Virtual Decoy Sets for Molecular Docking Benchmarks

Wallach, Izhar; Lilien, Ryan H.

doi:10.1021/ci100374f

Cited by 55 publications

(64 citation statements)

References 17 publications

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“…A great interest grows for datasets that include experimental data, however, retrieving such information in the scientific literature is still difficult, notably about inactive compounds since negative data are often not published. Another idea was to use virtual decoys that ignore synthetic feasibility to obtain compounds displaying physico-chemical properties that were more similar to the properties of the active compounds 91 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 …”

Section: Decoys Selectionmentioning

confidence: 99%

Benchmarking Data Sets for the Evaluation of Virtual Ligand Screening Methods: Review and Perspectives

Lagarde

Zagury

Montès

2015

J. Chem. Inf. Model.

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Virtual screening methods are commonly used nowadays in drug discovery processes. However, to ensure their reliability, they have to be carefully evaluated. The evaluation of these methods is often realized in a retrospective way, notably by studying the enrichment of benchmarking data sets. To this purpose, numerous benchmarking data sets were developed over the years, and the resulting improvements led to the availability of high quality benchmarking data sets. However, some points still have to be considered in the selection of the active compounds, decoys, and protein structures to obtain optimal benchmarking data sets.

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Section: Decoys Selectionmentioning

confidence: 99%

Benchmarking Data Sets for the Evaluation of Virtual Ligand Screening Methods: Review and Perspectives

Lagarde

Zagury

Montès

2015

J. Chem. Inf. Model.

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“…Multiple research groups have been building decoy sets to address the apparent weaknesses in DUD [62–64, 92, 94]. Good and Oprea generated WOrld of Molecular BioAcTivity (WOMBAT) data sets at the same time in order to expand the limited number of diverse ligands in “DUD clusters” [92].…”

Section: Currently Available Benchmarking Setsmentioning

confidence: 99%

“…the SBVS-specific and the LBVS-specific. Datasets such as Directory of Useful Decoys (DUD) [57] and its recent DUD-Enhanced (DUD-E) [58], virtual decoy sets (VDS) [62], G protein-coupled receptors (GPCRs) ligand library (GLL) and GPCRs Decoy Database (GDD) [63], Demanding Evaluation Kits for Objective in Silico Screening (DEKOIS) [64] and DEKOIS 2.0 [65], Nuclear Receptors Ligands and Structures Benchmarking DataBase (NRLiSt BDB) belong to SBVS-specific benchmarking sets. By contrast, only 3 datasets, i.e.…”

Section: Introductionmentioning

confidence: 99%

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

Xia

Tilahun

Reid

et al. 2015

Methods

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Retrospective small-scale virtual screening (VS) based on benchmarking data sets has been widely used to estimate ligand enrichments of VS approaches in the prospective (i.e. real-world) efforts. However, the intrinsic differences of benchmarking sets to the real screening chemical libraries can cause biased assessment. Herein, we summarize the history of benchmarking methods as well as data sets and highlight three main types of biases found in benchmarking sets, i.e. “analogue bias”, “artificial enrichment” and “false negative”. In addition, we introduced our recent algorithm to build maximum-unbiased benchmarking sets applicable to both ligand-based and structure-based VS approaches, and its implementations to three important human histone deacetylase (HDAC) isoforms, i.e. HDAC1, HDAC6 and HDAC8. The Leave-One-Out Cross-Validation (LOO CV) demonstrates that the benchmarking sets built by our algorithm are maximum-unbiased in terms of property matching, ROC curves and AUCs.

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“…They further clustered the remaining actives by their chemotype, or scaffold similarity. Their work, however, did not address the decoys set, but see Wallach and Lilien's for discussion about its composition [47]. Scaffold redundancy is particularly problematic when ligand-based methods are considered.…”

Section: Enrichment Tuningmentioning

confidence: 99%

Improving molecular docking through eHiTS’ tunable scoring function

Ravitz¹,

Zsoldos²,

Simon³

2011

J Comput Aided Mol Des

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We present three complementary approaches for score-tuning that improve docking performance in pose prediction, virtual screening and binding affinity assessment. The methodology utilizes experimental data to customize the scoring function for the system of interest considering the specific docking scenario. The tuning approach, which has been implemented as an automated utility in eHiTS, is introduced as a solution to one of the conundrums of the molecular docking paradigm, namely, the lack of a universally well performing scoring function. The accuracy of scoring functions has been shown to be generally system-dependent, and particularly lacking for binding energy and bio-activity predictions. In the proposed approach, pose and energy predictions are enhanced by adjusting the relative weights of the eHiTS energy terms to improve score-RMSD or score-affinity correlations. In a virtual screening context ligand-based similarity is used to rescale the docking score such that better enrichment factors are achieved. We discuss the algorithmic details of the methods, and demonstrate the effects of score tuning on a variety of targets, including CDK2, BACE1 and neuraminidase, as well as on the popular benchmarks--the Directory of Useful Decoys and the PDBBind database.

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Virtual Decoy Sets for Molecular Docking Benchmarks

Cited by 55 publications

References 17 publications

Benchmarking Data Sets for the Evaluation of Virtual Ligand Screening Methods: Review and Perspectives

Benchmarking Data Sets for the Evaluation of Virtual Ligand Screening Methods: Review and Perspectives

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

Improving molecular docking through eHiTS’ tunable scoring function

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