The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.
Natural Product-likeness Score and Its Application for Prioritization of Compound Libraries
Abstract
Natural products (NPs) have been optimized in a very long natural selection process for optimal interactions with biological macromolecules. NPs are therefore an excellent source of validated substructures for the design of novel bioactive molecules. Various cheminformatics techniques can provide useful help in analyzing NPs, and the results of such studies may be used with advantage in the drug discovery process. In the present study we describe a method to calculate the natural product-likeness score--a Bayesian measure which allows for the determination of how molecules are similar to the structural space covered by natural products. This score is shown to efficiently separate NPs from synthetic molecules in a cross-validation experiment. Possible applications of the NP-likeness score are discussed and illustrated on several examples including virtual screening, prioritization of compound libraries toward NP-likeness, and design of building blocks for the synthesis of NP-like libraries.
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Cites
The Elements of Statistical Learning
Hastie, T., Friedman, J., & Tibshirani, R. (2001) · Springer Series in Statistics · doi:10.1007/978-0-387-21606-5 · Cited by 24,162
Natural products as sources of new drugs over the last 25 years
Newman, D. J., & Cragg, G. M. (2007) · Journal of Natural Products · doi:10.1021/np068054v · Cited by 5,517
ZINC − A Free Database of Commercially Available Compounds for Virtual Screening
Irwin, J. J., & Shoichet, B. K. (2005) · Journal of Chemical Information and Modeling · doi:10.1021/ci049714+ · Cited by 4,058
DrugBank: a comprehensive resource for in silico drug discovery and exploration
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Diversity-oriented synthesis: Exploring the intersections between chemistry and biology
Tan, D. S. (2005) · Nature Chemical Biology · doi:10.1038/nchembio0705-74 · Cited by 605
Cited by
Ertl, P., & Schuffenhauer, A. (2009) · Journal of Cheminformatics · doi:10.1186/1758-2946-1-8 · Cited by 1,939
Self-citationDeep learning for molecular design—a review of the state of the art
Elton, D. C., Boukouvalas, Z., Fuge, M. D., & Chung, P. W. (2019) · Molecular Systems Design & Engineering · doi:10.1039/c9me00039a · Cited by 683
A Systematic Cheminformatics Analysis of Functional Groups Occurring in Natural Products
Ertl, P., & Schuhmann, T. (2019) · Journal of Natural Products · doi:10.1021/acs.jnatprod.8b01022 · Cited by 358
Self-citationShape-Based Generative Modeling for de Novo Drug Design
Skalic, M., Jiménez, J., Sabbadin, D., & De Fabritiis, G. (2019) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.8b00706 · Cited by 229
Modeling the expansion of virtual screening libraries
Lyu, J., Irwin, J. J., & Shoichet, B. K. (2023) · Nature Chemical Biology · doi:10.1038/s41589-022-01234-w · Cited by 193
Metadata
- Authors
- Ertl, P., Roggo, S., & Schuffenhauer, A.
- Venue
- Journal of Chemical Information and Modeling
- Publisher
- American Chemical Society (ACS)
- Published
- 2007-11-23
- Volume
- 48
- Issue
- 1
- Pages
- 68-74
- ISSN
- 1549-9596 (ISSN-L)
- 1549-960X
- OpenAlex
- W2064963922
- Cited by
- 387
- References
- 5
- FWCI
- 13.32
- Type
- journal article
- Language
- en
- Open access
- closed
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