A genetic algorithm for the automated generation of small organic molecules: Drug design using an evolutionary algorithm

Rational drug design involves finding solutions to large combinatorial problems for which an exhaustive search is impractical. Genetic algorithms provide a novel tool for the investigation of such problems. These are a class of algorithms that mimic some of the major characteristics of Darwinian evolution. LEA has been designed in order to conceive novel small organic molecules which satisfy quantitative structure-activity relationship based rules (fitness). The fitness consists of a sum of constraints that are range properties. The algorithm takes an initial set of fragments and iteratively improves them by means of crossover and mutation operators that are related to those involved in Darwinian evolution. The basis of the algorithm, its implementation and parameterization, are described together with an application in de novo molecular design of new retinoids. The results may be promising for chemical synthesis and show that this tool may find extensive applications in de novo drug design projects.     

G-SIMS and SMILES: Simulated fragmentation pathways for identification of complex molecules, amino acids and peptides

G-SIMS is a powerful method for the identification of organics and complex molecules at surfaces. We have previously shown that the molecular structure may be reassembled from fragment ions by studying the evolution of G-SIMS intensities as the surface plasma, with effective temperature T_p, is varied, using a method known as G-SIMS-FPM.Here, we develop a novel approach, based on SMILES (Simplified Molecular Input Line Entry Specification), to assist the reassembly process in an automated way through evaluation of the fragmentation pathways for given molecular structures. A computer program takes a parent structure and goes through every possible fragmentation to provide a tree structure of fragmentation products and simulated fragmentation pathways. For any fragment it is then possible to identify the molecular structure, its mass and a pathway to the parent. We find that there is a good correlation with peak evolution in G-SIMS-FPM data and simulated pathways for two amino acids and a simple peptide. This significantly enhances the application of G-SIMS-FPM to unknown materials.