pubmed-article:19210777 | rdf:type | pubmed:Citation | lld:pubmed |
pubmed-article:19210777 | lifeskim:mentions | umls-concept:C0220908 | lld:lifeskim |
pubmed-article:19210777 | lifeskim:mentions | umls-concept:C1522290 | lld:lifeskim |
pubmed-article:19210777 | lifeskim:mentions | umls-concept:C0679199 | lld:lifeskim |
pubmed-article:19210777 | lifeskim:mentions | umls-concept:C0002045 | lld:lifeskim |
pubmed-article:19210777 | lifeskim:mentions | umls-concept:C1553497 | lld:lifeskim |
pubmed-article:19210777 | lifeskim:mentions | umls-concept:C2698650 | lld:lifeskim |
pubmed-article:19210777 | lifeskim:mentions | umls-concept:C1704419 | lld:lifeskim |
pubmed-article:19210777 | lifeskim:mentions | umls-concept:C1705938 | lld:lifeskim |
pubmed-article:19210777 | lifeskim:mentions | umls-concept:C1527178 | lld:lifeskim |
pubmed-article:19210777 | pubmed:dateCreated | 2009-4-24 | lld:pubmed |
pubmed-article:19210777 | pubmed:abstractText | Development of a fast and accurate scoring function in virtual screening remains a hot issue in current computer-aided drug research. Different scoring functions focus on diverse aspects of ligand binding, and no single scoring can satisfy the peculiarities of each target system. Therefore, the idea of a consensus score strategy was put forward. Integrating several scoring functions, consensus score re-assesses the docked conformations using a primary scoring function. However, it is not really robust and efficient from the perspective of optimization. Furthermore, to date, the majority of available methods are still based on single objective optimization design. | lld:pubmed |
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pubmed-article:19210777 | pubmed:language | eng | lld:pubmed |
pubmed-article:19210777 | pubmed:journal | http://linkedlifedata.com/r... | lld:pubmed |
pubmed-article:19210777 | pubmed:citationSubset | IM | lld:pubmed |
pubmed-article:19210777 | pubmed:status | MEDLINE | lld:pubmed |
pubmed-article:19210777 | pubmed:issn | 1471-2105 | lld:pubmed |
pubmed-article:19210777 | pubmed:author | pubmed-author:InEE | lld:pubmed |
pubmed-article:19210777 | pubmed:author | pubmed-author:JiangHualiang... | lld:pubmed |
pubmed-article:19210777 | pubmed:author | pubmed-author:KangLingL | lld:pubmed |
pubmed-article:19210777 | pubmed:author | pubmed-author:LiuXiaofengX | lld:pubmed |
pubmed-article:19210777 | pubmed:author | pubmed-author:LiHonglinH | lld:pubmed |
pubmed-article:19210777 | pubmed:author | pubmed-author:WangXichengX | lld:pubmed |
pubmed-article:19210777 | pubmed:author | pubmed-author:ZhengMingyueM | lld:pubmed |
pubmed-article:19210777 | pubmed:author | pubmed-author:ZhangHaileiH | lld:pubmed |
pubmed-article:19210777 | pubmed:issnType | Electronic | lld:pubmed |
pubmed-article:19210777 | pubmed:volume | 10 | lld:pubmed |
pubmed-article:19210777 | pubmed:owner | NLM | lld:pubmed |
pubmed-article:19210777 | pubmed:authorsComplete | Y | lld:pubmed |
pubmed-article:19210777 | pubmed:pagination | 58 | lld:pubmed |
pubmed-article:19210777 | pubmed:dateRevised | 2010-11-18 | lld:pubmed |
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pubmed-article:19210777 | pubmed:year | 2009 | lld:pubmed |