Application of molecular topology to the prediction of antifungal activity for a set of dication-substituted carbazoles, furans and benzimidazoles

Quantitative structure-activity relationship models for compounds with anticonvulsant activity

Introduction: Third-generation antiepileptic drugs have seemingly failed to improve the global figures of seizure control and can still be regarded as symptomatic treatments. Quantitative structure-activity relationships (QSAR) can be used to guide hit-to-lead and lead optimization projects and applied to the large-scale virtual screening of chemical libraries. Areas covered: In this review, the authors cover reports on QSAR models related to antiepileptic drugs and drug targets in epilepsy, analyzing whether they refer to classic or non-classic QSAR and if they apply QSAR as a descriptive or predictive approach, among other considerations. The article finally focuses on a more detailed discussion of those predictive studies which include some sort of experimental validation, i.e. papers in which the reported models have been used to identify novel active compounds which have been tested in vitro and/or in vivo. Expert opinion: There are significant opportunities to apply the QSAR methodology to assist the discovery of more efficacious antiepileptic drugs. Considering the intrinsic complexity of the disorder, such applications should focus on state-of-the-art approximations (e.g. systemic, multi-target and multi-scale QSAR as well as ensemble and deep learning) and modeling the effects on novel drug targets and modern screening tools.

The application of molecular topology for ulcerative colitis drug discovery

INTRODUCTION

Although the therapeutic arsenal against ulcerative colitis has greatly expanded (including the revolutionary advent of biologics), there remain patients who are refractory to current medications while the safety of the available therapeutics could also be improved. Molecular topology provides a theoretic framework for the discovery of new therapeutic agents in a very efficient manner, and its applications in the field of ulcerative colitis have slowly begun to flourish. Areas covered: After discussing the basics of molecular topology, the authors review QSAR models focusing on validated targets for the treatment of ulcerative colitis, entirely or partially based on topological descriptors. Expert opinion: The application of molecular topology to ulcerative colitis drug discovery is still very limited, and many of the existing reports seem to be strictly theoretic, with no experimental validation or practical applications. Interestingly, mechanism-independent models based on phenotypic responses have recently been reported. Such models are in agreement with the recent interest raised by network pharmacology as a potential solution for complex disorders. These and other similar studies applying molecular topology suggest that some therapeutic categories may present a 'topological pattern' that goes beyond a specific mechanism of action.

Benzimidazole-1,2,3-triazole hybrid molecules: synthesis and evaluation for antibacterial/antifungal activity

A novel series of hybrid molecules 4a-i and 5a-i were prepared by condensation of 4-(trimethylsilylethynyl)benzaldehyde 1 with substituted o-phenylenediamines. These in turn were reacted with 2-(azidomethoxy)ethyl acetate in a Cu alkyne-azide cycloaddition (CuAAC) to generate the 1,2,3-triazole pharmacophore under microwave assistance. The newly synthesized compounds were examined for their in vitro antimicrobial activities against Gram-positive and Gram-negative bacteria and the phytopathogenic fungi Verticillium dahliae and Fusarium oxysporum f. sp. albedinis. 2-((4-(4-(5-Trifluoromethyl benzimidazol-2-yl)phenyl)-1,2,3-triazol-1-yl)methoxy)ethanol 5e showed a moderate inhibition of 30% in the Foa sporulation test.

QSAR of heterocyclic antifungal agents by flip regression

QSAR analysis of a set of 96 heterocyclics with antifungal activity was performed. The results reveals that a pyridine ring is more favorable than benzene as the 6-membered ring, for high activity, but thiazole is unfavorable as the 5-membered ring relative to imidazole or oxazole. Methylene is the spacer leading to the highest activity. The descriptors used are indicator variables, which account for identity of substituent, lipophilicity and volume of substituent, and total polarizability. Unlike previously reported results for this data set, our fits do not exceed the limitations set by the nature of the data itself.

QSAR analysis for heterocyclic antifungals

We perform linear regression analyses on 1202 numerical descriptors that encode the various aspects of the topological, geometrical and electronic molecular structure with the aim of achieving the best QSAR relationship between the antifungal potencies against the Candida albicans strain and the structure of 96 heterocyclic ring derivatives. As a realistic application we employ the model found to predict the biological activity for 60 non-yet measured compounds.

Linear and nonlinear modeling of antifungal activity of some heterocyclic ring derivatives using multiple linear regression and Bayesian-regularized neural networks

Antifungal activity was modeled for a set of 96 heterocyclic ring derivatives (2,5,6-trisubstituted benzoxazoles, 2,5-disubstituted benzimidazoles, 2-substituted benzothiazoles and 2-substituted oxazolo(4,5-b)pyridines) using multiple linear regression (MLR) and Bayesian-regularized artificial neural network (BRANN) techniques. Inhibitory activity against Candida albicans (log(1/C)) was correlated with 3D descriptors encoding the chemical structures of the heterocyclic compounds. Training and test sets were chosen by means of k-Means Clustering. The most appropriate variables for linear and nonlinear modeling were selected using a genetic algorithm (GA) approach. In addition to the MLR equation (MLR-GA), two nonlinear models were built, model BRANN employing the linear variable subset and an optimum model BRANN-GA obtained by a hybrid method that combined BRANN and GA approaches (BRANN-GA). The linear model fit the training set (n = 80) with r2 = 0.746, while BRANN and BRANN-GA gave higher values of r2 = 0.889 and r2 = 0.937, respectively. Beyond the improvement of training set fitting, the BRANN-GA model was superior to the others by being able to describe 87% of test set (n = 16) variance in comparison with 78 and 81% the MLR-GA and BRANN models, respectively. Our quantitative structure-activity relationship study suggests that the distributions of atomic mass, volume and polarizability have relevant relationships with the antifungal potency of the compounds studied. Furthermore, the ability of the six variables selected nonlinearly to differentiate the data was demonstrated when the total data set was well distributed in a Kohonen self-organizing neural network (KNN).

Getting new bronchodilator compounds from molecular topology

Molecular topology has been used to select new lead bronchodilator compounds. The main advantage of this method, as compared to others frequently used, is that it does not require a previous explicit knowledge of the mechanism of action (MOA) of the compounds analyzed. A large database (12,000 chemicals) has been examined in this study to find less than 5% compounds with bronchodilator activity. After removing those compounds already described as bronchodilators, we present here the results for 20 among these compounds, some of them showing other pharmacological activities. Some of the compounds selected in this study showed higher relaxation and higher potency than theophylline, which is the reference drug used in the bronchodilator assay performed. For instance, tetrahydro-papaveroline showed significantly higher values than theophylline (93.9% versus 77.0% and pD2=7.30 versus pD2=4.69, respectively). Other compounds, although eliciting small or no relaxation at 0.1mM, produced larger relaxation at higher concentrations (1mM). In conclusion, the molecular topology based approach used in this work has demonstrated to be effective in the search of new bronchodilators.