Conformer clustering algorithm and its application for crown-type macrocycles

Vesterman, B.; Golender, V. E.; Golender, L. O.; Fuchs, Benzion

doi:10.1016/s0166-1280(96)90553-9

Cited by 6 publications

(5 citation statements)

References 5 publications

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“…1,2 A number of well-documented methods exist for clustering conformations. [3][4][5][6] Nearly all of these are based on some kind of distance measure between pairs of conformers, using either the distance matrix or a selection of dihedral angles. The most popular clustering methods for conformational selection are based on the hierarchical and Jarvis-Patrick schemes.…”

Section: Introductionmentioning

confidence: 99%

“…The word clustering generally implies the identification of groups such that the similarities within the groups are significantly greater than those between the groups. , A number of well-documented methods exist for clustering conformations. − Nearly all of these are based on some kind of distance measure between pairs of conformers, using either the distance matrix or a selection of dihedral angles. The most popular clustering methods for conformational selection are based on the hierarchical and Jarvis−Patrick schemes.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Metric and Multidimensional Scaling: Efficient Tools for Clustering Molecular Conformations

Fehér¹,

Schmidt²

2001

J. Chem. Inf. Comput. Sci.

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The application of metric and multidimensional scaling to conformer ensembles was demonstrated in this work. An automated process was devised to cluster and assign group memberships and cluster representatives. The method allows rapid clustering, leading to intuitive results that can be visually inspected. Multidimensional scaling was found to be superior to metric scaling for clustering conformers. The performance of different hierarchical clustering algorithms was compared using multidimensional plots, and the group average method was found to perform best.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Metric and Multidimensional Scaling: Efficient Tools for Clustering Molecular Conformations

Fehér¹,

Schmidt²

2001

J. Chem. Inf. Comput. Sci.

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“…Finding an adequate stop criterion for the hierarchical clustering is a difficult problem. Many criterions are based on an arbitrary fixed critical distance threshold, , or calculate F as the ratio of the sample variance between clusters to that within clusters, and then calculate the probability of F to exceed a fixed probability level . The optimal selection of such arbitrary values is difficult to do in a virtual screening context.…”

Section: Methodsmentioning

confidence: 99%

Assessing the Geometric Diversity of Cytochrome P450 Ligand Conformers by Hierarchical Clustering with a Stop Criterion

Meslamani

André

Petitjean

2009

J. Chem. Inf. Model.

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An algorithm is presented, which exhibits a computed number of rigid conformers of an input small molecule, covering the geometric diversity in the conformational space, with minimal structural redundancy. The algorithm calls a conformer generator, then performs an agglomerative hierarchical clustering with the modified clustering gain as the stop criterion. The number of classes is computed without an arbitrary parameter. A representative conformer is selected in each class, and nonrepresentative conformers are discarded. For illustration, the algorithm has been applied on a database containing 70 ligands of the cytochrome CYP 3A4, showing that the structural flexibility of each ligand is indeed handled via a small number of its representative conformers. The method is valid for all small molecules.

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“…6 This approach may lose some important information about local states and transitions between them. 7,8 For this reason, some new approaches based on network method 5,7,[9][10][11][12][13][14][15][16][17] and graph theory 2,4,8,18,19 have been developed to investigate the freeenergy surface of peptides and protein. 20 For examples, Duan and Kollman 9 developed a clustering method to analyze a 1-ms simulation trajectory of the 36-residue peptide (HP-36).…”

Section: Introductionmentioning

confidence: 99%

Improvements of network approach for analysis of the folding free‐energy surface of peptides and proteins

2010

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Folding network is an effective approach to investigate the high-dimensional free-energy surface of peptide and protein folding, and it can avoid the limitations of the projected free-energy surface based on two-order parameters. In this article, we present improvements of the effectiveness and accuracy of the folding network analysis based on Markov cluster (MCL) algorithm. We used this approach to investigate the folding free-energy surface of the beta-hairpin peptide trpzip2 and found the folding network is able to determine the basins and folding paths of trpzip2 more clearly and accurately than the two-dimensional free-energy surface.

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Conformer clustering algorithm and its application for crown-type macrocycles

Cited by 6 publications

References 5 publications

Metric and Multidimensional Scaling: Efficient Tools for Clustering Molecular Conformations

Metric and Multidimensional Scaling: Efficient Tools for Clustering Molecular Conformations

Assessing the Geometric Diversity of Cytochrome P450 Ligand Conformers by Hierarchical Clustering with a Stop Criterion

Improvements of network approach for analysis of the folding free‐energy surface of peptides and proteins

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