Recent Progress of Molecular Docking Simulations Applied to Development of Drugs

In order to obtain structural information about intermolecular interactions between a protein target and a drug we could either solve the structure by experimental techniques (protein crystallography or nuclear magnetic resonance), or simulate the protein-drug complex computationally. Molecular docking is a computer simulation methodology that can predict the conformation of a protein-drug complex, with relatively high accuracy when compared with experimental structures. Although a plethora of algorithms has been applied to the problem of molecular docking simulation, recent results show that the most successful approaches are those based on evolutionary algorithms. Evolution as a source of inspiration has been shown to have a great positive impact on the progress of new computational methodologies. In this scenario, analyses of the interactions between a protein target and a drug can be simulated by these evolutionary algorithms. These algorithms mimic evolution to create new paradigms for computation. This review provides a description of evolutionary algorithms and applications to molecular docking simulation. Special attention is dedicated to differential evolutionary algorithm and its implementation in the program molegro virtual docker. Recent applications of these methodologies to protein targets such as acetylcholinesterese, cyclin-dependent kinase 2, purine nucleoside phosphorylase, and shikimate kinase are described.