De Novo Molecular Design using a Graph-Based Genetic Algorithm Approach

摘要

The area of computer-aided molecular design has greatly influenced the rate and cost at which novel chemicals with desired attributes have been identified. As such, great effort has been invested in new methodologies which allow for the solution of larger and more complex problems of this nature. 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. In addition, it has been shown that many molecular properties or attributes are often best characterized by a combination of descriptors with varying dimensionality. The inverse solution to property models of this nature, which entails identifying candidate molecular structures with the desired properties as defined by the given model, is often highly non-linear in nature. In addition, the use of molecular fragments, as often practiced in the de novo design of novel structures, can lead to a combinatorially large search space which becomes intractable for exhaustive solution techniques. The application of GAs provides a powerful method for the solution of these types of molecular design problems in which there are often multiple objective constraints with high computational complexity and a large search space. This approach 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 this algorithm.