Antibiotic Molecular Design Using Artificial Bee Colony Algorithm

Abstract

Research is acutely needed to develop novel therapies to treat resistant infections. This project aims to design a drug molecule via a computer aided molecular design approach to provide lead candidates for the treatment of bacterial infections caused by Staphylococcus aureus. In a recently published WHO report, a list of bacteria which pose the greatest threat to human health was given. The purpose of this report was to identify the most important resistant bacteria at global level for which immediate treatment is required. Staphylococcus aureus, which is on this list, is a pathogen causing infections such as pneumonia and bone disorders. A methodology which determines the structures of candidate antibiotic molecules is described. The Artificial Bee Colony algorithm has been used for the first time for molecular design in this work. It is necessary to predict physical and/or biological properties of compounds in order to design them. The prediction of properties is performed using Quantitative Structure Property Relationships (QSPRs). QSPRs are equations, which are developed using reported data for properties of interest by the method of regression analysis. This work applies connectivity indices and 3D MoRSE descriptors to develop QSPRs. The properties used in this work are minimum inhibitory concentration and Log P values. 3D MoRSE descriptors have been used for the first time for molecular design in this work. The QSPRs are combined with structural feasibility and connectivity constraints to formulate an optimization problem, which is a mixed integer nonlinear program (MINLP). Because of the large number of potential chemical structures and the uncertainty in the structure-property correlations, stochastic algorithms are preferred to solve the resulting MINLP. One stochastic algorithm which has shown promise to solve these problems is the Artificial Bee Colony algorithm, which relies on principles of swarm intelligence to find near-optimal solutions efficiently. The Artificial Bee Colony algorithm described in this work is used to derive solutions which serve as lead compounds for a narrowed search for novel antibiotics. Results show that the ABC algorithm is very effective in finding near optimal solutions to the MINLP, which is a combinatorial optimization problem. Molecular structures were obtained by optimizing objective function for individual property values and simultaneously for both the properties.