Filters








85 Hits in 5.5 sec

An Unbiased Method To Build Benchmarking Sets for Ligand-Based Virtual Screening and its Application To GPCRs

Jie Xia, Hongwei Jin, Zhenming Liu, Liangren Zhang, Xiang Simon Wang
2014 Journal of Chemical Information and Modeling  
Herein, we propose an unbiased method to build benchmarking sets for LBVS and validate it on a multitude of GPCRs targets.  ...  Due to the lack of crystal structures, there is great need for unbiased benchmarking sets to evaluate various ligand-based virtual screening (LBVS) methods for important drug targets such as G protein-coupled  ...  We applied this workflow to build Unbiased Ligand Set (ULS)/ Unbiased Decoy Set (UDS) for 17 agonists/antagonists sets of 10 representative GPCR targets and carried out Leave-One-Out (LOO) Cross-Validation  ... 
doi:10.1021/ci500062f pmid:24749745 pmcid:PMC4038372 fatcat:2dvblysuzbfu7fcxfdspxaribq

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

Jie Xia, Ermias Lemma Tilahun, Terry-Elinor Reid, Liangren Zhang, Xiang Simon Wang
2015 Methods  
In addition, we introduced our recent algorithm to build maximum-unbiased benchmarking sets applicable to both ligand-based and structurebased VS approaches, and its implementations to three important  ...  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.  ...  In order to broaden the application domain of currently available LBVS-specific benchmarking sets, we recently proposed an unbiased method to build LBVS-specific benchmarking sets [78] .  ... 
doi:10.1016/j.ymeth.2014.11.015 pmid:25481478 pmcid:PMC4278665 fatcat:nuszj6hlrbe53borci42r6dhoq

Recent Advances and Applications of Molecular Docking to G Protein-Coupled Receptors

Damian Bartuzi, Agnieszka Kaczor, Katarzyna Targowska-Duda, Dariusz Matosiuk
2017 Molecules  
A number of improvements and innovative applications of this method were documented recently. In this review, we focus particularly on innovations in docking to GPCRs.  ...  The latter play an important role in this process, since they allow for observations on scales inaccessible for most other methods.  ...  Virtual screening approaches can be divided into ligand-based and structure-based.  ... 
doi:10.3390/molecules22020340 pmid:28241450 fatcat:xyytfxpfcvd43dyoefto4qr5pi

Comparative Modeling and Benchmarking Data Sets for Human Histone Deacetylases and Sirtuin Families

Jie Xia, Ermias Lemma Tilahun, Eyob Hailu Kebede, Terry-Elinor Reid, Liangren Zhang, Xiang Simon Wang
2015 Journal of Chemical Information and Modeling  
sets for ligand-based virtual screening (LBVS).  ...  To facilitate the process, we constructed the Maximal Unbiased Benchmarking Data Sets for HDACs (MUBD-HDACs) using our recently published methods that were originally developed for building unbiased benchmarking  ...  screening SBVS structure-based virtual screening LBVS ligand-based virtual screening DUD directory of useful decoys DUD-E DUD-enhanced VDS virtual decoy sets GPCRs G protein-coupled  ... 
doi:10.1021/ci5005515 pmid:25633490 pmcid:PMC4677826 fatcat:tfj3rsugs5eannty6k66f5ruei

Maximal Unbiased Benchmarking Data Sets for Human Chemokine Receptors and Comparative Analysis

Jie Xia, Terry-Elinor Reid, Song Wu, Liangren Zhang, Xiang Simon Wang
2018 Journal of Chemical Information and Modeling  
For rational selection of a wide variety of VS approaches, ligand enrichment assessment based on a benchmarking data set has become an indispensable practice.  ...  However, the lack of versatile benchmarking sets for the whole CRs family that are able to unbiasedly evaluate every single approaches including both structure and ligand based VS, somewhat hinders modern  ...  virtual screens (REPROVIS-DB) 56 and maximum unbiased validation (MUV) 57 were developed specifically for benchmarking LBVS approaches.  ... 
doi:10.1021/acs.jcim.8b00004 pmid:29698608 pmcid:PMC6197807 fatcat:mdqt6fklt5egdfobbb6oyz34vm

Benchmarking Data Sets from PubChem BioAssay Data: Current Scenario and Room for Improvement

