The DOI matches its Crossref record; 102 of the 102 references it cites were found in a registry or an index.
Next generation 3D pharmacophore modeling
Open accessAbstract
3D pharmacophore models are three‐dimensional ensembles of chemically defined interactions of a ligand in its bioactive conformation. They represent an elegant way to decipher chemically encoded ligand information and have therefore become a valuable tool in drug design. In this review, we provide an overview on the basic concept of this method and summarize key studies for applying 3D pharmacophore models in virtual screening and mechanistic studies for protein functionality. Moreover, we discuss recent developments in the field. The combination of 3D pharmacophore models with molecular dynamics simulations could be a quantum leap forward since these approaches consider macromolecule–ligand interactions as dynamic and therefore show a physiologically relevant interaction pattern. Other trends include the efficient usage of 3D pharmacophore information in machine learning and artificial intelligence applications or freely accessible web servers for 3D pharmacophore modeling. The recent developments show that 3D pharmacophore modeling is a vibrant field with various applications in drug discovery and beyond. This article is categorized under: Computer and Information Science > Chemoinformatics Computer and Information Science > Computer Algorithms and Programming Molecular and Statistical Mechanics > Molecular Interactions
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AutoDock4 and AutoDockTools4: Automated docking with selective receptor flexibility
Morris, G. M., Huey, R., Lindstrom, W., Sanner, M. F., Belew, R. K., Goodsell, D. S., & Olson, A. J. (2009) · Journal of Computational Chemistry · doi:10.1002/jcc.21256 · Cited by 25,486
Friesner, R. A., Murphy, R. B., Repasky, M. P., Frye, L. L., Greenwood, J. R., Halgren, T. A., Sanschagrin, P. C., & Mainz, D. T. (2006) · Journal of Medicinal Chemistry · doi:10.1021/jm051256o · Cited by 6,851
Cramer, R. D., Patterson, D. E., & Bunce, J. D. (1988) · Journal of the American Chemical Society · doi:10.1021/ja00226a005 · Cited by 3,931
PubChem 2019 update: improved access to chemical data
Kim, S., Chen, J., Cheng, T., Gindulyte, A., He, J., He, S., Li, Q., Shoemaker, B. A., Thiessen, P. A., Yu, B., et al. (2019) · Nucleic Acids Research · doi:10.1093/nar/gky1033 · Cited by 3,249
Applications of machine learning in drug discovery and development
Vamathevan, J., Clark, D., Czodrowski, P., Dunham, I., Ferran, E., Lee, G., Li, B., Madabhushi, A., Shah, P., Spitzer, M., et al. (2019) · Nature Reviews Drug Discovery · doi:10.1038/s41573-019-0024-5 · Cited by 3,228
ZINC 15 – Ligand Discovery for Everyone
Sterling, T., & Irwin, J. J. (2015) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.5b00559 · Cited by 3,224
Goodford, P. J. (1985) · Journal of Medicinal Chemistry · doi:10.1021/jm00145a002 · Cited by 2,634
Trends in GPCR drug discovery: new agents, targets and indications
Hauser, A. S., Attwood, M. M., Rask-Andersen, M., Schiöth, H. B., & Gloriam, D. E. (2017) · Nature Reviews Drug Discovery · doi:10.1038/nrd.2017.178 · Cited by 2,627
Directory of useful decoys, enhanced (DUD-E): better ligands and decoys for better benchmarking.
Mysinger, M. M., Carchia, M., Irwin, J. J., & Shoichet, B. K. (2012) · Journal of Medicinal Chemistry · doi:10.1021/jm300687e · Cited by 2,412
Gaulton, A., Hersey, A., Nowotka, M., Bento, A. P., Chambers, J., Mendez, D., Mutowo, P., Atkinson, F., Bellis, L. J., Cibrián-Uhalte, E., et al. (2017) · Nucleic Acids Research · doi:10.1093/nar/gkw1074 · Cited by 2,330
Cited by
Mind the Gap—Deciphering GPCR Pharmacology Using 3D Pharmacophores and Artificial Intelligence
(2022) · doi:10.3390/ph15111304 · Cited by 15
Stevenson, G. A., Kirshner, D., Bennion, B. J., Yang, Y., Zhang, X., Zemla, A., Torres, M. W., Epstein, A., Jones, D., Kim, H., et al. (2023) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.3c00722 · Cited by 7
Metadata
- Authors
- Schaller, D., Šribar, D., Noonan, T., Deng, L., Nguyen, T. N., Pach, S., Machalz, D., Bermudez, M., & Wolber, G.
- Venue
- WIREs Computational Molecular Science
- Publisher
- Wiley
- Published
- 2020-02-26
- Volume
- 10
- Issue
- 4
- ISSN
- 1759-0876 (ISSN-L)
- 1759-0884
- OpenAlex
- W3006781192
- Cited by
- 269
- References
- 102
- FWCI
- 14.89
- Type
- journal article
- Language
- en
- Open access
- hybrid
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- Schaller, D., Šribar, D., Noonan, T., Deng, L., Nguyen, T. N., Pach, S., Machalz, D., Bermudez, M., & Wolber, G. (2020). Next generation 3D pharmacophore modeling. WIREs Computational Molecular Science. https://doi.org/10.1002/wcms.1468
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