Frequent hitters: nuisance artifacts in high-throughput screening.
暂无分享,去创建一个
Dong-Sheng Cao | Zi-Yi Yang | Jun-Hong He | Ai-Ping Lu | Ting-Jun Hou | Dongsheng Cao | Aiping Lu | Tingjun Hou | Zi-Yi Yang | Jun-Hong He
[1] Renaldo Mendoza,et al. ALARM NMR: a rapid and robust experimental method to detect reactive false positives in biochemical screens. , 2005, Journal of the American Chemical Society.
[2] R. Solé,et al. Data completeness—the Achilles heel of drug-target networks , 2008, Nature Biotechnology.
[3] Balaguru Ravikumar,et al. Improving the efficacy-safety balance of polypharmacology in multi-target drug discovery , 2018, Expert opinion on drug discovery.
[4] Igor V. Tetko,et al. Identification of Small-Molecule Frequent Hitters from AlphaScreen High-Throughput Screens , 2014, Journal of biomolecular screening.
[5] B. Shoichet,et al. A common mechanism underlying promiscuous inhibitors from virtual and high-throughput screening. , 2002, Journal of medicinal chemistry.
[6] Anne Mai Wassermann,et al. Dark chemical matter as a promising starting point for drug lead discovery. , 2015, Nature chemical biology.
[7] Paul J Hergenrother,et al. Deoxynyboquinones as NQO1-Activated Cancer Therapeutics. , 2015, Accounts of chemical research.
[8] Scott B Ficarro,et al. A structure-guided approach to creating covalent FGFR inhibitors. , 2010, Chemistry & biology.
[9] F. Nan,et al. Isoquinoline-1,3,4-trione Derivatives Inactivate Caspase-3 by Generation of Reactive Oxygen Species* , 2008, Journal of Biological Chemistry.
[10] Luhua Lai,et al. LigBuilder 2: A Practical de Novo Drug Design Approach , 2011, J. Chem. Inf. Model..
[11] Anton Simeonov,et al. Firefly luciferase in chemical biology: a compendium of inhibitors, mechanistic evaluation of chemotypes, and suggested use as a reporter. , 2012, Chemistry & biology.
[12] Jürgen Bajorath,et al. Data structures for computational compound promiscuity analysis and exemplary applications to inhibitors of the human kinome , 2019, Journal of Computer-Aided Molecular Design.
[13] B. Shoichet,et al. Identification and prediction of promiscuous aggregating inhibitors among known drugs. , 2003, Journal of medicinal chemistry.
[14] Robert W. Bryant,et al. Evaluation of Fluorescent Compound Interference in 4 Fluorescence Polarization Assays: 2 Kinases, 1 Protease, and 1 Phosphatase , 2003, Journal of biomolecular screening.
[15] Dan Li,et al. Prediction of luciferase inhibitors by the high-performance MIEC-GBDT approach based on interaction energetic patterns. , 2017, Physical chemistry chemical physics : PCCP.
[16] Feng-Xu Wu,et al. ACFIS: a web server for fragment-based drug discovery , 2016, Nucleic Acids Res..
[17] Adrian Whitty,et al. The resurgence of covalent drugs , 2011, Nature Reviews Drug Discovery.
[18] T. Hunter,et al. The Protein Kinase Complement of the Human Genome , 2002, Science.
[19] Olivier Sperandio,et al. FAF-Drugs3: a web server for compound property calculation and chemical library design , 2015, Nucleic Acids Res..
[20] J Stables,et al. Development of a dual glow-signal firefly and Renilla luciferase assay reagent for the analysis of G-protein coupled receptor signalling. , 1999, Journal of receptor and signal transduction research.
[21] Peter Wipf,et al. Profiling the NIH Small Molecule Repository for compounds that generate H2O2 by redox cycling in reducing environments. , 2010, Assay and drug development technologies.
[22] Alexander Tropsha,et al. Phantom PAINS: Problems with the Utility of Alerts for Pan-Assay INterference CompoundS , 2017, J. Chem. Inf. Model..
[23] Brian K Shoichet,et al. Interpreting steep dose-response curves in early inhibitor discovery. , 2006, Journal of medicinal chemistry.
[24] Yanli Wang,et al. A novel method for mining highly imbalanced high-throughput screening data in PubChem , 2009, Bioinform..
[25] Kristin E. D. Coan,et al. Stoichiometry and physical chemistry of promiscuous aggregate-based inhibitors. , 2008, Journal of the American Chemical Society.
[26] Jayme L. Dahlin,et al. Assay Interference by Chemical Reactivity , 2015 .
[27] Adam Yasgar,et al. Quantitative high-throughput screening: a titration-based approach that efficiently identifies biological activities in large chemical libraries. , 2006, Proceedings of the National Academy of Sciences of the United States of America.
[28] Nils-Ole Friedrich,et al. Hit Dexter: A Machine‐Learning Model for the Prediction of Frequent Hitters , 2018, ChemMedChem.
[29] Christopher P Austin,et al. Characterization of chemical libraries for luciferase inhibitory activity. , 2008, Journal of medicinal chemistry.
