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 …
A fully differentiable ligand pose optimization framework guided by deep learning and a traditional scoring function
The recently reported machine learning-or deep learning-based scoring functions (SFs)
have shown exciting performance in predicting protein–ligand binding affinities with fruitful …
have shown exciting performance in predicting protein–ligand binding affinities with fruitful …
Predicting protein–ligand docking structure with graph neural network
Modern day drug discovery is extremely expensive and time consuming. Although
computational approaches help accelerate and decrease the cost of drug discovery, existing …
computational approaches help accelerate and decrease the cost of drug discovery, existing …
DeepBSP—a machine learning method for accurate prediction of protein–ligand docking structures
J Bao, X He, JZH Zhang - Journal of chemical information and …, 2021 - ACS Publications
In recent years, machine-learning-based scoring functions have significantly improved the
scoring power. However, many of these methods do not perform well in distinguishing the …
scoring power. However, many of these methods do not perform well in distinguishing the …
Accuracy or novelty: what can we gain from target-specific machine-learning-based scoring functions in virtual screening?
Abstract Machine-learning (ML)-based scoring functions (MLSFs) have gradually emerged
as a promising alternative for protein–ligand binding affinity prediction and structure-based …
as a promising alternative for protein–ligand binding affinity prediction and structure-based …
A generalized protein–ligand scoring framework with balanced scoring, docking, ranking and screening powers
Applying machine learning algorithms to protein–ligand scoring functions has aroused
widespread attention in recent years due to the high predictive accuracy and affordable …
widespread attention in recent years due to the high predictive accuracy and affordable …
Delta machine learning to improve scoring-ranking-screening performances of protein–ligand scoring functions
Protein–ligand scoring functions are widely used in structure-based drug design for fast
evaluation of protein–ligand interactions, and it is of strong interest to develop scoring …
evaluation of protein–ligand interactions, and it is of strong interest to develop scoring …
OnionNet-2: a convolutional neural network model for predicting protein-ligand binding affinity based on residue-atom contacting shells
One key task in virtual screening is to accurately predict the binding affinity (△ G) of protein-
ligand complexes. Recently, deep learning (DL) has significantly increased the predicting …
ligand complexes. Recently, deep learning (DL) has significantly increased the predicting …
Improving docking-based virtual screening ability by integrating multiple energy auxiliary terms from molecular docking scoring
Virtual Screening (VS) based on molecular docking is an efficient method used for retrieving
novel hit compounds in drug discovery. However, the accuracy of the current docking …
novel hit compounds in drug discovery. However, the accuracy of the current docking …
D3R Grand Challenge 2: blind prediction of protein–ligand poses, affinity rankings, and relative binding free energies
Z Gaieb, S Liu, S Gathiaka, M Chiu, H Yang… - Journal of computer …, 2018 - Springer
Abstract The Drug Design Data Resource (D3R) ran Grand Challenge 2 (GC2) from
September 2016 through February 2017. This challenge was based on a dataset of …
September 2016 through February 2017. This challenge was based on a dataset of …