Use of alignment-free molecular descriptors in diversity analysis and optimal sampling of molecular libraries
The selection of a sample of diverse compounds is a common strategy for exploring large molecular libraries. However, the success of such approach depends on the selection of relevant molecular descriptors and the use of appropriate sampling methods. In the context of pharmaceutical research, the molecular descriptors should be based on physicochemical properties related with the pharmacological behaviour of the compounds. In this sense, the alignment-free GRIND and VolSurf molecular descriptors are promising candidates since they have been successfully used in the modelling of both pharmacodynamic and pharmacokinetic properties of drugs. This work describes the use of such descriptors in the diversity sampling of a library of primary amines and compares the results with those obtained in a previous study that used quantum-mechanical descriptors. As in the previous work, principal component (PC) analysis was applied to reduce the dimensionality and remove redundant information of the original descriptors, and the compounds were sampled on the basis of k-means clustering on the space of the selected PCs. The results of the present study show that VolSurf and GRIND provide similar quality sampling regarding global features of the molecules such as hydrophilicity, however the topology of the compounds is considered differently. The similarity between particular compounds strongly depends on the original descriptors used. However all the sample selections done in the PC space after k-means clustering provide the same apparent diversity in comparison to the whole dataset. The results indicate that there is no best set of descriptors on a diversity basis. The selection of descriptors must be based on the drug features to be investigated.
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- 2.Zheng, W., Cho, S. J., Waller, C. L. and Tropsha, A., Rational combinatorial library design. 3. Simulated annealing guided evaluation (SAGE) of molecular diversity: A novel computational tool for universal library design and database mining, J. Chem. Inf. Comput. Sci., 39 (1999) 738–746.CrossRefGoogle Scholar
- 7.Tropsha, A. and Zheng, W., Rational principles of compound selection for combinatorial library design, Comb.Chem.High Throughput Screen., 5 (2002) 111–123.Google Scholar
- 10.Gutiérrez-de-Terán, H., Lozano, J. J., Segarra, V. and Sanz, F., Molecular diversity sample generation on the basis of quantum-mechanical computations and principal component analysis, Comb. Chem. High Throughput Screen., 5 (2002) 49–57.Google Scholar
- 11.Gillet, V. J., Background theory of molecular diversity, In Dean, P. M. and Lewis, R. A. (eds.), Molecular Diversity in Drug Design, Kluwer Academic Publishers, Dordrecht, 1999, pp. 43–65.Google Scholar
- 12.Xue, L. and Bajorath, J., Molecular descriptors in chemoinformatics, computational combinatorial chemistry, and virtual screening, Comb. Chem. High Throughput Screen., 3 (2000) 363–372.Google Scholar
- 16.Barnard, J. M., Downs, G. M., Von Scholley-Pfab, A. and Brown, R. D., Use of Markush structure analysis techniques for descriptor generation and clustering of large combinatorial libraries, J. Mol. Graph. Model., 18 (2000) 452–463.Google Scholar
- 34.AMSOL 6.5.2, Hawkins, G. D., Giesen, D. J., G. C., L., Chambers, C. C., Rossi, I., Storer, J. W., Rinaldi, D., Liotard, D. A., Cramer, C. J. and Truhlar, D. G., University of Minnesota, Minneapolis, 1997.Google Scholar
- 35.VolSurf 3.0.7c, Cruciani, G., Pastor, M. and Mecucci, S., Molecular Discovery Ltd., Perugia, 2002.Google Scholar
- 36.Almond 3.2.0, Cruciani, G., Fontaine, F. and Pastor, M., Molecular Discovery Ltd., Perugia, 2003.Google Scholar
- 38.SPSS 11.0.1, SPSS inc. Chicago, 2001.Google Scholar
- 39.Downs, G. M. and Barnard, J. M., Clustering methods and their uses in computational chemistry, In Lipkowitz, K. B. and Boyd, D. B. (eds.), Reviews in Computational Chemistry, Wiley-VCH, John Wiley & Sons, Inc., 2002, pp. 1–40.Google Scholar