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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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Modellierung molekularer und makromolekularer Zustände im geleiteten strukturbasierten virtuellen Screening

TL;DR: In this article, the authors describe the RAISE (RApid Index-based Screening Engine) method for structure-based virtual screening, which is used in early stages of rational drug design.
Journal ArticleDOI

Assessing protein–ligand binding modes with computational tools: the case of PDE4B

TL;DR: It is shown that binding free energies obtained after a docking process by AutoDock are not accurate enough to reproduce this linear relationship and MM-GB/SA post-processing of molecular dynamics (MD) trajectories of the top ranked AutoD Dock pose improves the linear relationship.
Journal ArticleDOI

Fragment-centric topographic mapping method guides the understanding of ABCG2-inhibitor interactions

TL;DR: A fragment-centric topographic mapping method was employed to map out concave interaction pockets at the assigned protein region to screen the known inhibitors for this specific binding site and to guide the molecular docking pose selection as well as protein–ligand interaction analysis.
Journal ArticleDOI

Mapping oncogenic protein interactions for precision medicine

TL;DR: In this article , the authors discuss the important hurdles for characterising oncoPPIs and propose a three-phase multidisciplinary workflow to identify and characterise onco-PPIs.
References
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Journal ArticleDOI

AutoDock Vina: Improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading

TL;DR: AutoDock Vina achieves an approximately two orders of magnitude speed‐up compared with the molecular docking software previously developed in the lab, while also significantly improving the accuracy of the binding mode predictions, judging by tests on the training set used in AutoDock 4 development.
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Automated docking using a Lamarckian genetic algorithm and an empirical binding free energy function

TL;DR: It is shown that both the traditional and Lamarckian genetic algorithms can handle ligands with more degrees of freedom than the simulated annealing method used in earlier versions of AUTODOCK, and that the Lamarckia genetic algorithm is the most efficient, reliable, and successful of the three.
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Glide: a new approach for rapid, accurate docking and scoring. 1. Method and assessment of docking accuracy.

TL;DR: Glide approximates a complete systematic search of the conformational, orientational, and positional space of the docked ligand to find the best docked pose using a model energy function that combines empirical and force-field-based terms.
Journal ArticleDOI

Glide: a new approach for rapid, accurate docking and scoring. 2. Enrichment factors in database screening.

TL;DR: Comparisons to results for the thymidine kinase and estrogen receptors published by Rognan and co-workers show that Glide 2.5 performs better than GOLD 1.1, FlexX 1.8, or DOCK 4.01.
Journal ArticleDOI

A Fast Flexible Docking Method using an Incremental Construction Algorithm

TL;DR: This work presents an automatic method for docking organic ligands into protein binding sites that combines an appropriate model of the physico-chemical properties of the docked molecules with efficient methods for sampling the conformational space of the ligand.
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