Literature DB >> 24716849

Comparative assessment of scoring functions on an updated benchmark: 1. Compilation of the test set.

Yan Li1, Zhihai Liu, Jie Li, Li Han, Jie Liu, Zhixiong Zhao, Renxiao Wang.   

Abstract

Scoring functions are often applied in combination with molecular docking methods to predict ligand binding poses and ligand binding affinities or to identify active compounds through virtual screening. An objective benchmark for assessing the performance of current scoring functions is expected to provide practical guidance for the users to make smart choices among available methods. It can also elucidate the common weakness in current methods for future improvements. The primary goal of our comparative assessment of scoring functions (CASF) project is to provide a high-standard, publicly accessible benchmark of this type. Our latest study, i.e., CASF-2013, evaluated 20 popular scoring functions on an updated set of protein-ligand complexes. This data set was selected out of 8302 protein-ligand complexes recorded in the PDBbind database (version 2013) through a fairly complicated process. Sample selection was made by considering the quality of complex structures as well as binding data. Finally, qualified complexes were clustered by 90% similarity in protein sequences. Three representative complexes were chosen from each cluster to control sample redundancy. The final outcome, namely, the PDBbind core set (version 2013), consists of 195 protein-ligand complexes in 65 clusters with binding constants spanning nearly 10 orders of magnitude. In this data set, 82% of the ligand molecules are "druglike" and 78% of the protein molecules are validated or potential drug targets. Correlation between binding constants and several key properties of ligands are discussed. Methods and results of the scoring function evaluation will be described in a companion work in this issue (doi: 10.1021/ci500081m ).

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Year:  2014        PMID: 24716849     DOI: 10.1021/ci500080q

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


  42 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.  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

4.  Improving scoring-docking-screening powers of protein-ligand scoring functions using random forest.

Authors:  Cheng Wang; Yingkai Zhang
Journal:  J Comput Chem       Date:  2016-11-17       Impact factor: 3.376

5.  Workflows and performances in the ranking prediction of 2016 D3R Grand Challenge 2: lessons learned from a collaborative effort.

Authors:  Ying-Duo Gao; Yuan Hu; Alejandro Crespo; Deping Wang; Kira A Armacost; James I Fells; Xavier Fradera; Hongwu Wang; Huijun Wang; Brad Sherborne; Andreas Verras; Zhengwei Peng
Journal:  J Comput Aided Mol Des       Date:  2017-10-06       Impact factor: 3.686

6.  Convex-PL: a novel knowledge-based potential for protein-ligand interactions deduced from structural databases using convex optimization.

Authors:  Maria Kadukova; Sergei Grudinin
Journal:  J Comput Aided Mol Des       Date:  2017-09-18       Impact factor: 3.686

7.  Assessing protein-ligand interaction scoring functions with the CASF-2013 benchmark.

Authors:  Yan Li; Minyi Su; Zhihai Liu; Jie Li; Jie Liu; Li Han; Renxiao Wang
Journal:  Nat Protoc       Date:  2018-03-08       Impact factor: 13.491

8.  Assessing and improving the performance of consensus docking strategies using the DockBox package.

Authors:  Jordane Preto; Francesco Gentile
Journal:  J Comput Aided Mol Des       Date:  2019-10-01       Impact factor: 3.686

9.  Lessons Learned over Four Benchmark Exercises from the Community Structure-Activity Resource.

Authors:  Heather A Carlson
Journal:  J Chem Inf Model       Date:  2016-06-27       Impact factor: 4.956

10.  PL-PatchSurfer2: Improved Local Surface Matching-Based Virtual Screening Method That Is Tolerant to Target and Ligand Structure Variation.

Authors:  Woong-Hee Shin; Charles W Christoffer; Jibo Wang; Daisuke Kihara
Journal:  J Chem Inf Model       Date:  2016-08-19       Impact factor: 4.956

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