Viet-Khoa Tran-Nguyen, Didier Rognan
2020 International Journal of Molecular Sciences  
Developing realistic data sets for evaluating virtual screening methods is a task that has been tackled by the cheminformatics community for many years.  ...  ligand sets.  ...  , the application of LIT-PCBA is thus not intended only for evaluating ligand-based or structure-based virtual screening alone but, rather, for both and, especially, for the field of machine-learning algorithm  ... 
doi:10.3390/ijms21124380 pmid:32575564 pmcid:PMC7352161 fatcat:pdalryhw6vchjmxvpg6pkhasde

Decoys Selection in Benchmarking Datasets: Overview and Perspectives

Manon Réau, Florent Langenfeld, Jean-François Zagury, Nathalie Lagarde, Matthieu Montes
2018 Frontiers in Pharmacology  
Such specialized datasets exist for GPCRs [GPCR ligand library (GLL)/Decoy Database (GDD) (Gatica and Cavasotto, 2012) ], histone deacetylases [maximal unbiased benchmarking data sets for HDACs-MUBD-HDACs  ...  CONCLUSION Benchmarking databases are widely used to evaluate virtual screening methods.  ...  Copyright © 2018 Réau, Langenfeld, Zagury, Lagarde and Montes. This is an openaccess article distributed under the terms of the Creative Commons Attribution License (CC BY).  ... 
doi:10.3389/fphar.2018.00011 pmid:29416509 pmcid:PMC5787549 fatcat:hiycz5gyf5gpvi2qyvo3hzyn5e

Computational Advances for the Development of Allosteric Modulators and Bitopic Ligands in G Protein-Coupled Receptors

Zhiwei Feng, Guanxing Hu, Shifan Ma, Xiang-Qun Xie
2015 AAPS Journal  
High-throughput screening (HTS) in combination with disulfide trapping and fragment-based screening are used to aid the discovery of the allosteric modulators or bitopic ligands of GPCRs.  ...  Discovering allosteric modulators or bitopic ligands for GPCRs has become an emerging research area, in which the design of allosteric modulators is a key step in the detection of bitopic ligands.  ...  ACKNOWLEDGMENTS The authors would like to acknowledge the funding support for the Xie laboratory at the University of Pittsburgh from the NIDA P30DA035778A1 and NIH R01DA025612.  ... 
doi:10.1208/s12248-015-9776-y pmid:25940084 pmcid:PMC4540734 fatcat:itsxo47p3rd33jxjx34qnqvhzy

Getting the most out of PubChem for virtual screening

Sunghwan Kim
2016 Expert Opinion on Drug Discovery  
It also provides multiple programmatic access routes, which are essential to build automated virtual screening pipelines that exploit PubChem data.  ...  Areas covered-This article provides an overview of how PubChem's data, tools, and services can be used for virtual screening and reviews recent publications that discuss important aspects of exploiting  ...  He also thanks Evan Bolton at PubChem and Bradley Otterson at the NIH Library Editing Service for critical reading of this manuscript.  ... 
doi:10.1080/17460441.2016.1216967 pmid:27454129 pmcid:PMC5045798 fatcat:7u2cvcj2zfdsrnhtpqvqueuuki

Machine learning classification can reduce false positives in structure-based virtual screening

Yusuf O. Adeshina, Eric J. Deeds, John Karanicolas
2020 Proceedings of the National Academy of Sciences of the United States of America  
With the recent explosion in the size of libraries available for screening, virtual screening is positioned to assume a more prominent role in early drug discovery's search for active chemical matter.  ...  These results support using the D-COID strategy for training classifiers in other computational biology tasks, and for vScreenML in virtual screening campaigns against other protein targets.  ...  We thank Joanna Slusky for a useful suggestion regarding presentation of the figures, and Juan Manuel Perez Bertoldi for his initial application of vScreenML to the PPI benchmark set.  ... 
doi:10.1073/pnas.2000585117 pmid:32669436 fatcat:zdybg4t6ofbyfbdskwnevedp7q

Machine learning classification can reduce false positives in structure-based virtual screening [article]

