Machine‐learning scoring functions to improve structure‐based binding affinity prediction and virtual screening
QU Ain, A Aleksandrova, FD Roessler… - Wiley Interdisciplinary …, 2015 - Wiley Online Library
Docking tools to predict whether and how a small molecule binds to a target can be applied
if a structural model of such target is available. The reliability of docking depends, however …
if a structural model of such target is available. The reliability of docking depends, however …
Does a more precise chemical description of protein–ligand complexes lead to more accurate prediction of binding affinity?
PJ Ballester, A Schreyer… - Journal of chemical …, 2014 - ACS Publications
Predicting the binding affinities of large sets of diverse molecules against a range of
macromolecular targets is an extremely challenging task. The scoring functions that attempt …
macromolecular targets is an extremely challenging task. The scoring functions that attempt …
Improving AutoDock Vina using random forest: the growing accuracy of binding affinity prediction by the effective exploitation of larger data sets
There is a growing body of evidence showing that machine learning regression results in
more accurate structure‐based prediction of protein‐ligand binding affinity. Docking …
more accurate structure‐based prediction of protein‐ligand binding affinity. Docking …
Performance of machine-learning scoring functions in structure-based virtual screening
Classical scoring functions have reached a plateau in their performance in virtual screening
and binding affinity prediction. Recently, machine-learning scoring functions trained on …
and binding affinity prediction. Recently, machine-learning scoring functions trained on …
Task-specific scoring functions for predicting ligand binding poses and affinity and for screening enrichment
HM Ashtawy, NR Mahapatra - Journal of chemical information and …, 2018 - ACS Publications
Molecular docking, scoring, and virtual screening play an increasingly important role in
computer-aided drug discovery. Scoring functions (SFs) are typically employed to predict the …
computer-aided drug discovery. Scoring functions (SFs) are typically employed to predict the …
Low-quality structural and interaction data improves binding affinity prediction via random forest
Docking scoring functions can be used to predict the strength of protein-ligand binding. It is
widely believed that training a scoring function with low-quality data is detrimental for its …
widely believed that training a scoring function with low-quality data is detrimental for its …
Statistical potentials and scoring functions applied to protein–ligand binding
In virtual screening, small-molecule ligands are docked into protein binding sites and their
binding affinity is predicted. Knowledge-based, regression-based and first-principle-based …
binding affinity is predicted. Knowledge-based, regression-based and first-principle-based …
Further development and validation of empirical scoring functions for structure-based binding affinity prediction
New empirical scoring functions have been developed to estimate the binding affinity of a
given protein-ligand complex with known three-dimensional structure. These scoring …
given protein-ligand complex with known three-dimensional structure. These scoring …
From machine learning to deep learning: Advances in scoring functions for protein–ligand docking
Molecule docking has been regarded as a routine tool for drug discovery, but its accuracy
highly depends on the reliability of scoring functions (SFs). With the rapid development of …
highly depends on the reliability of scoring functions (SFs). With the rapid development of …
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 …
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