[30] G. Rishton. Reactive compounds and in vitro false positives in HTS , 1997 .
[31] Brian K Shoichet,et al. A detergent-based assay for the detection of promiscuous inhibitors , 2006, Nature Protocols.
[32] Jürgen Bajorath,et al. How Frequently Are Pan-Assay Interference Compounds Active? Large-Scale Analysis of Screening Data Reveals Diverse Activity Profiles, Low Global Hit Frequency, and Many Consistently Inactive Compounds. , 2017, Journal of medicinal chemistry.
[33] Brian K. Shoichet,et al. Colloidal Drug Formulations Can Explain “Bell-Shaped” Concentration–Response Curves , 2014, ACS chemical biology.
[34] D. Auld,et al. Illuminating insights into firefly luciferase and other bioluminescent reporters used in chemical biology. , 2010, Chemistry & biology.
[35] Johannes Kirchmair,et al. Hit Dexter 2.0: Machine-Learning Models for the Prediction of Frequent Hitters , 2019, J. Chem. Inf. Model..
[36] P Schneider,et al. Spotting and designing promiscuous ligands for drug discovery. , 2016, Chemical communications.
[37] Wolfgang Guba,et al. Development of a virtual screening method for identification of "frequent hitters" in compound libraries. , 2002, Journal of medicinal chemistry.
[38] Evan Bolton,et al. PubChem 2019 update: improved access to chemical data , 2018, Nucleic Acids Res..
[39] D. Boschelli,et al. 4-anilino-3-quinolinecarbonitriles: an emerging class of kinase inhibitors. , 2002, Current topics in medicinal chemistry.
[40] Tudor I. Oprea,et al. Badapple: promiscuity patterns from noisy evidence , 2016, Journal of Cheminformatics.
[41] Ian A. Watson,et al. Rules for identifying potentially reactive or promiscuous compounds. , 2012, Journal of medicinal chemistry.
[42] Peter Caprioli,et al. Evaluation of an antibody-free ADP detection assay: ADP-Glo. , 2009, Assay and drug development technologies.
[43] R. Solé,et al. The topology of drug-target interaction networks: implicit dependence on drug properties and target families. , 2009, Molecular bioSystems.
[44] B. Shoichet,et al. High-throughput assays for promiscuous inhibitors , 2005, Nature chemical biology.
[45] Tudor I. Oprea,et al. An Overview of the Challenges in Designing, Integrating, and Delivering BARD , 2014, Journal of biomolecular screening.
[46] Bo-Han Su,et al. Rule-Based Classification Models of Molecular Autofluorescence , 2015, J. Chem. Inf. Model..
[47] George Papadatos,et al. The ChEMBL database in 2017 , 2016, Nucleic Acids Res..
[48] Jie Dong,et al. TargetNet: a web service for predicting potential drug–target interaction profiling via multi-target SAR models , 2016, Journal of Computer-Aided Molecular Design.
[49] Jürgen Bajorath,et al. Matched molecular pair analysis of small molecule microarray data identifies promiscuity cliffs and reveals molecular origins of extreme compound promiscuity. , 2012, Journal of medicinal chemistry.
[50] Jürgen Bajorath,et al. Systematic computational identification of promiscuity cliff pathways formed by inhibitors of the human kinome , 2019, Journal of Computer-Aided Molecular Design.
[51] J Willem M Nissink,et al. Promiscuous 2-aminothiazoles (PrATs): a frequent hitting scaffold. , 2015, Journal of medicinal chemistry.
[52] Taraneh Mirzadegan,et al. Structural Basis of Small-Molecule Aggregate Induced Inhibition of a Protein-Protein Interaction. , 2017, Journal of medicinal chemistry.
[53] Michael Cory,et al. Expression, preparation, and high-throughput screening of caspase-8: discovery of redox-based and steroid diacid inhibition. , 2002, Archives of biochemistry and biophysics.
[54] Michael J. Keiser,et al. Predicting new molecular targets for known drugs , 2009, Nature.
[55] Anton Simeonov,et al. Interference with Fluorescence and Absorbance , 2015 .
[56] Ruili Huang,et al. Fluorescence spectroscopic profiling of compound libraries. , 2008, Journal of medicinal chemistry.
[57] Michael J. Keiser,et al. Relating protein pharmacology by ligand chemistry , 2007, Nature Biotechnology.
[58] Ronald T Raines,et al. Bright ideas for chemical biology. , 2008, ACS chemical biology.
[59] Olivier Sperandio,et al. FAF-Drugs2: Free ADME/tox filtering tool to assist drug discovery and chemical biology projects , 2008, BMC Bioinformatics.
[60] Aurélien Grosdidier,et al. SwissTargetPrediction: a web server for target prediction of bioactive small molecules , 2014, Nucleic Acids Res..
[61] J. Jesús Naveja,et al. HitPickV2: a web server to predict targets of chemical compounds , 2018, Bioinform..
[62] Maria Paola Costi,et al. Comprehensive mechanistic analysis of hits from high-throughput and docking screens against beta-lactamase. , 2008, Journal of medicinal chemistry.
[63] J. Peters. Polypharmacology - foe or friend? , 2013, Journal of medicinal chemistry.
