Application of Quantitative Structure–Activity Relationship Models of 5-HT1A Receptor Binding to Virtual Screening Identifies Novel and Potent 5-HT1A Ligands
Open accessAbstract
The 5-hydroxytryptamine 1A (5-HT1A) serotonin receptor has been an attractive target for treating mood and anxiety disorders such as schizophrenia. We have developed binary classification quantitative structure-activity relationship (QSAR) models of 5-HT1A receptor binding activity using data retrieved from the PDSP Ki database. The prediction accuracy of these models was estimated by external 5-fold cross-validation as well as using an additional validation set comprising 66 structurally distinct compounds from the World of Molecular Bioactivity database. These validated models were then used to mine three major types of chemical screening libraries, i.e., drug-like libraries, GPCR targeted libraries, and diversity libraries, to identify novel computational hits. The five best hits from each class of libraries were chosen for further experimental testing in radioligand binding assays, and nine of the 15 hits were confirmed to be active experimentally with binding affinity better than 10 μM. The most active compound, Lysergol, from the diversity library showed very high binding affinity (Ki) of 2.3 nM against 5-HT1A receptor. The novel 5-HT1A actives identified with the QSAR-based virtual screening approach could be potentially developed as novel anxiolytics or potential antischizophrenic drugs.
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Using data on affinity of drug-like compounds against several GPCRs, the kNN method gave the best results, PASS moderate, SEA lowest.
“The best results were obtained with the kNN method, while PASS demonstrated a moderate predictive accuracy and SEA shown the lowest prediction power across multiple targets.”
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Cites
Breiman, L. (2001) · Machine Learning · doi:10.1023/a:1010933404324 · Cited by 133,538 · see citing works
LIBSVM: A library for support vector machines
Chang, C.-C., & Lin, C.-J. (2011) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/1961189.1961199 · Cited by 40,918 · see citing works
The Nature of Statistical Learning Theory
Vapnik, V. N. (1995) · doi:10.1007/978-1-4757-2440-0 · Cited by 39,138 · see citing works
Classification and Regression by randomForest
Liaw, A. I., & Wiener, M. C. (2007) · Cited by 18,594 · see citing works
Lipinski, C. A., Lombardo, F., Dominy, B. W., & Feeney, P. J. (2001) · Advanced Drug Delivery Reviews · doi:10.1016/s0169-409x(00)00129-0 · Cited by 18,139 · see citing works
Weininger, D. (1988) · Journal of Chemical Information and Computer Sciences · doi:10.1021/ci00057a005 · Cited by 7,436 · see citing works
Golbraikh, A., & Tropsha, A. (2002) · Journal of Molecular Graphics and Modelling · doi:10.1016/s1093-3263(01)00123-1 · Cited by 3,735 · see citing works
Self-citationTropsha, A., Gramatica, P., & Gombar, V. K. (2003) · QSAR & Combinatorial Science · doi:10.1002/qsar.200390007 · Cited by 2,195 · see citing works
Self-citationBest Practices for QSAR Model Development, Validation, and Exploitation
Tropsha, A. (2010) · Molecular Informatics · doi:10.1002/minf.201000061 · Cited by 2,042 · see citing works
Self-citationPredicting new molecular targets for known drugs
Keiser, M. J., Setola, V., Irwin, J. J., Laggner, C., Abbas, A. I., Hufeisen, S. J., Jensen, N. H., Kuijer, M. B., Matos, R. C., Tran, T. B., et al. (2009) · Nature · doi:10.1038/nature08506 · Cited by 1,667 · see citing works
Self-citationSchotte, A., Janssen, P. F. M., Gommeren, W., Luyten, W. H. M. L., Gompel, P. V., Lesage, A. S., Loore, K. D., & Leysen, J. E. (1996) · Psychopharmacology · doi:10.1007/bf02245606 · Cited by 1,167 · see citing works
