A PLS kernel algorithm for data sets with many variables and fewer objects. Part 1: Theory and algorithm

Rännar, Stefan; Lindgren, Fredrik; Geladi, Paul; Wold, Svante

doi:10.1002/cem.1180080204

Cited by 282 publications

(152 citation statements)

References 15 publications

(3 reference statements)

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“…70 3D-QSAR CoMSIA Model. The PLS analysis implemented in SYBYL 8.0 71,72 was employed to obtain correlation between the CoMSIA similarity fields, which were used as independent explanatory variables, and the pIC 50 values which were used as target dependent variables. PLS was performed in two stages.…”

Section: ■ Materials and Methodsmentioning

confidence: 99%

Molecular Modeling on Pyrimidine-Urea Inhibitors of TNF-α Production: An Integrated Approach Using a Combination of Molecular Docking, Classification Techniques, and 3D-QSAR CoMSIA

Mouchlis

Melagraki

Mavromoustakos

et al. 2012

J. Chem. Inf. Model.

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Molecular docking, classification techniques, and 3D-QSAR CoMSIA were combined in a multistep framework with the ultimate goal of identifying potent pyrimidine-urea inhibitors of TNF-α production. Using the crystal structure of p38α, all the compounds were docked into the enzyme active site. The docking pose of each compound was subsequently used in a receptor-based alignment for the generation of the CoMSIA fields. "Active" and "inactive" compounds were used to build a Random Tree classification model using the docking score and the CoMSIA fields as input parameters. Domain of applicability indicated the compounds for which activity estimations can be accepted with confidence. For the active compounds, a 3D-QSAR CoMSIA model was subsequently built to accurately estimate the IC 50 values. This novel multistep framework gives insight into the structural characteristics that affect the binding and the inhibitory activity of these analogues on p38α MAP kinase, and it can be extended to other classes of small-molecule inhibitors. In addition, the simplicity of the proposed approach provides expansion to its applicability such as in virtual screening procedures.

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Section: ■ Materials and Methodsmentioning

confidence: 99%

Molecular Modeling on Pyrimidine-Urea Inhibitors of TNF-α Production: An Integrated Approach Using a Combination of Molecular Docking, Classification Techniques, and 3D-QSAR CoMSIA

Mouchlis

Melagraki

Mavromoustakos

et al. 2012

J. Chem. Inf. Model.

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“…The best grid spacing results were obtained with a 1.0 Å lattice spacing. Such late value was considered an optimum value since higher precision in evaluating the 3D field by introducing a finer grid would have resulted in an increase of so-called "brown noise" caused by the grid size sensitivity of the statistical technique used for generating the models [36].…”

Section: Alignment Rulementioning

confidence: 99%

Diosgenone Synthesis, Anti-Malarial Activity and QSAR of Analogues of This Natural Product

et al. 2013

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Solanum nudum Dunal steroids have been reported as being antimalarial compounds; however, their concentration in plants is low, meaning that the species could be threatened by over-harvesting for this purpose. Swern oxidation was used for hemisynthesis of diosgenone (one of the most active steroidal sapogenin diosgenin compounds). Eighteen structural analogues were prepared; three of them were found to be more active than diosgenone (IC 50 27.9 μM vs. 10.1 μM, 2.9 μM and 11.3 μM). The presence of a 4-en-3-one grouping in the A-ring of the compounds seems to be indispensable for antiplasmodial activity; progesterone (having the same functional group in the steroid A-ring) has also displayed antiplasmodial activity. Quantitative correlations between molecular structure and bioactivity were thus explored in diosgenone and several derivatives using well-established 3D-QSAR techniques. The models showed that combining electrostatic (70%) and steric (30%) fields can explain most variance regarding OPEN ACCESSMolecules 2013, 18 3357 compound activity. Malarial parasitemia in mice became reduced by oral administration of two diosgenone derivatives.

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“…The main difference between the two methods is that, PLSR uses the information available from both the response and predictor variables when projecting into the pdimensional subspace, which entails that PLSR is based on a supervised dimension reduction technique. The kernelised version of the PLSR algorithm follows from the algorithm presented in Ränner et al [34]. This results in the following estimate of the Kalman gain matrix:…”

Section: Kernel Principal Component Regressionmentioning

confidence: 99%

“…Ränner et al [34] outline an efficient procedure for solving this problem when n x and n d are larger than n e . This gives the following EnKF updating scheme based on kernel PLSR:…”

Section: Kernel Principal Component Regressionmentioning

confidence: 99%

Ensemble Kalman filtering for non-linear likelihood models using kernel-shrinkage regression techniques

Sætrom

Omre

2011

Comput Geosci

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One of the major limitations of the classical ensemble Kalman filter (EnKF) is the assumption of a linear relationship between the state vector and the observed data. Thus, the classical EnKF algorithm can suffer from poor performance when considering highly non-linear and non-Gaussian likelihood models. In this paper, we have formulated the EnKF based on kernel-shrinkage regression techniques. This approach makes it possible to handle highly non-linear likelihood models efficiently. Moreover, a solution to the preimage problem, essential in previously suggested EnKF schemes based on kernel methods, is not required. Testing the suggested procedure on a simple, illustrative problem with a non-linear likelihood model, we were able to obtain good results when the classical EnKF failed.

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A PLS kernel algorithm for data sets with many variables and fewer objects. Part 1: Theory and algorithm

Cited by 282 publications

References 15 publications

Molecular Modeling on Pyrimidine-Urea Inhibitors of TNF-α Production: An Integrated Approach Using a Combination of Molecular Docking, Classification Techniques, and 3D-QSAR CoMSIA

Molecular Modeling on Pyrimidine-Urea Inhibitors of TNF-α Production: An Integrated Approach Using a Combination of Molecular Docking, Classification Techniques, and 3D-QSAR CoMSIA

Diosgenone Synthesis, Anti-Malarial Activity and QSAR of Analogues of This Natural Product

Ensemble Kalman filtering for non-linear likelihood models using kernel-shrinkage regression techniques

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