| 1. |
- Brandmaier, Stefan, et al.
(författare)
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An evaluation of experimental design in QSAR modelling utilizing the k-medoid clustering
- 2012
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Ingår i: Journal of Chemometrics. - : Wiley. - 0886-9383 .- 1099-128X. ; 26:10, s. 509-517
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Tidskriftsartikel (refereegranskat)abstract
- A reliable selection of a representative subset of chemical compounds has been reported to be crucial for numeroustasks in computational chemistry and chemoinformatics. We investigated the usability of an approach on the basisof the k-medoid algorithm for this task and in particular for experimental design and the split between training andvalidation set. We therefore compared the performance of models derived from such a selection to that of modelsderived using several other approaches, such as space-filling design and D-optimal design. We validated the performance on four datasets with different endpoints, representing toxicity, physicochemical properties and others.Compared with the models derived from the compounds selected by the other examined approaches, those derivedwith the k-medoid selection show a high reliability for experimental design, as their performance was constantlyamong the best for all examined datasets. Of all the models derived with all examined approaches, those derivedwith the k-medoid approach were the only ones that showed a significantly improved performance compared witha random selection, for all datasets, the whole examined range of selected compounds and for each dimensionalityof the search space.
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| 2. |
- Brandmaier, Stefan, et al.
(författare)
-
PLS-Optimal: A stepwise D-Optimal design based on latent variables
- 2012
-
Ingår i: Journal of Chemical Information and Modeling. - : American Chemical Society (ACS). - 1549-9596 .- 1549-960X. ; 52:4, s. 975-983
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Tidskriftsartikel (refereegranskat)abstract
- Several applications, such as risk assessment within REACH or drug discovery, require reliable methods for the design of experiments and efficient testing strategies. Keeping the number of experiments as low as possible is important from both a financial and an ethical point of view, as exhaustive testing of compounds requires significant financial resources and animal lives. With a large initial set of compounds, experimental design techniques can be used to select a representative subset for testing. Once measured, these compounds can be used to develop quantitative structure–activity relationship models to predict properties of the remaining compounds. This reduces the required resources and time. D-Optimal design is frequently used to select an optimal set of compounds by analyzing data variance. We developed a new sequential approach to apply a D-Optimal design to latent variables derived from a partial least squares (PLS) model instead of principal components. The stepwise procedure selects a new set of molecules to be measured after each previous measurement cycle. We show that application of the D-Optimal selection generates models with a significantly improved performance on four different data sets with end points relevant for REACH. Compared to those derived from principal components, PLS models derived from the selection on latent variables had a lower root-mean-square error and a higher Q2 and R2. This improvement is statistically significant, especially for the small number of compounds selected.
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