Literature DB >> 20730182

Scoring functions and their evaluation methods for protein-ligand docking: recent advances and future directions.

Sheng-You Huang1, Sam Z Grinter, Xiaoqin Zou.   

Abstract

The scoring function is one of the most important components in structure-based drug design. Despite considerable success, accurate and rapid prediction of protein-ligand interactions is still a challenge in molecular docking. In this perspective, we have reviewed three basic types of scoring functions (force-field, empirical, and knowledge-based) and the consensus scoring technique that are used for protein-ligand docking. The commonly-used assessment criteria and publicly available protein-ligand databases for performance evaluation of the scoring functions have also been presented and discussed. We end with a discussion of the challenges faced by existing scoring functions and possible future directions for developing improved scoring functions.

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Year:  2010        PMID: 20730182     DOI: 10.1039/c0cp00151a

Source DB:  PubMed          Journal:  Phys Chem Chem Phys        ISSN: 1463-9076            Impact factor:   3.676


  107 in total

1.  Are predefined decoy sets of ligand poses able to quantify scoring function accuracy?

Authors:  Oliver Korb; Tim Ten Brink; Fredrick Robin Devadoss Victor Paul Raj; Matthias Keil; Thomas E Exner
Journal:  J Comput Aided Mol Des       Date:  2012-01-10       Impact factor: 3.686

2.  Locating binding poses in protein-ligand systems using reconnaissance metadynamics.

Authors:  Pär Söderhjelm; Gareth A Tribello; Michele Parrinello
Journal:  Proc Natl Acad Sci U S A       Date:  2012-03-21       Impact factor: 11.205

3.  Scoring and lessons learned with the CSAR benchmark using an improved iterative knowledge-based scoring function.

Authors:  Sheng-You Huang; Xiaoqin Zou
Journal:  J Chem Inf Model       Date:  2011-08-31       Impact factor: 4.956

4.  Incorporating specificity into optimization: evaluation of SPA using CSAR 2014 and CASF 2013 benchmarks.

Authors:  Zhiqiang Yan; Jin Wang
Journal:  J Comput Aided Mol Des       Date:  2016-02-15       Impact factor: 3.686

5.  Construction and test of ligand decoy sets using MDock: community structure-activity resource benchmarks for binding mode prediction.

Authors:  Sheng-You Huang; Xiaoqin Zou
Journal:  J Chem Inf Model       Date:  2011-08-03       Impact factor: 4.956

6.  Structure-based ensemble-QSAR model: a novel approach to the study of the EGFR tyrosine kinase and its inhibitors.

Authors:  Xian-qiang Sun; Lei Chen; Yao-zong Li; Wei-hua Li; Gui-xia Liu; Yao-quan Tu; Yun Tang
Journal:  Acta Pharmacol Sin       Date:  2013-12-16       Impact factor: 6.150

7.  Iterative Knowledge-Based Scoring Functions Derived from Rigid and Flexible Decoy Structures: Evaluation with the 2013 and 2014 CSAR Benchmarks.

Authors:  Chengfei Yan; Sam Z Grinter; Benjamin Ryan Merideth; Zhiwei Ma; Xiaoqin Zou
Journal:  J Chem Inf Model       Date:  2015-10-01       Impact factor: 4.956

8.  Benchmarking methods and data sets for ligand enrichment assessment in virtual screening.

Authors:  Jie Xia; Ermias Lemma Tilahun; Terry-Elinor Reid; Liangren Zhang; Xiang Simon Wang
Journal:  Methods       Date:  2014-12-03       Impact factor: 3.608

9.  Docking challenge: protein sampling and molecular docking performance.

Authors:  Khaled M Elokely; Robert J Doerksen
Journal:  J Chem Inf Model       Date:  2013-04-15       Impact factor: 4.956

10.  Neural-Network Scoring Functions Identify Structurally Novel Estrogen-Receptor Ligands.

Authors:  Jacob D Durrant; Kathryn E Carlson; Teresa A Martin; Tavina L Offutt; Christopher G Mayne; John A Katzenellenbogen; Rommie E Amaro
Journal:  J Chem Inf Model       Date:  2015-09-04       Impact factor: 4.956

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