Background In previous reports, Marrero-Ponce et al. 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. 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. 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. To compute these MDs, a computational program with the same name was initially developed. 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. 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.ResultsThe 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. This software permits the parallel computation of the indices, contains a batch processing module and data curation functionalities. This program was developed in Java v1.7 using the Chemistry Development Kit library (version 1.4.19). 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. The relevance of the novel extensions and generalizations implemented in this software is demonstrated through three studies. Firstly, a comparative Shannon’s entropy based variability study for the proposed QuBiLS-MAS and the DRAGON indices demonstrates superior performance for the former. A principal component analysis reveals that the QuBiLS-MAS approach captures chemical information orthogonal to that codified by the DRAGO...

The present manuscript introduces, for the first time, a novel 3D-QSAR alignment free method (QuBiLS-MIDAS) based on tensor concepts through the use of the three-linear and four-linear algebraic forms as specific cases of n-linear maps. To this end, the k(th) three-tuple and four-tuple spatial-(dis)similarity matrices are defined, as tensors of order 3 and 4, respectively, to represent 3Dinformation among "three and four" atoms of the molecular structures. Several measures (multi-metrics) to establish (dis)-similarity relations among "three and four" atoms are discussed, as well as, normalization schemes proposed for the n-tuple spatial-(dis)similarity matrices based on the simple-stochastic and mutual probability algebraic transformations. To consider specific interactions among atoms, both for the global and local indices, n-tuple path and length cut-off constraints are introduced. This algebraic scaffold can also be seen as a generalization of the vector-matrix-vector multiplication procedure (which is a matrix representation of the traditional linear, quadratic and bilinear forms) for the calculation of molecular descriptors and is thus a new theoretical approach with a methodological contribution. A variability analysis based on Shannon's entropy reveals that the best distributions are achieved with the ternary and quaternary measures corresponding to the bond and dihedral angles. In addition, the proposed indices have superior entropy behavior than the descriptors calculated by other programs used in chemo-informatics studies, such as, DRAGON, PADEL, Mold2, and so on. A principal component analysis shows that the novel 3D n-tuple indices codify the same information captured by the DRAGON 3D-indices, as well as, information not codified by the latter. A QSAR study to obtain deeper criteria on the contribution of the novel molecular parameters was performed for the binding affinity to the corticosteroid-binding globulin, using Cramer's steroid database. The achieved results reveal superior statistical parameters for the Bond Angle and Dihedral Angle approaches, consistent with the results obtained in variability analysis. Finally, the obtained QuBiLS-MIDAS models yield superior performances than all 3D-QSAR methods reported in the literature using the 31 steroids as training set, and for the popular division of Cramer's database in training (1-21) and test (22-31) sets, comparable to superior results in the prediction of the activity of the steroids are obtained. From the results achieved, it can be suggested that the proposed QuBiLS-MIDAS N-tuples indices are a useful tool to be considered in chemo-informatics studies.

No abstract

The features and theoretical background of a new and free computational program for chemometric analysis denominated IMMAN (acronym for Information theory-based CheMoMetrics ANalysis) are presented. This is multi-platform software developed in the Java programming language, designed with a remarkably user-friendly graphical interface for the computation of a collection of information-theoretic functions adapted for rank-based unsupervised and supervised feature selection tasks. A total of 20 feature selection parameters are presented, with the unsupervised and supervised frameworks represented by 10 approaches in each case. Several information-theoretic parameters traditionally used as molecular descriptors (MDs) are adapted for use as unsupervised rank-based feature selection methods. On the other hand, a generalization scheme for the previously defined differential Shannon's entropy is discussed, as well as the introduction of Jeffreys information measure for supervised feature selection. Moreover, well-known information-theoretic feature selection parameters, such as information gain, gain ratio, and symmetrical uncertainty are incorporated to the IMMAN software ( http://mobiosd-hub.com/imman-soft/ ), following an equal-interval discretization approach. IMMAN offers data pre-processing functionalities, such as missing values processing, dataset partitioning, and browsing. Moreover, single parameter or ensemble (multi-criteria) ranking options are provided. Consequently, this software is suitable for tasks like dimensionality reduction, feature ranking, as well as comparative diversity analysis of data matrices. Simple examples of applications performed with this program are presented. A comparative study between IMMAN and WEKA feature selection tools using the Arcene dataset was performed, demonstrating similar behavior. In addition, it is revealed that the use of IMMAN unsupervised feature selection methods improves the performance of both IMMAN and WEKA supervised algorithms. Graphic representation for Shannon's distribution of MD calculating software.

The present report introduces the QuBiLS-MIDAS software belonging to the ToMoCoMD-CARDD suite for the calculation of three-dimensional molecular descriptors (MDs) based on the two-linear (bilinear), three-linear, and four-linear (multilinear or N-linear) algebraic forms. Thus, it is unique software that computes these tensor-based indices. These descriptors, establish relations for two, three, and four atoms by using several (dis-)similarity metrics or multimetrics, matrix transformations, cutoffs, local calculations and aggregation operators. The theoretical background of these N-linear indices is also presented. The QuBiLS-MIDAS software was developed in the Java programming language and employs the Chemical Development Kit library for the manipulation of the chemical structures and the calculation of the atomic properties. This software is composed by a desktop user-friendly interface and an Abstract Programming Interface library. The former was created to simplify the configuration of the different options of the MDs, whereas the library was designed to allow its easy integration to other software for chemoinformatics applications. This program provides functionalities for data cleaning tasks and for batch processing of the molecular indices. In addition, it offers parallel calculation of the MDs through the use of all available processors in current computers. The studies of complexity of the main algorithms demonstrate that these were efficiently implemented with respect to their trivial implementation. Lastly, the performance tests reveal that this software has a suitable behavior when the amount of processors is increased. Therefore, the QuBiLS-MIDAS software constitutes a useful application for the computation of the molecular indices based on N-linear algebraic maps and it can be used freely to perform chemoinformatics studies.