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Journal ArticleDOI

Latest developments in molecular docking: 2010-2011 in review.

TLDR
The main developments in docking in this period, covered in this review, are receptor flexibility, solvation, fragment docking, postprocessing, docking into homology models, and docking comparisons.
Abstract
The aim of docking is to accurately predict the structure of a ligand within the constraints of a receptor binding site and to correctly estimate the strength of binding. We discuss, in detail, methodological developments that occurred in the docking field in 2010 and 2011, with a particular focus on the more difficult, and sometimes controversial, aspects of this promising computational discipline. The main developments in docking in this period, covered in this review, are receptor flexibility, solvation, fragment docking, postprocessing, docking into homology models, and docking comparisons. Several new, or at least newly invigorated, advances occurred in areas such as nonlinear scoring functions, using machine-learning approaches. This review is strongly focused on docking advances in the context of drug design, specifically in virtual screening and fragment-based drug design. Where appropriate, we refer readers to exemplar case studies.

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Citations
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Journal ArticleDOI

PLIP: fully automated protein–ligand interaction profiler

TL;DR: The protein-ligand interaction profiler (PLIP) is presented, a novel web service for fully automated detection and visualization of relevant non-covalent protein–ligand contacts in 3D structures, freely available at projects.tu-dresden.de/plip-web.
Journal ArticleDOI

Comprehensive evaluation of ten docking programs on a diverse set of protein-ligand complexes: the prediction accuracy of sampling power and scoring power.

TL;DR: Overall, the ligand binding poses could be identified in most cases by the evaluated docking programs but the ranks of the binding affinities for the entire dataset could not be well predicted by most docking programs.
Journal ArticleDOI

DOCK 6: Impact of new features and current docking performance

TL;DR: This manuscript presents the latest algorithmic and methodological developments to the structure‐based design program DOCK 6.7 focused on an updated internal energy function, new anchor selection control, enhanced minimization options, a footprint similarity scoring function, a symmetry‐corrected root‐mean‐square deviation algorithm, a database filter, and docking forensic tools.
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Receptor–ligand molecular docking

TL;DR: The main topics and recent computational and methodological advances in protein–ligand docking are summarised, including protein flexibility, multiple ligand binding modes and the free-energy landscape profile for binding affinity prediction.
Journal ArticleDOI

Oncogenic protein interfaces: small molecules, big challenges

TL;DR: Some of the latest techniques to discover modulators of protein–protein interactions are described and how current drug discovery approaches have been adapted to successfully target these interfaces are described.
References
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Journal ArticleDOI

Structure-Based Discovery of A2A Adenosine Receptor Ligands

TL;DR: In this paper, the A2A adenosine receptor signals in the periphery and the CNS, with agonists explored as anti-inflammatory drugs and antagonists explored for neurodegenerative diseases.
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Crystal structure-based virtual screening for novel fragment-like ligands of the human histamine H1 receptor

TL;DR: The optimized in silico screening approach was successfully applied to identify a chemically diverse set of novel fragment-like H(1)R ligands with an exceptionally high hit rate, 73%.
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A Machine Learning-Based Method To Improve Docking Scoring Functions and Its Application to Drug Repurposing

TL;DR: This paper shows how the use of support vector machines (SVMs), trained by associating sets of individual energy terms retrieved from molecular docking with the known binding affinity of each compound from high-throughput screening experiments, can be used to improve the correlation between known binding affinities and those predicted by the docking program eHiTS.
Journal ArticleDOI

NNScore: a neural-network-based scoring function for the characterization of protein-ligand complexes.

TL;DR: A scoring function based on a neural network, a computational model that attempts to simulate, albeit inadequately, the microscopic organization of the brain, could prove useful in future drug-discovery efforts.
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