Literature DB >> 24708446

Comparative assessment of scoring functions on an updated benchmark: 2. Evaluation methods and general results.

Yan Li1, Li Han, Zhihai Liu, Renxiao Wang.   

Abstract

Our comparative assessment of scoring functions (CASF) benchmark is created to provide an objective evaluation of current scoring functions. The key idea of CASF is to compare the general performance of scoring functions on a diverse set of protein-ligand complexes. In order to avoid testing scoring functions in the context of molecular docking, the scoring process is separated from the docking (or sampling) process by using ensembles of ligand binding poses that are generated in prior. Here, we describe the technical methods and evaluation results of the latest CASF-2013 study. The PDBbind core set (version 2013) was employed as the primary test set in this study, which consists of 195 protein-ligand complexes with high-quality three-dimensional structures and reliable binding constants. A panel of 20 scoring functions, most of which are implemented in main-stream commercial software, were evaluated in terms of "scoring power" (binding affinity prediction), "ranking power" (relative ranking prediction), "docking power" (binding pose prediction), and "screening power" (discrimination of true binders from random molecules). Our results reveal that the performance of these scoring functions is generally more promising in the docking/screening power tests than in the scoring/ranking power tests. Top-ranked scoring functions in the scoring power test, such as X-Score(HM), ChemScore@SYBYL, ChemPLP@GOLD, and PLP@DS, are also top-ranked in the ranking power test. Top-ranked scoring functions in the docking power test, such as ChemPLP@GOLD, Chemscore@GOLD, GlidScore-SP, LigScore@DS, and PLP@DS, are also top-ranked in the screening power test. Our results obtained on the entire test set and its subsets suggest that the real challenge in protein-ligand binding affinity prediction lies in polar interactions and associated desolvation effect. Nonadditive features observed among high-affinity protein-ligand complexes also need attention.

Mesh:

Substances:

Year:  2014        PMID: 24708446     DOI: 10.1021/ci500081m

Source DB:  PubMed          Journal:  J Chem Inf Model        ISSN: 1549-9596            Impact factor:   4.956


  82 in total

1.  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

2.  Exploring the stability of ligand binding modes to proteins by molecular dynamics simulations.

Authors:  Kai Liu; Etsurou Watanabe; Hironori Kokubo
Journal:  J Comput Aided Mol Des       Date:  2017-01-10       Impact factor: 3.686

3.  SPA-LN: a scoring function of ligand-nucleic acid interactions via optimizing both specificity and affinity.

Authors:  Zhiqiang Yan; Jin Wang
Journal:  Nucleic Acids Res       Date:  2017-07-07       Impact factor: 16.971

4.  GalaxyDock BP2 score: a hybrid scoring function for accurate protein-ligand docking.

Authors:  Minkyung Baek; Woong-Hee Shin; Hwan Won Chung; Chaok Seok
Journal:  J Comput Aided Mol Des       Date:  2017-06-16       Impact factor: 3.686

5.  Docking pose selection by interaction pattern graph similarity: application to the D3R grand challenge 2015.

Authors:  Inna Slynko; Franck Da Silva; Guillaume Bret; Didier Rognan
Journal:  J Comput Aided Mol Des       Date:  2016-08-01       Impact factor: 3.686

6.  Generative network complex (GNC) for drug discovery.

Authors:  Christopher Grow; Kaifu Gao; Duc Duy Nguyen; Guo-Wei Wei
Journal:  Commun Inf Syst       Date:  2019

7.  The Development of Target-Specific Pose Filter Ensembles To Boost Ligand Enrichment for Structure-Based Virtual Screening.

Authors:  Jie Xia; Jui-Hua Hsieh; Huabin Hu; Song Wu; Xiang Simon Wang
Journal:  J Chem Inf Model       Date:  2017-06-01       Impact factor: 4.956

Review 8.  A review of mathematical representations of biomolecular data.

Authors:  Duc Duy Nguyen; Zixuan Cang; Guo-Wei Wei
Journal:  Phys Chem Chem Phys       Date:  2020-02-26       Impact factor: 3.676

9.  Structure and ligand-binding mechanism of the human OX1 and OX2 orexin receptors.

Authors:  Jie Yin; Kerim Babaoglu; Chad A Brautigam; Lindsay Clark; Zhenhua Shao; Thomas H Scheuermann; Charles M Harrell; Anthony L Gotter; Anthony J Roecker; Christopher J Winrow; John J Renger; Paul J Coleman; Daniel M Rosenbaum
Journal:  Nat Struct Mol Biol       Date:  2016-03-07       Impact factor: 15.369

10.  Predicting the relative binding affinity of mineralocorticoid receptor antagonists by density functional methods.

Authors:  Katarina Roos; Anders Hogner; Derek Ogg; Martin J Packer; Eva Hansson; Kenneth L Granberg; Emma Evertsson; Anneli Nordqvist
Journal:  J Comput Aided Mol Des       Date:  2015-11-16       Impact factor: 3.686

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.