Various conformation-generating algorithms are commonly used in commercially available programs and open source wares. Therefore, the selection of representative conformers is valuable and very important in the 3D-QSAR model, pharmacophore model, proteinРІР‚вЂњligand docking, and Bayesian classification model from 3D fingerprints. The use of clustering algorithms to group similar conformations is the most appropriate data mining technique to distill the structural information from properties of an MD trajectory. Moreover, the clustering method can also be applied to molecular dynamics sampling simulation results.Р”В¬lustering algorithms used in a variety of situations, such as understanding virtual screening results, partitioning data sets into structurally homogeneous subsets for modeling, and picking representative chemical structures from individual clusters. The performance of the clustering algorithms was consistent over different transformation functions. RCDTC presented the maximum Dunn and omega-squared values of the four algorithms in addition to consistent reduction rate between the population size and the sample size. The clustering methods are simple and practical as they are fast and do not ask for any explicit parameters. Every algorithm could find representative conformers automatically without any user intervention, and they reduced the data to 14РІР‚вЂњ19% of the original values within 1.13Р’В s per sample at the most. The evaluation includes the reduction (abstraction) rate of the data, correlation between the sizes of the population and the samples, the computational complexity and the memory usage as well. ConclusionsР”Вunn index, DaviesРІР‚вЂњBouldin index, Eta-squared values and omega-squared values were used to evaluate the clustering algorithms with respect to the compactness and the explanatory power. The directed dissimilarity matrix becomes the only input to the clustering algorithms. In the network, the representative conformer group could be resampled for four kinds of algorithms with implicit parameters. This approach used to evaluate the performance of the well-known clustering algorithms by comparison in terms of generating representative conformer ensembles and test them over different matrix transformation functions considering the stability.

RMSD was the important descriptor (variable) of each column of the NР’В Р“вЂ” N matrix considered as N variables describing the relationship (network) between the conformer (in a row) and the other N conformers. In this work, we have attempted to make clustering algorithms, which could find reasonable number of representative conformer ensembles automatically with asymmetric dissimilarity matrix generated from openeye tool kit. Since a number of diverse conformational sampling algorithm exist, which exhaustively generate enough conformers, however model building methods relies on explicit number of common conformers. The accuracy of any 3D-QSAR, Pharmacophore and 3D-similarity based chemometric target fishing models are highly dependent on a reasonable sample of active conformations.

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