Radioreceptor Binding Profile of the Atypical Antipsychotic Olanzapine
Bymaster, F. P., Calligaro, D. O., Falcone, J. F., Marsh, R. D., Moore, N. A., Tye, N. C., Seeman, P. M., & Wong, D. T. (1996) · Neuropsychopharmacology · doi:10.1016/0893-133x(94)00129-n · Cited by 1,061 · see citing works
Aripiprazole, A Novel Atypical Antipsychotic Drug with a Unique and Robust Pharmacology
Shapiro, D. A., Renock, S. M., Arrington, E. G., Chiodo, L. A., Liu, L.-X., Sibley, D. R., Roth, B. L., & Mailman, R. B. (2003) · Neuropsychopharmacology · doi:10.1038/sj.npp.1300203 · Cited by 971 · see citing works
Self-citationAutomated design of ligands to polypharmacological profiles
Besnard, J., Ruda, G. F., Setola, V., Abecassis, K., Rodriguiz, R. M., Huang, X.-P., Norval, S., Sassano, M. F., Shin, A. I., Webster, L. A., et al. (2012) · Nature · doi:10.1038/nature11691 · Cited by 832 · see citing works
Self-citationFourches, D., Muratov, E., & Tropsha, A. (2010) · Journal of Chemical Information and Modeling · doi:10.1021/ci100176x · Cited by 832 · see citing works
Self-citationKroeze, W. K., Hufeisen, S. J., Popadak, B. A., Renock, S. M., Steinberg, S., Ernsberger, P. R., Jayathilake, K., Meltzer, H. Y., & Roth, B. L. (2003) · Neuropsychopharmacology · doi:10.1038/sj.npp.1300027 · Cited by 803 · see citing works
Self-citationSalvinorin A: A potent naturally occurring nonnitrogenous κ opioid selective agonist
Roth, B. L., Baner, K., Westkaemper, R., Siebert, D., Rice, K. C., Steinberg, S., Ernsberger, P., & Rothman, R. B. (2002) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.182234399 · Cited by 784 · see citing works
Self-citationIntroductory Statistics with R
Dalgaard, P. (2002) · Springer eBooks · doi:10.1007/b97671 · Cited by 734 · see citing works
Rational selection of training and test sets for the development of validated QSAR models
Golbraikh, A., Shen, M., Xiao, Z., Xiao, Y.-D., Lee, K.-H., & Tropsha, A. (2003) · Journal of Computer-Aided Molecular Design · doi:10.1023/a:1025386326946 · Cited by 712 · see citing works
Self-citationStructure-based virtual screening: an overview
Lyne, P. D. (2002) · Drug Discovery Today · doi:10.1016/s1359-6446(02)02483-2 · Cited by 662 · see citing works
Introductory Statistics with R
(2008) · doi:10.1007/978-0-387-79054-1 · Cited by 648 · see citing works
Efficient svm training using low-rank kernel representations
Fine, S., & Scheinberg, K. (2002) · Cited by 584 · see citing works
Golbraikh, A., & Tropsha, A. (2002) · Journal of Computer-Aided Molecular Design · doi:10.1023/a:1020869118689 · Cited by 504 · see citing works
Self-citationPredictive QSAR Modeling Workflow, Model Applicability Domains, and Virtual Screening
Tropsha, A., & Golbraikh, A. (2007) · Current Pharmaceutical Design · doi:10.2174/138161207782794257 · Cited by 479 · see citing works
Self-citationZheng, W., & Tropsha, A. (1999) · Journal of Chemical Information and Computer Sciences · doi:10.1021/ci980033m · Cited by 450 · see citing works
Self-citationStatistical Validation of QSAR Results
Wold, S., Eriksson, L., & Clementi, S. (1995) · Methods and principles in medicinal chemistry · doi:10.1002/9783527615452.ch5 · Cited by 335 · see citing works
Weinshank, R. L., Zgombick, J. M., Macchi, M., Branchek, T. A., & Hartig, P. R. (1992) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.89.8.3630 · Cited by 294 · see citing works
Golbraikh, A., & Tropsha, A. (2000) · Molecular Diversity · doi:10.1023/a:1021372108686 · Cited by 250 · see citing works
Self-citationThe 5-HT1A receptor in schizophrenia: a promising target for novel atypical neuroleptics?