Yusuf Adeshina, Eric J. Deeds, John Karanicolas
2020 bioRxiv   pre-print
With the recent explosion in the size of libraries available for screening, virtual screening is positioned to assume a more prominent role in early drug discovery's search for active chemical matter.  ...  These results support using the D-COID strategy for training classifiers in other computational biology tasks, and for vScreenML in virtual screening campaigns against other protein targets.  ...  Acknowledgements We thank Joanna Slusky for a useful suggestion regarding presentation of the figures, and Juan Manuel Perez Bertoldi for his initial application of vScreenML to the PPI benchmark set.  ... 
doi:10.1101/2020.01.10.902411 fatcat:srhoxesav5g7lmyf7untfch6zm

Compound Activity Prediction Using Models of Binding Pockets or Ligand Properties in 3D

Irina Kufareva, Yu-Chen Chen, Andrey V. Ilatovskiy, Ruben Abagyan
2012 Current Topics in Medicinal Chemistry  
Using the data collected in the Pocketome encyclopedia, we here provide an overview of two types of the three-dimensional ligand activity models, pocketbased and ligand property-based, for two important  ...  Current advances in high-resolution protein structure determination, database development, and docking methodology make it possible to design three-dimensional models for prediction of such interactions  ...  Compound sets for model benchmarking Virtual models for prediction of compound activity may be required in context of several applications: compound screening for lead discovery, optimization for potency  ... 
doi:10.2174/156802612804547335 pmid:23116466 pmcid:PMC4085113 fatcat:cxm5b724djdgjnqf7vnaelbe5e

Orphan receptor ligand discovery by pickpocketing pharmacological neighbors

Tony Ngo, Andrey V Ilatovskiy, Alastair G Stewart, James L J Coleman, Fiona M McRobb, R Peter Riek, Robert M Graham, Ruben Abagyan, Irina Kufareva, Nicola J Smith
2016 Nature Chemical Biology  
When applied to orphan receptor GPR37L1, GPCR-CoINPocket identified its pharmacological neighbors, and transfer of their pharmacology aided discovery of the first surrogate ligands for this orphan with  ...  Although primarily designed for GPCRs, the method is easily transferable to other protein families.  ...  One notable advance has been the move to computer-based, virtual ligand screening (VLS) using both 3D receptor and ligand chemicalfield models.  ... 
doi:10.1038/nchembio.2266 pmid:27992882 pmcid:PMC5247308 fatcat:p2retgoawbg7beqasscm77f224

Benchmarking Ligand-Based Virtual High-Throughput Screening with the PubChem Database

Mariusz Butkiewicz, Edward Lowe, Ralf Mueller, Jeffrey Mendenhall, Pedro Teixeira, C. Weaver, Jens Meiler
2013 Molecules  
With the rapidly increasing availability of High-Throughput Screening (HTS) data in the public domain, such as the PubChem database, methods for ligand-based computer-aided drug discovery (LB-CADD) have  ...  We assemble nine data sets from realistic HTS campaigns representing major families of drug target proteins for benchmarking LB-CADD methods.  ...  Acknowledgments This work is supported through NIH (R01 MH090192, R01 GM080403) and NSF (Career 0742762, 0959454) to Jens Meiler. Edward W.  ... 
doi:10.3390/molecules18010735 pmid:23299552 pmcid:PMC3759399 fatcat:t5ya6sgagbe4dphrvsu4vgds5u

Improving virtual screening of G protein-coupled receptors via ligand-directed modeling

Thomas Coudrat, John Simms, Arthur Christopoulos, Denise Wootten, Patrick M. Sexton, Avner Schlessinger
2017 PLoS Computational Biology  
There is increasing interest in using structural information for virtual screening (VS) of libraries and for structure-based drug design to identify novel agonist or antagonist leads.  ...  We developed an extensive benchmark comparing LDM-refined binding pockets to GPCR X-ray crystal structures across seven different GPCRs bound to a range of ligands of different chemotypes and pharmacological  ...  This research was supported in part by the Monash eResearch Centre and eSolutions-Research Support Services through the use of the Monash Campus HPC Cluster.  ... 
doi:10.1371/journal.pcbi.1005819 pmid:29131821 pmcid:PMC5708846 fatcat:ymjtsmav7ffd5gbnsbne4ggfmq
« Previous Showing results 1 — 15 out of 85 results