Optimum Search Strategies or Novel 3D Molecular Descriptors: is there a Stalemate?

Author(s): Yovani Marrero-Ponce, César R. García-Jacas, Stephen J . Barigye, José R. Valdés-Martiní, Oscar Miguel Rivera-Borroto, Ricardo W. Pino-Urias, Néstor Cubillán, Ysaías J. Alvarado, Huong Le-Thi-Thu

Journal Name: Current Bioinformatics

Volume 10 , Issue 5 , 2015

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The present manuscript describes a novel 3D-QSAR alignment free method (QuBiLS-MIDAS Duplex) based on algebraic bilinear, quadratic and linear forms on the kth two-tuple spatial-(dis)similarity matrix. Generalization schemes for the inter-atomic spatial distance using diverse (dis)-similarity measures are discussed. On the other hand, normalization approaches for the two-tuple spatial-(dis)similarity matrix by using simple- and double-stochastic and mutual probability schemes are introduced. With the aim of taking into consideration particular inter-atomic interactions in total or local-fragment indices, path and length cut-off constraints are used. Also, in order to generalize the use of the linear combination of atom-level indices to yield global (molecular) definitions, a set of aggregation operators (invariants) are applied. A Shannon’s entropy based variability study for the proposed 3D algebraic form-based indices and the DRAGON molecular descriptor families demonstrates superior performance for the former. A principal component analysis reveals that the novel indices codify structural information orthogonal to those captured by the DRAGON indices. Finally, a QSAR study for the binding affinity to the corticosteroidbinding globulin using Cramer’s steroid database is performed. From this study, it is revealed that the QuBiLS-MIDAS Duplex approach yields similar-to-superior performance statistics than all the 3D-QSAR methods reported in the literature reported so far, even with lower degree of freedom, using both the 31 steroids as the training set and the popular division of Cramer’s database in training [1-21] and test sets [22-31]. It is thus expected that this methodology provides useful tools for the diversity analysis of compound datasets and high-throughput screening structure–activity data.

Keywords: Alignment free method, aggregation operator, Minkowski distance matrix, principal component analysis, QuBiLSMIDAS, 3D-QSAR, two-tuple spatial-(dis)similarity matrix, TOMOCOMD-CARDD, variability analysis.

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Article Details

Year: 2015
Published on: 13 November, 2015
Page: [533 - 564]
Pages: 32
DOI: 10.2174/1574893610666151008011457
Price: $65

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