Topological Model for the Search of New Antibacterial Drugs. 158 Theoretical Candidates

Abstract

In this paper, molecular topology was used to develop a mathematical model capable of classifying compounds according to their antibacterial activity. Topological indices were used as structural descriptors and their relation to antibacterial activity was determined by applying linear discriminant analysis (LDA) on a group of quinolones, widely used nowadays because of their broad spectrum of activity, well tolerance profile and advantageous pharmacokinetic properties. The topological model of activity obtained included two discriminant functions, selected by a combination of various statistical paremeters such as Fisher-Snedecor F and Wilk's lambda, and allows the reliable prediction of antibacterial activity in any organic compound. After a virtual pharmacological screening on a library of 6375 compounds, the model has selected 263 as active compounds, from which 40% have proven antibacterial activity. The results obtained clearly reveal the high efficiency of molecular topology for the prediction of pharmacological activities. These models are very helpful in the discovery of new applications of natural and synthetic molecules with different chemical or biological properties. Therefore, we finally present 158 strong candidates to be developed as novel antibacterials.

Molecular Topology for the Discovery of New Broad-Spectrum Antibacterial Drugs

“…To obtain the discriminant functions we used the data from a previous study [ 19 ], in which the statistical program BMDP randomly formed two training groups with 38 active and 38 inactive compounds and two test groups with 12 active and 11 inactive compounds. This test groups allowed evaluating the quality of the selected discriminant functions.…”

Progress and future of the computational design of antimicrobial peptides (AMPs): bio-inspired functional molecules

“…QSAR methods have been widely used in discovering and growing different libraries for new antibacterial agents. [52][53][54][55] Various linear and nonlinear statistical methods are used to develop these models based on the 2D or 3D representations of molecules. As a result of its simplicity, transparency, reproducibility, and ease of interpretation, multiple linear regression (MLR) is oen used to obtain QSAR models.…”

Quantitative structure–activity relationship methods in the discovery and development of antibacterials

“…with minimum inhibitory concentration (MIC) values between 16 and 31 mg/L. Similarly, Bueso-Bordils et al 45 developed a topological model using LDA to predict general antibacterial activity. This model was applied to a virtual library of 6,375 compounds, having selected 263 as theoretically active.…”

“…The fact that QSAR models provide a fast route for drug discovery has led many research groups to use these techniques in order to find new active drugs against multiresistant bacteria causing nosocomial infections in the hospital setting. [45][46][47] Speck-Planche et al have focused their work in developing multitask models to establish relationships between the chemical structure of a compounds and its biological effect (mtk-QSBER: multitasking Quantitative Structure-Biological Effect Relationship). 46,47 They developed two models, one targeting active compounds against Escherichia coli 46 and another one targeting active compounds against Pseudomonas spp.…”

“…In this same work, the model was also applied to 263 theoretical antibacterial agents, 45 of which 34 were selected as theoretically active against MRSA. Nine compounds were bibliographically verified to be correctly selected, of the 25 remaining, 3 were randomly selected for assays.…”