Ligand and structure-based discovery of phosphorus-containing compounds as potential metalloproteinase inhibitors.

In this study, a methodology is proposed, combining ligand- and structure-based virtual screening tools, for the identification of phosphorus-containing compounds as inhibitors of zinc metalloproteases. First, we use Dragon molecular descriptors to develop a Linear Discriminant Analysis classification model, which is widely validated according to the OECD principles. This model is simple, robust, stable and has good discriminating power. Furthermore, it has a defined applicability domain and it is used for virtual screening of the DrugBank database. Second, docking experiments are carried out on the identified compounds that showed good binding energies to the enzyme thermolysin. Considering the potential toxicity of phosphorus-containing compounds, their toxicological profile is evaluated according to Protox II. Of the five molecules evaluated, two show carcinogenic and mutagenic potential at small LD50, not recommended as drugs, while three of them are classified as non-toxic, and could constitute a starting point for the development of new vasoactive metalloprotease inhibitor drugs. According to molecular dynamics simulation, two of them show stable interactions with the active site maintaining coordination with the metal. A high agreement is evident between QSAR, docking and molecular dynamics results, demonstrating the potentialities of the combination of these tools.

Tensor algebra-based geometric methodology to codify central chirality on organic molecules.

A novel mathematical procedure to codify chiral features of organic molecules in the QuBiLS-MIDAS framework is introduced. This procedure constitutes a generalization to that commonly used to date, where the values 1 and -1 (correction factor) are employed to weight the molecular vectors when each atom is labelled as R (rectus) or S (sinister) according to the Cahn-Ingold-Prelog rules. Therefore, values in the range [Formula: see text] with steps equal to 0.25 may be accounted for. The atoms labelled R or S can have negative and positive values assigned (e.g. -3 for an R atom and 1 for an S atom, or vice versa), opposed values (e.g. -3 for an R atom and 3 for an S atom, or vice versa), positive values (e.g. 3 for an R atom and 1 for an S atom) or negative values (e.g. -3 for an R atom and -1 for an S atom). These proposed Chiral QuBiLS-MIDAS 3D-MDs are real numbers, non-symmetric and reduced to 'classical' (non-chiral) QuBiLS-MIDAS 3D-MDs when symmetry is not codified (correction factor equal to zero). In this report, only the factors with opposed values were considered with the purpose of demonstrating the feasibility of this proposal. From QSAR modelling carried out on four chemical datasets (Cramer's steroids, fenoterol stereoisomer derivatives, N-alkylated 3-(3-hydroxyphenyl)-piperidines, and perindoprilat stereoisomers), it was demonstrated that the use of several correction factors contributes to the building of models with greater robustness and predictive ability than those reported in the literature, as well as with respect to the models exclusively developed with QuBiLS-MIDAS 3D-MDs based on the factor 1 | -1. In conclusion, it can be stated that this novel strategy constitutes a suitable alternative to computed chirality-based descriptors, contributing to the development of good models to predict properties depending on symmetry.

Exploring the QSAR's predictive truthfulness of the novel N-tuple discrete derivative indices on benchmark datasets.

Graph derivative indices (GDIs) have recently been defined over N-atoms (N = 2, 3 and 4) simultaneously, which are based on the concept of derivatives in discrete mathematics (finite difference), metaphorical to the derivative concept in classical mathematical analysis. These molecular descriptors (MDs) codify topo-chemical and topo-structural information based on the concept of the derivative of a molecular graph with respect to a given event (S) over duplex, triplex and quadruplex relations of atoms (vertices). These GDIs have been successfully applied in the description of physicochemical properties like reactivity, solubility and chemical shift, among others, and in several comparative quantitative structure activity/property relationship (QSAR/QSPR) studies. Although satisfactory results have been obtained in previous modelling studies with the aforementioned indices, it is necessary to develop new, more rigorous analysis to assess the true predictive performance of the novel structure codification. So, in the present paper, an assessment and statistical validation of the performance of these novel approaches in QSAR studies are executed, as well as a comparison with those of other QSAR procedures reported in the literature. To achieve the main aim of this research, QSARs were developed on eight chemical datasets widely used as benchmarks in the evaluation/validation of several QSAR methods and/or many different MDs (fundamentally 3D MDs). Three to seven variable QSAR models were built for each chemical dataset, according to the original dissection into training/test sets. The models were developed by using multiple linear regression (MLR) coupled with a genetic algorithm as the feature wrapper selection technique in the MobyDigs software. Each family of GDIs (for duplex, triplex and quadruplex) behaves similarly in all modelling, although there were some exceptions. However, when all families were used in combination, the results achieved were quantitatively higher than those reported by other authors in similar experiments. Comparisons with respect to external correlation coefficients (q2ext) revealed that the models based on GDIs possess superior predictive ability in seven of the eight datasets analysed, outperforming methodologies based on similar or more complex techniques and confirming the good predictive power of the obtained models. For the q2ext values, the non-parametric comparison revealed significantly different results to those reported so far, which demonstrated that the models based on DIVATI's indices presented the best global performance and yielded significantly better predictions than the 12 0-3D QSAR procedures used in the comparison. Therefore, GDIs are suitable for structure codification of the molecules and constitute a good alternative to build QSARs for the prediction of physicochemical, biological and environmental endpoints.

