LigEvolver: A Tool for ligand formulation using genetic algorithm

Human Immunodeficiency Virus type 1 (HIV - 1) has been found to be the cause of Acquired Immune Deficiency Syndrome (AIDS). This virus caused uproar during the 1980\u27s and started a major drug discovery race among the pharmaceutical companies. It also proved to be a milestone in the establishment of the use of computers in rational drug design. Evolutionary computation is a commonly used computational approach that has been successfully applied to a variety of fields ranging from engineering to life science. The main reason for its effectiveness is that it is driven by the principles of evolution. A genetic algorithm is an approach to evolutionary computation that allows random combination of data to occur in a series of generations and enables the identification of novel systems that might otherwise have gone undetected. This work explores the use of genetic algorithms to generate new ligand structures that may be effective in inhibiting HIV - 1 Protease, one of the major drug targets in HIV. One of the computational challenges associated with drug discovery is the conversion of chemical and biological entities into formats that the computer can use. Chemical structures can be represented by linear character strings called SMILES strings. SMILES (Simplified Molecular Input Line Entry Specification) strings are taken as the genetic representation for our approach to drug discovery, and a genetic algorithm has been developed to generate appropriate ligands for HIV-1 protease. Based on a fitness function, the ligands are evaluated and either kept or removed from the gene pool following the survival of the fittest pattern found in nature