The DOI matches its Crossref record; 54 of the 54 references it cites were found in a registry or an index.
Combinatorial chemistry in drug discovery
Open accessAbstract
Several combinatorial methods have been developed to create focused or diverse chemical libraries with a wide range of linear or macrocyclic chemical molecules: peptides, non-peptide oligomers, peptidomimetics, small-molecules, and natural product-like organic molecules. Each combinatorial approach has its own unique high-throughput screening and encoding strategy. In this article, we provide a brief overview of combinatorial chemistry in drug discovery with emphasis on recently developed new technologies for design, synthesis, screening and decoding of combinatorial library. Examples of successful application of combinatorial chemistry in hit discovery and lead optimization are given. The limitations and strengths of combinatorial chemistry are also briefly discussed. We are now in a better position to truly leverage the power of combinatorial technologies for the discovery and development of next-generation drugs.
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Cites
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Cited by
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Big data and artificial intelligence modeling for drug discovery
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Metadata
- Authors
- Liu, R., Li, X., & Lam, K. S.
- Venue
- Current Opinion in Chemical Biology
- Publisher
- Elsevier BV
- Published
- 2017-06
- Volume
- 38
- Pages
- 117-126
- ISSN
- 1367-5931 (ISSN-L)
- OpenAlex
- W2612027503
- Cited by
- 332
- References
- 54
- FWCI
- 1.95
- Type
- journal article
- Language
- en
- Open access
- green
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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