Graph-Based Genetic Algorithm for De Novo Molecular Design

The field of computer-aided molecular design has benefited from the introduction of many new methodologies made possible through improvements in computing capabilities and techniques for handling large amounts of data. These advances in computational capabilities have ushered in an array of improved molecular simulation techniques as well as ways to characterize these phenomena. The increase in useful descriptors, capturing more detailed information than ever before, has allowed for more flexibility in developing structure-property and activity relationships. Coupled with powerful variable selection techniques, increasingly accurate and predictive models are being generated. This requires a paralleled increase in methodologies useful for utilization of these models, often containing descriptors of widely varying dimensionality, in a predictive, or inverse, manner. The application of genetic algorithms (GAs) is one such technique which has shown promise in the solution of large combinatorial, and highly non-linear molecular design problems. The approach presented here utilizes a fragment based descriptor known as the Signature descriptor, which is represented as a molecular graph, as building blocks to generate candidate solutions. The graph-based genetic operators necessary for such an approach will be outlined as well as exemplified through a case study which will highlight the advantages of the algorithm.