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

Title:	Optimum Search Strategies or Novel 3D Molecular Descriptors: Is there a Stalemate?
Authors:	Le, Thi Thu Huong
Keywords:	Alignment free method;Aggregation operator;Minkowski distance matrix;Principal component analysis;QuBiLS-MIDAS;3D-QSAR
Issue Date:	2015
Publisher:	Bentham Science Publishers

URI:	http://repository.vnu.edu.vn/handle/VNU_123/11502

The present manuscript describes a novel 3D-QSAR alignment free method (QuBiLS-MIDAS Duplex) based on algebraic bilinear, quadratic and linear forms on the k th 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 corticosteroid-binding 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 dat