Artificial Neural Networks and Linear Discriminant Analysis:  A Valuable Combination in the Selection of New Antibacterial Compounds

Abstract

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.

Computational Methods in Developing Quantitative Structure-Activity Relationships (QSAR): A Review

“…In predicting antibacterial activity [118,119], it performed worse than neural network. LDA was also used to predict drug likeness [120], showing results slightly better than linear programming machine, a method similar to linear SVM.…”

“…However, single MLP networks have been shown inferior to ensembles of such networks in prediction of antifilarial activity, GABA A receptor binding and inhibition of dihydrofolate reductase [129]. In an antibacterial activity study [118], MLP performed better than LDA. This type of networks has also been applied to prediction of logP [104], faring better than linear regression and comparable to decision trees.…”

An Empirical Evaluation of Common Vector Based Classification Methods and Some Extensions

“…The second dataset corresponds to a particular problem in the domain of classification of chemical compounds from their topological and structural description [10]. In particular, a dataset of 434 samples consisting of 62 molecular descriptors computed from the graph corresponding to its chemical formula is considered.…”

Compound Selection and Filtering in Library Design

“…Focusing a set of compounds by property-based selection can also be applied to defined therapeutic areas or even specific compound classes. In example, artificial neural networks and linear discriminant analysis have been used successfully to discriminate between antibacterial and non-antibacterial compounds using topological descriptors [31] as well as predict the Minimal Inhibitory Concentration (MIC) of fluoro-quinolone antibiotics [32]. These few examples all highlight the differing methodologies for selecting a set of compounds with focused characteristics, potentially increasing the chance of success against a given target or therapeutic area.…”