Post-High-Throughput Screening Analysis: An Empirical Compound Prioritization Scheme
Open accessAbstract
An empirical scheme to evaluate and prioritize screening hits from high-throughput screening (HTS) is proposed. Negative scores are given when chemotypes found in the HTS hits are present in annotated databases such as MDDR and WOMBAT or for testing positive in toxicity-related experiments reported in TOXNET. Positive scores were given for higher measured biological activities, for testing negative in toxicity-related literature, and for good overlap when profiled against drug-related properties. Particular emphasis is placed on estimating aqueous solubility to prioritize in vivo experiments. This empirical scheme is given as an illustration to assist the decision-making process in selecting chemotypes and individual compounds for further experimentation, when confronted with multiple hits from high-throughput experiments. The decision-making process is discussed for a set of G-protein coupled receptor antagonists and validated on a literature example for dihydrofolate reductase inhibition.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Cites
Calculating log Poct from structures
Leo, A. J. (1993) · Chemical Reviews · doi:10.1021/cr00020a001 · Cited by 705 · see citing works
Pharmacophore perception, development, and use in drug design
Güner, O. F. (2000) · Medical Entomology and Zoology · Cited by 691 · see citing works
Property distribution of drug-related chemical databases*
Oprea, T. I. (2000) · Journal of Computer-Aided Molecular Design · doi:10.1023/a:1008130001697 · Cited by 629 · see citing works
Self-citationNonleadlikeness and leadlikeness in biochemical screening
Rishton, G. M. (2003) · Drug Discovery Today · doi:10.1016/s1359644602025722 · Cited by 381 · see citing works
Computer Prediction of Possible Toxic Action from Chemical Structure; The DEREK System
Sanderson, D. M., & Earnshaw, C. G. (1991) · Human & Experimental Toxicology · doi:10.1177/096032719101000405 · Cited by 370 · see citing works
Reactive compounds and in vitro false positives in HTS
Rishton, G. M. (1997) · Drug Discovery Today · doi:10.1016/s1359-6446(97)01083-0 · Cited by 348 · see citing works
MULTICASE 1. A Hierarchical Computer Automated Structure Evaluation Program
Klopman, G. (1992) · Quantitative Structure-Activity Relationships · doi:10.1002/qsar.19920110208 · Cited by 315 · see citing works
Flow cytometry for high-throughput, high-content screening
Edwards, B. S., Oprea, T. I., Prossnitz, E. R., & Sklar, L. A. (2004) · Current Opinion in Chemical Biology · doi:10.1016/j.cbpa.2004.06.007 · Cited by 206 · see citing works
Self-citationWOMBAT: World of Molecular Bioactivity
Olah, M., Mracec, M., Halip, L., Rad-Curpăn, R., Bora, A., Hadaruga, N. G., Olah, I., Banda, M., Simon, Z., Mracec, M., et al. (2005) · Methods and principles in medicinal chemistry · doi:10.1002/3527603743.ch9 · Cited by 117 · see citing works
Self-citationEvers, A., & Klebe, G. (2003) · Angewandte Chemie International Edition · doi:10.1002/anie.200352776 · Cited by 93 · see citing works
Zolli‐Juran, M., Cechetto, J. D., Hartlen, R., et al. (2003) · Bioorganic & Medicinal Chemistry Letters · doi:10.1016/s0960-894x(03)00480-3 · Cited by 69 · see citing works
Cheminformatics: a tool for decision-makers in drug discovery.
Olsson, T., & Oprea, T. I. (2001) · PubMed · Cited by 39 · see citing works
Self-citationMuegge, I., & Enyedy, I. J. (2003) · doi:10.1201/9780203913390-21 · Cited by 22 · see citing works
Comprehensive analysis of high-throughput screening data
Heyse, S. (2002) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.472120 · Cited by 19 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Muratov, E. N., Bajorath, J., Sheridan, R. P., Tetko, I. V., Filimonov, D., Poroikov, V., Oprea, T. I., Baskin, I. I., Varnek, A., Roitberg, A., et al. (2020) · Chemical Society Reviews · doi:10.1039/d0cs00098a · Cited by 913 · see citing works
Self-citation
- Authors
- Oprea, T. I., Bologa, C. G., Edwards, B. S., Prossnitz, E. R., & Sklar, L. A.
- Affiliations
- University of New Mexico (US)
- Venue
- SLAS Discovery
- Publisher
- Elsevier BV
- Published
- 2005-08
- Volume
- 10
- Issue
- 5
- Pages
- 419-426
- ISSN
- 2472-5552 (ISSN-L)
- OpenAlex
- W2108426257
- Cited by
- 24
- References
- 14
- Citations by year
- 2022: 1 · 2020: 1 · 2019: 1 · 2018: 1 · 2016: 1 · 2015: 1 · 2014: 1 · 2013: 1 · 2012: 1
- FWCI
- 4.12
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- SLAS DISCOVERY (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Health Sciences › Veterinary › Small Animals › Animal testing and alternatives
- Physical Sciences › Chemistry › Organic Chemistry › Synthesis and Characterization of Heterocyclic Compounds
- Keywords
- high-throughput screening hits, compound prioritization, drug-likeness, wombats, TOXNET, toxicity prediction, G protein-coupled receptor antagonists, dihydrofolate reductase inhibition
- MeSH terms
- Automation, Chemistry, Pharmaceutical, Computer Simulation, Dose-Response Relationship, Drug, Drug Industry, Drug Evaluation, Preclinical, Pharmaceutical Preparations, Models, Chemical, Models, Molecular, Solubility, Structure-Activity Relationship, Computational Biology, Combinatorial Chemistry Techniques
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
