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Improving structure...
Improving structure-based virtual screening by multivariate analysis of scoring data
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- Jacobsson, Micael (författare)
- Uppsala universitet, Avdelningen för organisk farmaceutisk kemi
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Lidén, Per (författare)
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Stjernschantz, Eva (författare)
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- Boström, Henrik (författare)
- Stockholm University, Sweden
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Norinder, Ulf (författare)
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(creator_code:org_t)
- 2003-11-20
- 2003
- Engelska.
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Ingår i: Journal of Medicinal Chemistry. - : American Chemical Society (ACS). - 0022-2623 .- 1520-4804. ; 46:26, s. 5781-5789
- Relaterad länk:
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https://urn.kb.se/re...
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https://doi.org/10.1...
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Abstract
Ämnesord
Stäng
- 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.
Ämnesord
- NATURVETENSKAP -- Data- och informationsvetenskap (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences (hsv//eng)
- MEDICIN OCH HÄLSOVETENSKAP -- Medicinsk bioteknologi -- Medicinsk bioteknologi (hsv//swe)
- MEDICAL AND HEALTH SCIENCES -- Medical Biotechnology -- Medical Biotechnology (hsv//eng)
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