F.J. García-March

Discrimination and selection of new potential antibacterial compounds using simple topological descriptors

The aim of the work was to discriminate between antibacterial and non-antibacterial drugs by topological methods and to select new potential antibacterial agents from among new structures. The method used for antibacterial activity selection was a linear discriminant analysis (LDA). It is possible to obtain a QSAR interpretation of the information contained in the discriminant function. We make use of the pharmacological distribution diagrams (PDDs) as a visualizing technique for the identification and selection of new antibacterial agents.

Use of topological descriptiors in chromatographic chiral separations

Abstract Studies of enantiomeric separations are reported, specifically the direct chromatographic separation of enantiomers using a chiral stationary phase by molecular topology. The results obtained show good correlation equations for the capacity factor, k ′, and the separation factor, α, for different set of compounds (hydantoins, aromatic α-amino acids and arylamides). Such equations may be useful for the selection of the optimum stationary and mobile phases for the separation of enantiomers. Futher, the correlation between topological descriptors and performance in chiral separations opens up a new approach to the design of chiral stationary phases.

Artificial neural networks and linear discriminant analysis: a valuable combination in the selection of new antibacterial compounds.

A set of topological descriptors has been used to discriminate between antibacterial and nonantibacterial drugs. Topological descriptors are simple integers calculated from the molecular structure represented in SMILES format. The methods used for antibacterial activity discrimination were linear discriminant analysis (LDA) and artificial neural networks of a multilayer perceptron (MLP) type. The following plot frequency distribution diagrams were used: a function of the number of drugs within a value interval of the discriminant function and the output value of the neural network versus these values. Pharmacological distribution diagrams (PDD) were used as a visualizing technique for the identification of antibacterial agents. The results confirmed the discriminative capacity of the topological descriptors proposed. The combined use of LDA and MLP in the guided search and the selection of new structures with theoretical antibacterial activity proved highly effective, as shown by the in vitro activity and toxicity assays conducted.

Drugs and nondrugs: an effective discrimination with topological methods and artificial neural networks.

A set of topological and structural descriptors has been used to discriminate general pharmacological activity. To that end, we selected a group of molecules with proven pharmacological activity including different therapeutic categories, and another molecule group without any activity. As a method for pharmacological activity discrimination, an artificial neural network was used, dividing molecules into active and inactive, to train the network and externally validate it. The following plot frequency distribution diagrams were used: a function of the number of drugs within a value interval, and the output value of the neural network versus these values. Pharmacological distribution diagrams (PDD) were used as a visualizing technique for the identification of drug and nondrug molecules. The results confirmed the discriminative capacity of the topological descriptors proposed.

Artificial neural network applied to the discrimination of antibacterial activity by topological methods

Abstract A new topological method that makes it possible to discriminate the active and inactive molecules on the basis of their chemical structures is applied in the present study to the antibacterial agents. This method uses neural networks in which training algorithms are used as well as different concepts and methods of artificial intelligence with a suitable set of topological descriptors. It is possible to obtain a QSAR interpretation of the information contained in the network after the training has been carried out.

Prediction of chromatographic properties for a group of natural phenolic derivatives by molecular topology

A study was made of the relationship between the RM values obtained by thin-layer chromatography for a group of phenols and connectivity indices proposed by Kier and Hall. By using multivariate regression the corresponding connectivity functions were obtained, which were selected based on their respective statistical parameters. Regression analysis of the connectivity functions showed a correct prediction of the experimental elution sequence for this group of molecules using silica gel stationary phases and mobile phases of different polarity. Random and stability studies of the different prediction models selected were carried out, and good stability and null randomness were obtained in all cases.

Calculation of chromatographic parameters by molecular topology : sulphamides

This investigation was undertaken to test the ability of the molecular connectivity model to predict RF values in thin-layer chromatography (TLC) for a group of sulphamides using multi-variable regression equations with multiple correlation coefficients, standard error of estimate, F-Snedecor function values and Students t-test as criteria of fit. Regression analyses showed that the molecular connectivity model predicts the values for this property in different silica gel stationary phases and different polar mobile phases. Corresponding stability and random studies were made on the selected prediction models which confirmed their goodness of fit. The results also demonstrated that different structural features determine the RF values in TLC of sulphamides.

Discrimination and molecular design of new theoretical hypolipaemic agents using the molecular connectivity functions

The molecular topology model and discriminant analysis have been applied to the prediction and QSAR interpretation of some pharmacological properties of hypolipaemic drugs using multivariable regression equations with their statistical parameters. Regression analysis showed that the molecular topology model predicts these properties. The corresponding stability (cross-validation) studies done on the selected prediction models confirmed the goodness of the fits. The method used for hypolipaemic activity selection was a linear discriminant analysis (LDA). We make use of the pharmacological distribution diagrams (PDDs) as a visualizing technique for the identification and design of new hypolipaemic agents.

Pharmacological Studies of 1‐(p‐Chlorophenyl)propanol and 2‐(1‐Hydroxy‐3‐butenyl)phenol: Two New Non‐narcotic Analgesics Designed by Molecular Connectivity

Molecular topology has been applied to the design of new analgesic drugs. Linear discriminant analysis and connectivity functions were used to design two potentially suitable drugs which were synthesized and tested for analgesic properties by the acetic acid‐induced abdominal constriction test in mice and the tail‐flick test in rats.

Prediction of chromatographic parameters for some anilines by molecular connectivity

SummaryThe possible relation existing between RF values obtained by thin-layer chromatography for a group of anilines with connectivity indices proposed by Kier and Hall has been studied. Using multivariable regression the corresponding connectivity functions, selected for their respective correlation coefficients, standard deviations, Snedecor’s F and Student’s t were obtained. Regression analysis of the connectivity functions gives a correct prediction of the experimental elution sequence for this group of substances on silica gel stationary phases and various mobile phases of different polarity. The corresponding random and stability studies of the different prediction models selected were carried out, showing good stability and null randomness in all cases.