Evolutionary algorithm for de novo molecular design with multi-dimensional constraints

• An evolutionary algorithm for molecular design has been developed.
• This approach allows for consideration of descriptors with varying dimensionality.
• Genetic operators are developed to act on graphically represented molecules.
• The spatial descriptors can be estimated with fragments geometries.
• The overall fitness was improved through implementation of designed operators.

An evolutionary approach for solving molecular design problems with descriptors of varying dimensionality has been developed. Spatial fragment based descriptors are employed to generate candidate solutions within a population, which is evolved through the application of genetic operators toward an improved fitness. The candidate molecules are represented as graphs, and as such, customized operators of crossover and mutation have been developed to be compatible with this representation. The search space is conveniently represented through limitations on the occurrence of each fragment, as defined by the chosen data set, and the spatial capabilities of this space are captured through an initial conformational analysis. This spatial information is compressed and utilized to generate conformational space estimations throughout the algorithm, which expedites the search for solution graphs. The effect of various user determined input parameters is considered and exemplified through a case study involving the identification of solvents falling within a desired boiling point range, as estimated by a multi-dimensional property model.