Consensus scoring for ligand/protein interactions

Clark, Robert D.; Strizhev, Alexander; Leonard, Joseph M.; Blake, James F.; Matthew, James B.

doi:10.1016/s1093-3263(01)00125-5

Cited by 393 publications

(364 citation statements)

References 40 publications

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“…The use of data fusion for VS is shown in Box 1, where a user-defined reference structure is searched against a database using several different similarity measures, an approach we refer to as similarity fusion; an analogous approach, called consensus scoring, can be used to combine the results of different search algorithms and/or scoring functions for ligand-protein docking [63,64]. Typical datafusion rules include the maximum, the minimum and the sum of the rank positions, P(I,J), allocated to each database-molecule J by each of the similarity measures; in our experiments, we have found that the sum of the rank positions normally gives the best results.…”

Section: Combination Of Rankings Using Similarity Fusionmentioning

confidence: 99%

Similarity-based virtual screening using 2D fingerprints

Willett

2006

Drug Discovery Today

783

673

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Teaser This paper discusses the use of binary-encoded fragment substructures to scan databases to find molecules that are structurally similar to a bioactive query compound.Abstract This paper summarises recent work at the University of Sheffield on virtual screening methods that use 2D fingerprint measures of structural similarity. A detailed comparison of a large number of similarity coefficients demonstrates that the well-known Tanimoto coefficient remains the method of choice for the computation of fingerprintbased similarity, despite possessing some inherent biases related to the sizes of the molecules that are being sought. Group fusion involves combining the results of similarity searches based on multiple reference structures and a single similarity measure. We demonstrate the effectiveness of this approach to screening, and also describe an approximate form of group fusion, turbo similarity searching, that can be used when just a single reference structure is available.

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Section: Combination Of Rankings Using Similarity Fusionmentioning

confidence: 99%

Similarity-based virtual screening using 2D fingerprints

Willett

2006

Drug Discovery Today

783

673

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“…In CScore, the range of scores for each scoring function are determined, above these the cutoff threshold are considered "good", and the consensus score is the sum of the number of "good" results for each ligand in each scoring function. A publication by Tripos scientists indicates the reliability of molecular docking can be improved by combining results from functions in CScore [133]. In 2002, Paul and Rogan proposed a consensus docking approach (ConsDock) that takes advantage of three widely used docking tools (Dock, FlexX, and Gold) [134].…”

Section: Scoring Functionsmentioning

confidence: 99%

Recent Development and Application of Virtual Screening in Drug Discovery: An Overview

Hou¹,

Xu²

2012

Frontiers in Medicinal Chemistry - (Volume 3)

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Virtual screening, especially the structure-based virtual screening, has emerged as a reliable, cost-effective and time-saving technique for the discovery of lead compounds. Here, the basic ideas and computational tools for virtual screening have been briefly introduced, and emphasis is placed on aspects of recent development of docking-based virtual screening, scoring functions in molecular docking and ADME/Tox-based virtual screening in the past three years (2000 to 2003). Moreover, successful examples are provided to further demonstrate the effectiveness of virtual screening in drug discovery.

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“…35 CScore is the counter of good results for each ligand in each scoring functionF_score, 32 G_score, 36 PMF score, 37 D_score 38 and Chem score. 39 In each run, 50 poses were generated if possible and the poses were classified into two groups (see Table 2).…”

Section: Computational Approachmentioning

confidence: 99%

Docking Studies on Formylchromone Derivatives as Protein Tyrosine Phosphatase 1B (PTP1B) Inhibitors

Kim

Lee²,

Zhang³

et al. 2007

Bulletin of the Korean Chemical Society

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Molecular modeling study has been performed to assist in the design of PTP1B inhibitors using FlexX. FlexX dockings with 19 test ligands, whose structures have been determined by X-ray crystallography, were successful in reproducing the experimental conformations within the protein. An increase in biological activity is observed as hydrophobic character of formylchromone derivatives increases. Most ligands bind to the activesite regions of the protein successfully in two different score runs. The Drug score run gave better results than the FlexX score run based on the score, rank, binding modes and bond distance of docked structures. Consensus values from the CScore scoring function are between 3 and 5, suggesting that the scoring scheme is reliable. All formylchromone inhibitors considered in this work show unidirectional binding modes in the active site pocket, which is contrary to the bidirectional X-ray results by Malamas et al. and amino acid residues responsible for such orientation are identified to help further development of the inhibitors.

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Consensus scoring for ligand/protein interactions

Cited by 393 publications

References 40 publications

Similarity-based virtual screening using 2D fingerprints

Similarity-based virtual screening using 2D fingerprints

Recent Development and Application of Virtual Screening in Drug Discovery: An Overview

Docking Studies on Formylchromone Derivatives as Protein Tyrosine Phosphatase 1B (PTP1B) Inhibitors

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