approaches to the development of protein–ligand scoring functions. However, the robust …
ResGen is a pocket-aware 3D molecular generation model based on parallel multiscale modelling
Most molecular generative models based on artificial intelligence for de novo drug design
are ligand-centric and do not consider the detailed three-dimensional geometries of protein …
are ligand-centric and do not consider the detailed three-dimensional geometries of protein …
A Universal and Accurate Method for Easily Identifying Components in Raman Spectroscopy Based on Deep Learning
X Fan, Y Wang, C Yu, Y Lv, H Zhang, Q Yang… - Analytical …, 2023 - ACS Publications
Raman spectroscopy has been widely used to provide the structural fingerprint for molecular
identification. Due to interference from coexisting components, noise, baseline, and …
identification. Due to interference from coexisting components, noise, baseline, and …
Geometric interaction graph neural network for predicting protein–ligand binding affinities from 3d structures (gign)
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 …
Recent advances have shown great potential in applying machine learning (ML) for PLA …
Graph neural networks
Graphs are flexible mathematical objects that can represent many entities and knowledge
from different domains, including in the life sciences. Graph neural networks (GNNs) are …
from different domains, including in the life sciences. Graph neural networks (GNNs) are …