Artificial intelligence in the prediction of protein–ligand interactions: recent advances and future directions
New drug production, from target identification to marketing approval, takes over 12 years
and can cost around $2.6 billion. Furthermore, the COVID-19 pandemic has unveiled the …
and can cost around $2.6 billion. Furthermore, the COVID-19 pandemic has unveiled the …
[HTML][HTML] Scoring functions for protein-ligand binding affinity prediction using structure-based deep learning: A review
The rapid and accurate in silico prediction of protein-ligand binding free energies or binding
affinities has the potential to transform drug discovery. In recent years, several deep learning …
affinities has the potential to transform drug discovery. In recent years, several deep learning …
Generating 3D molecules conditional on receptor binding sites with deep generative models
The goal of structure-based drug discovery is to find small molecules that bind to a given
target protein. Deep learning has been used to generate drug-like molecules with certain …
target protein. Deep learning has been used to generate drug-like molecules with certain …
P2Rank: machine learning based tool for rapid and accurate prediction of ligand binding sites from protein structure
Background Ligand binding site prediction from protein structure has many applications
related to elucidation of protein function and structure based drug discovery. It often …
related to elucidation of protein function and structure based drug discovery. It often …
Three-dimensional convolutional neural networks and a cross-docked data set for structure-based drug design
PG Francoeur, T Masuda, J Sunseri, A Jia… - Journal of chemical …, 2020 - ACS Publications
One of the main challenges in drug discovery is predicting protein–ligand binding affinity.
Recently, machine learning approaches have made substantial progress on this task …
Recently, machine learning approaches have made substantial progress on this task …
Combining docking pose rank and structure with deep learning improves protein–ligand binding mode prediction over a baseline docking approach
We present a simple, modular graph-based convolutional neural network that takes
structural information from protein–ligand complexes as input to generate models for activity …
structural information from protein–ligand complexes as input to generate models for activity …
Learning protein-ligand binding affinity with atomic environment vectors
Scoring functions for the prediction of protein-ligand binding affinity have seen renewed
interest in recent years when novel machine learning and deep learning methods started to …
interest in recent years when novel machine learning and deep learning methods started to …
Visualizing convolutional neural network protein-ligand scoring
Protein-ligand scoring is an important step in a structure-based drug design pipeline.
Selecting a correct binding pose and predicting the binding affinity of a protein-ligand …
Selecting a correct binding pose and predicting the binding affinity of a protein-ligand …
A consistent scheme for gradient-based optimization of protein–ligand poses
F Flachsenberg, A Meyder, K Sommer… - Journal of Chemical …, 2020 - ACS Publications
Scoring and numerical optimization of protein–ligand poses is an integral part of docking
tools. Although many scoring functions exist, many of them are not continuously …
tools. Although many scoring functions exist, many of them are not continuously …