Dock around the Clock – Current Status of Small Molecule Docking and Scoring

Rester, Ulrich

doi:10.1002/qsar.200510183

Cited by 39 publications

(29 citation statements)

References 146 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…11,12 Usually, the receptor is treated as a rigid molecule because of high computational costs, whereas conformational flexibility of ligands is taken into account leading to different placement algorithms. 13 The scoring procedure of such docked conformers is still regarded as one of the most difficult tasks in molecular docking because of their empirical nature. In our work, we used only software that considers flexibility of ligands, not proteins, and thus structure of protein before docking was not changed in comparison with original pdb file, assuring that protein is already in bounded state.…”

Section: Introductionmentioning

confidence: 99%

VoteDock: Consensus docking method for prediction of protein–ligand interactions

et al. 2010

View full text Add to dashboard Cite

Molecular recognition plays a fundamental role in all biological processes, and that is why great efforts have been made to understand and predict protein–ligand interactions. Finding a molecule that can potentially bind to a target protein is particularly essential in drug discovery and still remains an expensive and time-consuming task. In silico, tools are frequently used to screen molecular libraries to identify new lead compounds, and if protein structure is known, various protein–ligand docking programs can be used. The aim of docking procedure is to predict correct poses of ligand in the binding site of the protein as well as to score them according to the strength of interaction in a reasonable time frame. The purpose of our studies was to present the novel consensus approach to predict both protein–ligand complex structure and its corresponding binding affinity. Our method used as the input the results from seven docking programs (Surflex, LigandFit, Glide, GOLD, FlexX, eHiTS, and AutoDock) that are widely used for docking of ligands. We evaluated it on the extensive benchmark dataset of 1300 protein–ligands pairs from refined PDBbind database for which the structural and affinity data was available. We compared independently its ability of proper scoring and posing to the previously proposed methods. In most cases, our method is able to dock properly approximately 20% of pairs more than docking methods on average, and over 10% of pairs more than the best single program. The RMSD value of the predicted complex conformation versus its native one is reduced by a factor of 0.5 Å. Finally, we were able to increase the Pearson correlation of the predicted binding affinity in comparison with the experimental value up to 0.5.

show abstract

Section: Introductionmentioning

confidence: 99%

VoteDock: Consensus docking method for prediction of protein–ligand interactions

et al. 2010

View full text Add to dashboard Cite

show abstract

“…The docking problem can be subdivided into two steps [50][51][52][53]. Docking has been acknowledged with significant attention among all the virtual screening methods.…”

Section: Dockingmentioning

confidence: 99%

Virtual Screening in Drug Discovery - A Computational Perspective

Reddy

Pati²,

Kumar³

et al. 2007

CPPS

281

View full text Add to dashboard Cite

Virtual screening emerged as an important tool in our quest to access novel drug like compounds. There are a wide range of comparable and contrasting methodological protocols available in screening databases for the lead compounds. The number of methods and software packages which employ the target and ligand based virtual screening are increasing at a rapid pace. However, the general understanding on the applicability and limitations of these methodologies is not emerging as fast as the developments of various methods. Therefore, it is extremely important to compare and contrast various protocols with practical examples to gauge the strength and applicability of various methods. The review provides a comprehensive appraisal on several of the available virtual screening methods to-date. Recent developments of the docking and similarity based methods have been discussed besides the descriptor selection and pharmacophore based searching. The review touches upon the application of statistical, graph theory based methods machine learning tools in virtual screening and combinatorial library design. Finally, several case studies are undertaken where the virtual screening technology has been applied successfully. A critical analysis of these case studies provides a good platform to estimate the applicability of various virtual screening methods in the new lead identification and optimization.

show abstract

“…The reader is referred to an excellent review on this topic [115] (see also Table 1.9 ). It involves generating rules from a training set of molecules based on their structures and known biological activities.…”

Section: Combinatorial Chemistry and Sarmentioning

confidence: 99%