Key topics in molecular docking for drug design
PHM Torres, ACR Sodero, P Jofily… - International journal of …, 2019 - mdpi.com
Molecular docking has been widely employed as a fast and inexpensive technique in the
past decades, both in academic and industrial settings. Although this discipline has now had …
past decades, both in academic and industrial settings. Although this discipline has now had …
Structure-based virtual screening for ligands of G protein–coupled receptors: what can molecular docking do for you?
F Ballante, AJ Kooistra, S Kampen, C de Graaf… - Pharmacological …, 2021 - ASPET
G protein–coupled receptors (GPCRs) constitute the largest family of membrane proteins in
the human genome and are important therapeutic targets. During the last decade, the …
the human genome and are important therapeutic targets. During the last decade, the …
GNINA 1.0: molecular docking with deep learning
Molecular docking computationally predicts the conformation of a small molecule when
binding to a receptor. Scoring functions are a vital piece of any molecular docking pipeline …
binding to a receptor. Scoring functions are a vital piece of any molecular docking pipeline …
Comparative assessment of scoring functions: the CASF-2016 update
In structure-based drug design, scoring functions are often employed to evaluate protein–
ligand interactions. A variety of scoring functions have been developed so far, and thus …
ligand interactions. A variety of scoring functions have been developed so far, and thus …
Structure-aware interactive graph neural networks for the prediction of protein-ligand binding affinity
Drug discovery often relies on the successful prediction of protein-ligand binding affinity.
Recent advances have shown great promise in applying graph neural networks (GNNs) for …
Recent advances have shown great promise in applying graph neural networks (GNNs) for …
Computational evaluation of major components from plant essential oils as potent inhibitors of SARS-CoV-2 spike protein
Abstract COVID-19, caused by SARS-CoV-2 has recently emerged as a global pandemic.
Intense efforts are ongoing to find a vaccine or a drug to control the disease across the …
Intense efforts are ongoing to find a vaccine or a drug to control the disease across the …
DeepAffinity: interpretable deep learning of compound–protein affinity through unified recurrent and convolutional neural networks
Motivation Drug discovery demands rapid quantification of compound–protein interaction
(CPI). However, there is a lack of methods that can predict compound–protein affinity from …
(CPI). However, there is a lack of methods that can predict compound–protein affinity from …
Comprehensive evaluation of ten docking programs on a diverse set of protein–ligand complexes: the prediction accuracy of sampling power and scoring power
Z Wang, H Sun, X Yao, D Li, L Xu, Y Li… - Physical Chemistry …, 2016 - pubs.rsc.org
As one of the most popular computational approaches in modern structure-based drug
design, molecular docking can be used not only to identify the correct conformation of a …
design, molecular docking can be used not only to identify the correct conformation of a …
Evaluation of AutoDock and AutoDock Vina on the CASF-2013 benchmark
T Gaillard - Journal of chemical information and modeling, 2018 - ACS Publications
Computer-aided protein–ligand binding predictions are a valuable help in drug discovery.
Protein–ligand docking programs generally consist of two main components: a scoring …
Protein–ligand docking programs generally consist of two main components: a scoring …
AI-based protein structure prediction in drug discovery: impacts and challenges
M Schauperl, RA Denny - Journal of Chemical Information and …, 2022 - ACS Publications
Proteins are the molecular machinery of the human body, and their malfunctioning is often
responsible for diseases, making them crucial targets for drug discovery. The three …
responsible for diseases, making them crucial targets for drug discovery. The three …