[64] Brian K. Shoichet,et al. Protein stability effects in aggregate-based enzyme inhibition. , 2019, Journal of medicinal chemistry.
[65] Igor V Tetko,et al. Identification of Small-Molecule Frequent Hitters of Glutathione S-Transferase–Glutathione Interaction , 2016, Journal of biomolecular screening.
[66] Jeffrey R. Huth,et al. Enhancement of chemical rules for predicting compound reactivity towards protein thiol groups , 2007, J. Comput. Aided Mol. Des..
[67] Michael Sattler,et al. Luciferase Advisor: High-Accuracy Model To Flag False Positive Hits in Luciferase HTS Assays , 2018, J. Chem. Inf. Model..
[68] Stefan Vasile,et al. The oxidative mechanism of action of ortho-quinone inhibitors of protein-tyrosine phosphatase alpha is mediated by hydrogen peroxide. , 2004, Archives of biochemistry and biophysics.
[69] Michal Heger,et al. Don't discount all curcumin trial data , 2017, Nature.
[70] J. Baell,et al. New substructure filters for removal of pan assay interference compounds (PAINS) from screening libraries and for their exclusion in bioassays. , 2010, Journal of medicinal chemistry.
[71] Ge-Fei Hao,et al. Computational discovery of picomolar Q(o) site inhibitors of cytochrome bc1 complex. , 2012, Journal of the American Chemical Society.
[72] Jürgen Bajorath,et al. Identification of Compounds That Interfere with High‐Throughput Screening Assay Technologies , 2019, Chemmedchem.
[73] Christopher P Austin,et al. Quantitative analyses of aggregation, autofluorescence, and reactivity artifacts in a screen for inhibitors of a thiol protease. , 2010, Journal of medicinal chemistry.
[74] G. Rishton. Nonleadlikeness and leadlikeness in biochemical screening. , 2003, Drug discovery today.
[75] David Lagorce,et al. FAF‐Drugs4: free ADME‐tox filtering computations for chemical biology and early stages drug discovery , 2017, Bioinform..
[76] Ajay Goel,et al. Multi-targeted therapy by curcumin: how spicy is it? , 2008, Molecular nutrition & food research.
[77] Dong-Sheng Cao,et al. Structural Analysis and Identification of Colloidal Aggregators in Drug Discovery , 2019, J. Chem. Inf. Model..
[78] Pierre Tufféry,et al. FAF-Drugs: free ADME/tox filtering of compound collections , 2006, Nucleic Acids Res..
[79] Michael J. Keiser,et al. Complementarity Between a Docking and a High-Throughput Screen in Discovering New Cruzain Inhibitors† , 2010, Journal of medicinal chemistry.
[80] Maria F. Sassano,et al. Automated design of ligands to polypharmacological profiles , 2012, Nature.
[81] Anton Simeonov,et al. Molecular basis for the high-affinity binding and stabilization of firefly luciferase by PTC124 , 2010, Proceedings of the National Academy of Sciences.
[82] Adel Bakhtiarova,et al. Resveratrol inhibits firefly luciferase. , 2006, Biochemical and biophysical research communications.
[83] J. Baell,et al. Chemistry: Chemical con artists foil drug discovery , 2014, Nature.
[84] Yanli Wang,et al. PubChem BioAssay: 2017 update , 2016, Nucleic Acids Res..
[85] Jean-Louis Reymond,et al. The polypharmacology browser: a web-based multi-fingerprint target prediction tool using ChEMBL bioactivity data , 2017, Journal of Cheminformatics.
[86] Igor V. Tetko,et al. Online chemical modeling environment (OCHEM): web platform for data storage, model development and publishing of chemical information , 2011, J. Comput. Aided Mol. Des..
[87] Jean-Louis Reymond,et al. Polypharmacology Browser PPB2: Target Prediction Combining Nearest Neighbors with Machine Learning , 2018, J. Chem. Inf. Model..
[88] J. Irwin,et al. An Aggregation Advisor for Ligand Discovery. , 2015, Journal of medicinal chemistry.
[89] Daniel Reker,et al. Computational advances in combating colloidal aggregation in drug discovery , 2019, Nature Chemistry.
[90] James Inglese,et al. Interferences with Luciferase Reporter Enzymes , 2016 .
[91] Hanbing Rao,et al. Identification of small molecule aggregators from large compound libraries by support vector machines , 2009, J. Comput. Chem..
[92] Jing-Fang Yang,et al. PADFrag: A Database Built for the Exploration of Bioactive Fragment Space for Drug Discovery , 2018, J. Chem. Inf. Model..
[93] Feng-Xu Wu,et al. Crystal Structure of 4-Hydroxyphenylpyruvate Dioxygenase in Complex with Substrate Reveals a New Starting Point for Herbicide Discovery , 2019, Research.
[94] Brian K Shoichet,et al. Colloidal aggregation: from screening nuisance to formulation nuance. , 2018, Nano today.
[95] Peter Wipf,et al. Redox Regulation of Cdc25B by Cell-Active Quinolinediones , 2005, Molecular Pharmacology.