Uniform Coverage Designs for Molecule Selection

Lam, Raymond; Welch, William J.; Young, S. Stanley

doi:10.1198/004017002317375055

Cited by 34 publications

(52 citation statements)

References 21 publications

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“…By weighting the design region according to experimenters' interest, we can reduce the effective size of the design space, making space-filling more feasible. In this sense, we suggest that weighting the design region has similar benefits to focussing on the space-filling of projections (see Lam et al, 2002 …”

Section: Discussionmentioning

confidence: 99%

Weighted space-filling designs

Bowman

Woods

2013

Journal of Simulation

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Many computer models or simulators have probabilistic dependencies between their input variables, which if not accounted for during design selection may result in a large numbers of simulator runs being required for analysis. We propose a method which incorporates known dependencies between input variables into design selection for simulators and demonstrate the benefits of this approach via a simulator for atmospheric dispersion. We quantify the benefit of the new techniques over standard space-filling and Monte Carlo simulation. The proposed methods are adaptations of computer-generated spread and coverage space-filling designs, with "distance" between two input points redefined to include a weight function. This weight function reflects any known multivariate dependencies between input variables and prior information on the design region. The methods can include quantitative and qualitative variables, and different types of prior information. Novel graphical methods, adapted from fraction of design space plots, are used to assess and compare the designs.

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Section: Discussionmentioning

confidence: 99%

Weighted space-filling designs

Bowman

Woods

2013

Journal of Simulation

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“…A SpaceFill program using uniform coverage design [19] was used for diverse chemical selection. A file with data was uploaded in the program and number of desired chemicals (n ¼ 25) was specified to run this program at default setting.…”

Section: Uniform Coverage Designmentioning

confidence: 99%

Selection of Appropriate Training Set of Chemicals for Modeling Dermal Permeability Using Uniform Coverage Design

et al. 2009

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In silico approaches to model dermal permeability require a training set of chemicals. Selection of an appropriate set of chemicals is the key factor in the precision of a model. The objective is to develop a training set of chemicals representing a wider chemical space relevant to biological activity such as dermal permeability and compare it with the currently used training set of chemicals by our laboratory, which was based on a subjective selection process. Wider chemical space refers to structurally diverse chemicals with a wide range of all the descriptor values. A parent dataset of 4098 chemicals with 5 solvatochromic descriptors obtained from ADME boxes database was used for this study. The approach for diverse chemical selection was performed using uniform coverage design (UCD) run by SpaceFill program and compared with a cluster analysis using SAS. Five sets of 25 chemicals were obtained from the design and evaluated based on gas chromatographic assay amenability and representation of structurally diverse group. A final set of 25 chemicals to be used for modeling dermal permeability was selected, based on aforementioned criteria. Graphical plot of the principal components demonstrated that currently used training set of chemicals have narrow representation of parent dataset whereas the selected training set have appropriate representation in terms of chemical space. QSAR models were built from both training set of chemicals. The model based on the 25 selected chemicals (R 2 ¼ 0.83) performed equally if not slightly better then the model based on currently used training set of 32 chemicals (R 2 ¼ 0.82).

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“…With SF methods there is also a risk of oversampling of the inner areas of the experimental space. A related group of designs is the cell-and grid-based designs, often called uniform coverage (UC) designs [15]. In these designs the experimental domain is divided into cells or grids by binning of the variables.…”

Section: Introductionmentioning

confidence: 99%

Controlling coverage of D‐optimal onion designs and selections

Olsson

Gottfries

Wold

2004

Journal of Chemometrics

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Statistical molecular design (SMD) is a powerful approach for selection of compound sets in medicinal chemistry and quantitative structure-activity relationships (QSARs) as well as other areas. Two techniques often used in SMD are space-filling and D-optimal designs. Both on occasions lead to unwanted redundancy and replication. To remedy such shortcomings, a generalization of D-optimal selection was recently developed. This new method divides the compound candidate set into a number of subsets ('layers' or 'shells'), and a D-optimal selection is made from each layer. This improves the possibility to select representative molecular structures throughout any property space independently of requested sample size. This is important in complex situations where any given model is unlikely to be valid over the whole investigated domain of experimental conditions. The number of selected molecules can be controlled by varying the number of subsets or by altering the complexity of the model equation in each layer and/or the dependency of previous layers. The new method, called D-optimal onion design (DOOD), will allow the user to choose the model equation complexity independently of sample size while still avoiding unwarranted redundancy. The focus of the present work is algorithmic improvements of DOOD in comparison with classical D-optimal design. As illustrations, extended DOODs have been generated for two applications by in-house programming, including some modifications of the D-optimal algorithm. The performances of the investigated approaches are expected to differ depending on the number of principal properties of the compounds in the design, sample sizes and the investigated model, i.e. the aim of the design. QSAR models have been generated from the selected compound sets, and root mean squared error of prediction (RMSEP) values have been used as measures of performance of the different designs.

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Uniform Coverage Designs for Molecule Selection

Cited by 34 publications

References 21 publications

Weighted space-filling designs

Weighted space-filling designs

Selection of Appropriate Training Set of Chemicals for Modeling Dermal Permeability Using Uniform Coverage Design

Controlling coverage of D‐optimal onion designs and selections

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