Discovering key residues of dengue virus NS2b-NS3-protease: New binding sites for antiviral inhibitors design.

The NS2B-NS3 protease is essential for the Dengue Virus (DENV) replication process. This complex constitutes a target for efficient antiviral discovery because a drug could inhibit the viral polyprotein processing. Furthermore, since the protease is highly conserved between the four Dengue virus serotypes, it is probable that a drug would be equally effective against all of them. In this article, a strategy is reported that allowed us to identify influential residues on the function of the Dengue NS2b-NS3 Protease. Moreover, this is a strategy that could be applied to virtually any protein for the search of alternative influential residues, and for non-competitive inhibitor development. First, we incorporated several features derived from computational alanine scanning mutagenesis, sequence, structure conservation, and other structure-based characteristics. Second, these features were used as variables to obtain a multilayer perceptron model to identify defined groups (clusters) of key residues as possible candidate pockets for binding sites of new leads on the DENV protease. The identified residues included: i) amino acids close to the beta sheet-loop-beta sheet known to be important in its closed conformation for NS2b ii) residues close to the active site, iii) several residues evenly spread on the NS2b-NS3 contact surface, and iv) some inner residues most likely related to the overall stability of the protease. In addition, we found concordance on our list of residues with previously identified amino acids part of a highly conserved peptide studied for vaccine development.

Conformation-dependent QSAR approach for the prediction of inhibitory activity of bromodomain modulators.

Epigenetic drug discovery is a promising research field with growing interest in the scientific community, as evidenced by the number of publications and the large amount of structure-epigenetic activity information currently available in the public domain. Computational methods are valuable tools to analyse and understand the activity of large compound collections from their structural information. In this manuscript, QSAR models to predict the inhibitory activity of a diverse and heterogeneous set of 88 organic molecules against the bromodomains BRD2, BRD3 and BRD4 are presented. A conformation-dependent representation of the chemical structures was established using the RDKit software and a training and test set division was performed. Several two-linear and three-linear QuBiLS-MIDAS molecular descriptors ( www.tomocomd.com ) were computed to extract the geometric structural features of the compounds studied. QuBiLS-MIDAS-based features sets, to be used in the modelling, were selected using dimensionality reduction strategies. The multiple linear regression procedure coupled with a genetic algorithm were employed to build the predictive models. Regression models containing between 6 to 9 variables were developed and assessed according to several internal and external validation methods. Analyses of outlier compounds and the applicability domain for each model were performed. As a result, the models against BRD2 and BRD3 with 8 variables and the model with 9 variables against BRD4 were those with the best overall performance according to the criteria accounted for. The results obtained suggest that the models proposed will be a good tool for studying the inhibitory activities of drug candidates against the bromodomains considered during epigenetic drug discovery.

N-tuple topological/geometric cutoffs for 3D N-linear algebraic molecular codifications: variability, linear independence and QSAR analysis.