Bantick, R. A., Deakin, J. F. W., & Grasby, P. M. (2001) · Journal of Psychopharmacology · doi:10.1177/026988110101500108 · Cited by 214 · see citing works
Li, Z., Ichikawa, J., Dai, J., & Meltzer, H. Y. (2004) · European Journal of Pharmacology · doi:10.1016/j.ejphar.2004.04.028 · Cited by 203 · see citing works
Chemoinformatics Approaches to Virtual Screening
Varnek, A. A., Tropsha, A., Zheng, W., et al. (2008) · Royal Society of Chemistry eBooks · doi:10.1039/9781847558879 · Cited by 188 · see citing works
Self-citationFourches, D., Barnes, J. C., Day, N. C., Bradley, P., Reed, J. Z., & Tropsha, A. (2010) · Chemical Research in Toxicology · doi:10.1021/tx900326k · Cited by 162 · see citing works
Self-citationRoth, B. L., Tandra, S., Burgess, L. H., Sibley, D. R., & Meltzer, H. Y. (1995) · Psychopharmacology · doi:10.1007/bf02311185 · Cited by 143 · see citing works
Self-citationCombinatorial QSAR Modeling of P-Glycoprotein Substrates
Lima, P. D. C., Golbraikh, A., Oloff, S., Xiao, Y.-D., & Tropsha, A. (2006) · Journal of Chemical Information and Modeling · doi:10.1021/ci0504317 · Cited by 138 · see citing works
Self-citationReview of the side-effect profile of buspirone
Newton, R. E., Marunycz, J. D., Alderdice, M. T., et al. (1986) · The American Journal of Medicine · doi:10.1016/0002-9343(86)90327-x · Cited by 127 · see citing works
WOMBAT and WOMBAT‐PK: Bioactivity Databases for Lead and Drug Discovery
Olah, M., Rad-Curpăn, R., Ostopovici, L., Bora, A., Hadaruga, N. G., Hadaruga, D. I., Moldovan, R., Fuliaş, A., Mractc, M., & Oprea, T. I. (2007) · Chemical biology · doi:10.1002/9783527619375.ch13b · Cited by 105 · see citing works
Simon, J. S., & Nemeroff, C. B. (2005) · The Journal of Clinical Psychiatry · doi:10.4088/jcp.v66n1002 · Cited by 103 · see citing works
Schechter, L. E., Dawson, L. A., & Harder, J. (2002) · Current Pharmaceutical Design · doi:10.2174/1381612023396483 · Cited by 93 · see citing works
Hoffman, B. T., Cho, S. J., Zheng, W., Wyrick, S. D., Nichols, D. E., Mailman, R. B., & Tropsha, A. (1999) · Journal of Medicinal Chemistry · doi:10.1021/jm980415j · Cited by 79 · see citing works
Self-citationLong-Term Antidepressant Efficacy and Safety of Olanzapine/Fluoxetine Combination
Corya, S. A., Andersen, S. W., Detke, H. C., Kelly, L. S., Campen, L. E. V., Sanger, T. M., Williamson, D. J., & Dubé, S. (2003) · The Journal of Clinical Psychiatry · doi:10.4088/jcp.v64n1111 · Cited by 78 · see citing works
k Nearest Neighbors QSAR Modeling as a Variational Problem: Theory and Applications
Itskowitz, P., & Tropsha, A. (2005) · Journal of Chemical Information and Modeling · doi:10.1021/ci049628+ · Cited by 69 · see citing works
Self-citationTropsha, A., & Weifan, Z. (2001) · Current Pharmaceutical Design · doi:10.2174/1381612013397834 · Cited by 61 · see citing works
Self-citationRecent Trends in Quantitative Structure‐Activity Relationships
Tropsha, A. (2003) · Burger's Medicinal Chemistry and Drug Discovery · doi:10.1002/0471266949.bmc002 · Cited by 41 · see citing works
Self-citationQSAR/QSPR and Proprietary Data
