| scholarly article | Q13442814 |
| P50 | author | Quentin S. Hanley | Q47304075 |
| P2093 | author name string | Quentin S Hanley | |
| P2860 | cites work | Data Mining and Computational Modeling of High-Throughput Screening Datasets. | Q52578677 |
| Bayesian Multi-Plate High-Throughput Screening of Compounds. | Q55472182 | ||
| Is Human Height Bimodal? | Q55966636 | ||
| Fluctuation scaling in complex systems: Taylor's law and beyond1 | Q56391649 | ||
| High-Throughput Screening Assay Datasets from the PubChem Database | Q56888960 | ||
| Exploring Activity Profiles of PAINS and Their Structural Context in Target–Ligand Complexes | Q56919637 | ||
| The Ecstasy and Agony of Assay Interference Compounds | Q60502263 | ||
| ChemBank: a small-molecule screening and cheminformatics resource database | Q36454111 | ||
| How to Triage PAINS-Full Research | Q36825688 | ||
| Lipopolysaccharide transport and assembly at the outer membrane: the PEZ model | Q37077552 | ||
| GIV/Girdin activates Gαi and inhibits Gαs via the same motif | Q37304977 | ||
| Mobyle: a new full web bioinformatics framework | Q37411747 | ||
| Detecting and overcoming systematic bias in high-throughput screening technologies: a comprehensive review of practical issues and methodological solutions | Q38370287 | ||
| Discovery, design and synthesis of 6H-anthra[1,9-cd]isoxazol-6-one scaffold as G9a inhibitor through a combination of shape-based virtual screening and structure-based molecular modification | Q38737563 | ||
| Chemical Measurement and Fluctuation Scaling. | Q38963767 | ||
| Open Source Bayesian Models. 2. Mining a "Big Dataset" To Create and Validate Models with ChEMBL. | Q40917756 | ||
| Identification and correction of spatial bias are essential for obtaining quality data in high-throughput screening technologies | Q41897369 | ||
| ChemMine. A compound mining database for chemical genomics. | Q45999839 | ||
| Fluctuation Scaling, Calibration of Dispersion, and Detection of Differences. | Q47822161 | ||
| Seven Year Itch: Pan-Assay Interference Compounds (PAINS) in 2017-Utility and Limitations. | Q48185720 | ||
| Maximizing gain in high-throughput screening using conformal prediction. | Q50320785 | ||
| Statistical methods for analysis of high-throughput RNA interference screens | Q24644919 | ||
| RPBS: a web resource for structural bioinformatics | Q24813293 | ||
| Alzheimer's disease therapeutics targeted to the control of amyloid precursor protein translation: maintenance of brain iron homeostasis | Q27024824 | ||
| Badapple: promiscuity patterns from noisy evidence | Q27321430 | ||
| The development, optimization and validation of an assay for high throughput antiviral drug screening against Dengue virus | Q27490779 | ||
| PubChem Substance and Compound databases | Q27942588 | ||
| A Simple Statistical Parameter for Use in Evaluation and Validation of High Throughput Screening Assays | Q28145712 | ||
| ChEMBL: a large-scale bioactivity database for drug discovery | Q28315179 | ||
| ChemMine tools: an online service for analyzing and clustering small molecules | Q28530229 | ||
| A yeast-based chemical screen identifies a PDE inhibitor that elevates steroidogenesis in mouse Leydig cells via PDE8 and PDE4 inhibition | Q28535340 | ||
| The ChEMBL database in 2017 | Q28584450 | ||
| BindingDB in 2015: A public database for medicinal chemistry, computational chemistry and systems pharmacology | Q28603124 | ||
| New substructure filters for removal of pan assay interference compounds (PAINS) from screening libraries and for their exclusion in bioassays | Q29615588 | ||
| Development of a virtual screening method for identification of "frequent hitters" in compound libraries | Q30783737 | ||
| Statistical practice in high-throughput screening data analysis | Q31032001 | ||
| High-throughput screening for human galactokinase inhibitors | Q33336038 | ||
| ChemmineR: a compound mining framework for R. | Q33348428 | ||
| Investigation of the incidence of "undesirable" molecular moieties for high-throughput screening compound libraries in marketed drug compounds | Q33359104 | ||
| FAF-Drugs2: free ADME/tox filtering tool to assist drug discovery and chemical biology projects | Q33371792 | ||
| A Computer Program for Classifying Plants | Q34006445 | ||
| Small molecules enhance autophagy and reduce toxicity in Huntington's disease models. | Q34626231 | ||
| Performance of Dark Chemical Matter in High Throughput Screening | Q36168803 | ||
| Phantom PAINS: Problems with the Utility of Alerts for Pan-Assay INterference CompoundS. | Q36271413 | ||
| Advances in understanding bacterial outer-membrane biogenesis | Q36343565 | ||
| 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 | Q36350578 | ||
| P275 | copyright license | Creative Commons Attribution 4.0 International | Q20007257 |
| P6216 | copyright status | copyrighted | Q50423863 |
| P433 | issue | 1 | |
| P407 | language of work or name | English | Q1860 |
| P304 | page(s) | 1268 | |
| P577 | publication date | 2019-02-04 | |
| P1433 | published in | Scientific Reports | Q2261792 |
| P1476 | title | The Distribution of Standard Deviations Applied to High Throughput Screening | |
| P478 | volume | 9 |
| Q92072825 | Potent and selective inhibitors for M32 metallocarboxypeptidases identified from high-throughput screening of anti-kinetoplastid chemical boxes |
| Q100559100 | Statistical models for identifying frequent hitters in high throughput screening |
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