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DC Field | Value | Language |
---|---|---|
dc.citation.endPage | 269 | - |
dc.citation.number | 5 | - |
dc.citation.startPage | 261 | - |
dc.citation.title | ACCOUNTS OF CHEMICAL RESEARCH | - |
dc.citation.volume | 35 | - |
dc.contributor.author | Grzybowski, BA | - |
dc.contributor.author | Ishchenko, AV | - |
dc.contributor.author | Shimada, J | - |
dc.contributor.author | Shakhnovich, EI | - |
dc.date.accessioned | 2023-12-22T11:38:06Z | - |
dc.date.available | 2023-12-22T11:38:06Z | - |
dc.date.created | 2020-07-14 | - |
dc.date.issued | 2002-05 | - |
dc.description.abstract | Computational methods are becoming increasingly used in the drug discovery process. In this Account, we review a novel computational method for lead discovery. This method, called CombiSMoG for "combinatorial small molecule growth", is based on two components: a fast and accurate knowledge-based scoring function used to predict binding affinities of protein-ligand complexes, and a Monte Carlo combinatorial growth algorithm that generates large numbers of low-free-energy ligands in the binding site of a protein. We illustrate the advantages of the method by describing its application in the design of picomolar inhibitors for human carbonic anhydrase. | - |
dc.identifier.bibliographicCitation | ACCOUNTS OF CHEMICAL RESEARCH, v.35, no.5, pp.261 - 269 | - |
dc.identifier.doi | 10.1021/ar970146b | - |
dc.identifier.issn | 0001-4842 | - |
dc.identifier.scopusid | 2-s2.0-0036001094 | - |
dc.identifier.uri | https://scholarworks.unist.ac.kr/handle/201301/33304 | - |
dc.identifier.url | https://pubs.acs.org/doi/10.1021/ar970146b | - |
dc.identifier.wosid | 000175775100002 | - |
dc.language | 영어 | - |
dc.publisher | AMER CHEMICAL SOC | - |
dc.title | From knowledge-based potentials to combinatorial lead design in silico | - |
dc.type | Article | - |
dc.description.isOpenAccess | FALSE | - |
dc.relation.journalWebOfScienceCategory | Chemistry, Multidisciplinary | - |
dc.relation.journalResearchArea | Chemistry | - |
dc.type.docType | Review | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.subject.keywordPlus | PROTEIN-LIGAND INTERACTIONS | - |
dc.subject.keywordPlus | DE-NOVO DESIGN | - |
dc.subject.keywordPlus | EMPIRICAL SCORING FUNCTION | - |
dc.subject.keywordPlus | DRUG DESIGN | - |
dc.subject.keywordPlus | STATISTICAL POTENTIALS | - |
dc.subject.keywordPlus | BINDING AFFINITIES | - |
dc.subject.keywordPlus | ORGANIC-MOLECULES | - |
dc.subject.keywordPlus | FORCE-FIELD | - |
dc.subject.keywordPlus | ENERGY | - |
dc.subject.keywordPlus | INHIBITORS | - |
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