Search Strategies for Applied Molecular Evolution

A new approach to drug discovery is based on the generation of high-diversity libraries of DNA, RNA, peptides or small molecules. Search of such libraries for useful molecules is an optimization problem on high-dimensional molecular fitness landscapes. We utilize a spin-glass-like model, the NK model, to analyze search strategies based on pooling, mutation, recombination, and selective hill-climbing. Our results suggest that pooling followed by recombination and/or hill-climbing finds better candidate molecules than pooling alone on most molecular landscapes. Our results point to new experiments to assess the structure of molecular fitness landscapes and improve current models.