Genetic Algorithm Parametrization for Informed Exploration of Short Peptides Chemical Space

Chemical space is vast and its exploration by hand-picking chemical compounds with desirable features can appear slow and demanding. With the advancement in the field of search based algorithms such exploration can be rationally controlled. Our goal was to construct a genetic algorithm that searches through chemical space of short peptides in intelligent and fast manner. We achieved our goal by adapting the multi-objective NSGA-II algorithm through the implementation of the early stopping criterion and mitigation of possible memory issues by using simulated annealing. The contribution of this paper is the design strategy for multiple peptide libraries that cover greater area of search space in exploration of new active peptides.