Jorgensen, W. L. (2006) · Journal of Chemical Information and Modeling · doi:10.1021/ci0680079 · Cited by 30 · see citing works
The effect of a 5-HT1A receptor agonist on striatal dopamine release
Bantick, R. A., de Vries, M. H., & Grasby, P. M. (2005) · Synapse · doi:10.1002/syn.20156 · Cited by 29 · see citing works
Mizukami, K., Ishikawa, M., Akatsu, H., Abrahamson, E. E., Ikonomović, M. D., & Asada, T. (2011) · Neuropathology · doi:10.1111/j.1440-1789.2010.01193.x · Cited by 26 · see citing works
Systematic study of substance P analogs
WANG, J., DIPASQUALEI, A. J., Bray, A. M., et al. (1993) · International journal of peptide & protein research · doi:10.1111/j.1399-3011.1993.tb00510.x · Cited by 17 · see citing works
A case study evaluating the use of clozapine in depression with psychotic features
Jeyapaul, P., & Vieweg, R. (2006) · Annals of General Psychiatry · doi:10.1186/1744-859x-5-20 · Cited by 13 · see citing works
[Generalized anxiety disorder (GAD)--diagnosis and therapy].
Boerner, R. J. (2007) · PubMed · Cited by 4 · see citing works
Groó, D., & Palosi, A. (1990) · PubMed · Cited by 1 · see citing works
Flagged first, then by citations
Research works printed but not linked: 1
- Fine S.; Scheinberg K.; Effcient S. V. M. Training Using Low-rank Kernel Representations. J. Mach. Learn. Res. 2001, 2, 243– 264.
Cited by
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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
- Luo, M., Wang, X. S., Roth, B. L., Golbraikh, A., & Tropsha, A.
- Affiliations
- University of North Carolina at Chapel Hill (US)
- Howard University (US)
- Corresponding author
- Wang, Xiang Simon
- Tropsha, Alexander
- Venue
- Journal of Chemical Information and Modeling
- Publisher
- American Chemical Society (ACS)
- Published
- 2014-02-12
- Volume
- 54
- Issue
- 2
- Pages
- 634-647
- ISSN
- 1549-9596 (ISSN-L)
- 1549-960X
- PMC
- PMC3985444
- OpenAlex
- W1990836196
- Cited by
- 37
- References
- 50
- Citations by year
- 2026: 3 · 2025: 4 · 2024: 2 · 2023: 4 · 2022: 3 · 2021: 5 · 2020: 1 · 2019: 2 · 2018: 3 · 2017: 1 · 2016: 4 · 2015: 3 · 2014: 2
- FWCI
- 3.45
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- Journal of Chemical Information and Modeling (publishedVersion, other-oa)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Carolina Digital Repository (University of North Carolina at Chapel Hill) (submittedVersion)
- UNC Libraries
- Funders
- National Institutes of Health (GM066940, GM096967, RO1MH61887)
- Topics
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Receptor Mechanisms and Signaling
- Health Sciences › Medicine › Pharmacology › Cholinesterase and Neurodegenerative Diseases
- Keywords
- 5-HT1A receptor, serotonin receptors, QSAR models, virtual screening, receptor binding affinity, binary classification, GPCR ligands, lysergol, anxiolytics, antipsychotic drugs, mood and anxiety disorders
- MeSH terms
- Drug Evaluation, Preclinical, Ligands, Protein Binding, Antipsychotic Agents, User-Computer Interface, Quantitative Structure-Activity Relationship, Receptor, Serotonin, 5-HT1A
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