Novel N-tuple topological/geometric cutoffs to consider specific inter-atomic relations in the QuBiLS-MIDAS framework are introduced in this manuscript. These molecular cutoffs permit the taking into account of relations between more than two atoms by using (dis-)similarity multi-metrics and the concepts related with topological and Euclidean-geometric distances. To this end, the kth two-, three- and four-tuple topological and geometric neighbourhood quotient (NQ) total (or local-fragment) spatial-(dis)similarity matrices are defined, to represent 3D information corresponding to the relations between two, three and four atoms of the molecular structures that satisfy certain cutoff criteria. First, an analysis of a diverse chemical space for the most common values of topological/Euclidean-geometric distances, bond/dihedral angles, triangle/quadrilateral perimeters, triangle area and volume was performed in order to determine the intervals to take into account in the cutoff procedures. A variability analysis based on Shannon's entropy reveals that better distribution patterns are attained with the descriptors based on the cutoffs proposed (QuBiLS-MIDAS NQ-MDs) with regard to the results obtained when all inter-atomic relations are considered (QuBiLS-MIDAS KA-MDs - 'Keep All'). A principal component analysis shows that the novel molecular cutoffs codify chemical information captured by the respective QuBiLS-MIDAS KA-MDs, as well as information not captured by the latter. Lastly, a QSAR study to obtain deeper knowledge of the contribution of the proposed methods was carried out, using four molecular datasets (steroids (STER), angiotensin converting enzyme (ACE), thermolysin inhibitors (THER) and thrombin inhibitors (THR)) widely used as benchmarks in the evaluation of several methodologies. One to four variable QSAR models based on multiple linear regression were developed for each compound dataset following the original division into training and test sets. The results obtained reveal that the novel cutoff procedures yield superior performances relative to those of the QuBiLS-MIDAS KA-MDs in the prediction of the biological activities considered. From the results achieved, it can be suggested that the proposed N-tuple topological/geometric cutoffs constitute a relevant criteria for generating MDs codifying particular atomic relations, ultimately useful in enhancing the modelling capacity of the QuBiLS-MIDAS 3D-MDs.

QuBiLs-MAS method in early drug discovery and rational drug identification of antifungal agents.

The QuBiLs-MAS approach is used for the in silico modelling of the antifungal activity of organic molecules. To this effect, non-stochastic (NS) and simple-stochastic (SS) atom-based quadratic indices are used to codify chemical information for a comprehensive dataset of 2478 compounds having a great structural variability, with 1087 of them being antifungal agents, covering the broadest antifungal mechanisms of action known so far. The NS and SS index-based antifungal activity classification models obtained using linear discriminant analysis (LDA) yield correct classification percentages of 90.73% and 92.47%, respectively, for the training set. Additionally, these models are able to correctly classify 92.16% and 87.56% of 706 compounds in an external test set. A comparison of the statistical parameters of the QuBiLs-MAS LDA-based models with those for models reported in the literature reveals comparable to superior performance, although the latter were built over much smaller and less diverse datasets, representing fewer mechanisms of action. It may therefore be inferred that the QuBiLs-MAS method constitutes a valuable tool useful in the design and/or selection of new and broad spectrum agents against life-threatening fungal infections.

Comparative study to predict toxic modes of action of phenols from molecular structures.

Quantitative structure-activity relationship models for the prediction of mode of toxic action (MOA) of 221 phenols to the ciliated protozoan Tetrahymena pyriformis using atom-based quadratic indices are reported. The phenols represent a variety of MOAs including polar narcotics, weak acid respiratory uncouplers, pro-electrophiles and soft electrophiles. Linear discriminant analysis (LDA), and four machine learning techniques (ML), namely k-nearest neighbours (k-NN), support vector machine (SVM), classification trees (CTs) and artificial neural networks (ANNs), have been used to develop several models with higher accuracies and predictive capabilities for distinguishing between four MOAs. Most of them showed global accuracy of over 90%, and false alarm rate values were below 2.9% for the training set. Cross-validation, complementary subsets and external test set were performed, with good behaviour in all cases. Our models compare favourably with other previously published models, and in general the models obtained with ML techniques show better results than those developed with linear techniques. We developed unsupervised and supervised consensus, and these results were better than our ML models, the results of rule-based approach and other ensemble models previously published. This investigation highlights the merits of ML-based techniques as an alternative to other more traditional methods for modelling MOA.

Event-based criteria in GT-STAF information indices: theory, exploratory diversity analysis and QSPR applications.

Versatile event-based approaches for the definition of novel information theory-based indices (IFIs) are presented. An event in this context is the criterion followed in the "discovery" of molecular substructures, which in turn serve as basis for the construction of the generalized incidence and relations frequency matrices, Q and F, respectively. From the resultant F, Shannon's, mutual, conditional and joint entropy-based IFIs are computed. In previous reports, an event named connected subgraphs was presented. The present study is an extension of this notion, in which we introduce other events, namely: terminal paths, vertex path incidence, quantum subgraphs, walks of length k, Sach's subgraphs, MACCs, E-state and substructure fingerprints and, finally, Ghose and Crippen atom-types for hydrophobicity and refractivity. Moreover, we define magnitude-based IFIs, introducing the use of the magnitude criterion in the definition of mutual, conditional and joint entropy-based IFIs. We also discuss the use of information-theoretic parameters as a measure of the dissimilarity of codified structural information of molecules. Finally, a comparison of the statistics for QSPR models obtained with the proposed IFIs and DRAGON's molecular descriptors for two physicochemical properties log P and log K of 34 derivatives of 2-furylethylenes demonstrates similar to better predictive ability than the latter.