The codification of chemical information using mathematical–computational methods to accelerate small-molecule drug discovery constitutes one of the fundamental tasks of mathematical chemistry. Lastly, a QSAR study for the binding affinity to the corticosteroid-binding globulin using Cramer’s steroid dataset is carried out.
A principal component analysis reveals that the QuBiLS-MAS approach captures chemical information orthogonal to that codified by the DRAGON descriptors. Firstly, a comparative Shannon’s entropy based variability study for the proposed QuBiLS-MAS and the DRAGON indices demonstrates superior performance for the former.
DRAGON V6 FOR MAC REVIEW SOFTWARE
The relevance of the novel extensions and generalizations implemented in this software is demonstrated through three studies. The QuBiLS-MAS software consists of two components: a desktop interface (GUI) and an API library allowing for the easy integration of the latter in chemoinformatics applications. This program was developed in Java v1.7 using the Chemistry Development Kit library (version 1.4.19). This software permits the parallel computation of the indices, contains a batch processing module and data curation functionalities. The QuBiLS-MAS module was designed as standalone software, in which extensions and generalizations of the former ToMoCoMD-CARDD 2D-algebraic indices are implemented, considering the following aspects: (a) two new matrix normalization approaches based on double-stochastic and mutual probability formalisms (b) topological constraints (cut-offs) to take into account particular inter-atomic relations (c) six additional atomic properties to be used as weighting schemes in the calculation of the molecular vectors (d) four new local-fragments to consider molecular regions of interest (e) number of lone-pair electrons in chemical structure defined by diagonal coefficients in matrix representations and (f) several aggregation operators ( invariants) applied over atom/bond-level descriptors in order to compute global indices. Therefore, the present manuscript introduces the QuBiLS-MAS (acronym for Quadratic, Bilinear and N-Linear mapS based on graph-theoretic electronic-density Matrices and Atomic weightingS) software designed to compute topological (0–2.5D) molecular descriptors based on bilinear, quadratic and linear algebraic forms for atom- and bond-based relations. However, this in house software barely offered the functionalities required in contemporary molecular modeling tasks, in addition to the inherent limitations that made its usability impractical.
To compute these MDs, a computational program with the same name was initially developed. These MDs have been successfully applied in the screening of chemical compounds of different therapeutic applications ranging from antimalarials, antibacterials, tyrosinase inhibitors and so on.
These MDs codify molecular information based on the bilinear, quadratic and linear algebraic forms and the graph- theoretical electronic- density and edge- adjacency matrices in order to consider atom- and bond-based relations, respectively. proposed algebraic formalisms for characterizing topological (2D) and chiral (2.5D) molecular features through atom- and bond-based ToMoCoMD-CARDD (acronym for Topological Molecular Computational Design-Computer Aided Rational Drug Design) molecular descriptors.
In previous reports, Marrero-Ponce et al.
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