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Improving structure-based virtual screening by multivariate analysis of scoring data
Uppsala universitet, Avdelningen för organisk farmaceutisk kemi.
Stockholm University, Sweden.
Vise andre og tillknytning
2003 (engelsk)Inngår i: Journal of Medicinal Chemistry, ISSN 0022-2623, E-ISSN 1520-4804, Vol. 46, nr 26, s. 5781-5789Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

hree different multivariate statistical methods, PLS discriminant analysis, rule-based methods, and Bayesian classification, have been applied to multidimensional scoring data from four different target proteins: estrogen receptor alpha (ERalpha), matrix metalloprotease 3 (MMP3), factor Xa (fXa), and acetylcholine esterase (AChE). The purpose was to build classifiers able to discriminate between active and inactive compounds, given a structure-based virtual screen. Seven different scoring functions were used to generate the scoring matrices. The classifiers were compared to classical consensus scoring and single scoring functions. The classifiers show a superior performance, with rule-based methods being most effective. The precision of correctly predicting an active compound is about 90% for three of the targets and about 25% for acetylcholine esterase. On the basis of these results, a new two-stage approach is suggested for structure-based virtual screening where limited activity information is available.

sted, utgiver, år, opplag, sider
American Chemical Society (ACS), 2003. Vol. 46, nr 26, s. 5781-5789
HSV kategori
Identifikatorer
URN: urn:nbn:se:kth:diva-221507DOI: 10.1021/jm030896tISI: 000187243700024PubMedID: 14667231Scopus ID: 2-s2.0-0346996357OAI: oai:DiVA.org:kth-221507DiVA, id: diva2:1175285
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QC 20180119

Tilgjengelig fra: 2008-10-10 Laget: 2018-01-17 Sist oppdatert: 2018-01-19bibliografisk kontrollert

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Boström, Henrik
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