Journal article
High confidence predictions of drug-drug interactions: predicting affinities for cytochrome P450 2C9 with multiple computational methods
Journal of medicinal chemistry, Vol.51(3), pp.648-654
02/14/2008
Handle:
https://hdl.handle.net/2376/104665
PMID: 18211009
Abstract
Four different models are used to predict whether a compound will bind to 2C9 with a K(i) value of less than 10 microM. A training set of 276 compounds and a diverse validation set of 50 compounds were used to build and assess each model. The modeling methods are chosen to exploit the differences in how training sets are used to develop the predictive models. Two of the four methods develop partitioning trees based on global descriptions of structure using nine descriptors. A third method uses the same descriptors to develop local descriptions that relate activity to structures with similar descriptor characteristics. The fourth method uses a graph-theoretic approach to predict activity based on molecular structure. When all of these methods agree, the predictive accuracy is 94%. An external validation set of 11 compounds gives a predictive accuracy of 91% when all methods agree.
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Details
- Title
- High confidence predictions of drug-drug interactions: predicting affinities for cytochrome P450 2C9 with multiple computational methods
- Creators
- Matthew G Hudelson - Department of Mathematics, Washington State University, Pullman, WA 99164-3113, USA. mhudelson@wsu.eduNikhil S KetkarLawrence B HolderTimothy J CarlsonChi-Chi PengBenjamin J WaldherJeffrey P Jones
- Publication Details
- Journal of medicinal chemistry, Vol.51(3), pp.648-654
- Academic Unit
- Chemistry, Department of; Mathematics and Statistics, Department of; Electrical Engineering and Computer Science, School of
- Publisher
- United States
- Grant note
- ES 09122 / NIEHS NIH HHS GM 32165 / NIGMS NIH HHS
- Identifiers
- 99900546841301842
- Language
- English
- Resource Type
- Journal article