Design of Linear Ligands for Selective Separation Using a Genetic Algorithm Applied to Molecular Architecture

Continuous purification of chemical reaction products through adsorption-based operations during workup may present advantages over batch chromatography or crystallization. In pharmaceutical syntheses, however, the desired product is often structurally similar to byproducts or unconverted reactant, so that identifying a suitable adsorption medium is challenging. We developed an in silico screening process to design organic ligands which, when chemically bound to a solid surface, would constitute an effective adsorption for a pharmaceutically relevant mixture of reaction products. This procedure employs automated molecular dynamics simulations to evaluate potential ligands, by measuring the difference in adsorption energy of two solutes which differed by one functional group. Then, a genetic algorithm was used to iteratively improve a population of such ligands through selection and reproduction steps. This procedure identified chemical designs of the surface-bound ligands that were outside the set we considered using chemical intuition. The ligand designs achieved selectivity by exploiting phenyl–phenyl stacking which was sterically hindered in the case of one solution component. The ligand designs had selectivity energies of 0.8–1.6 kcal/mol in single-ligand, solvent-free simulations, if entropic contributions to the relative selectivity are neglected. We believe this molecular evolution technique presents a useful method for the directed exploration of chemical space or for molecular design, when the chemical properties of interest can be efficiently evaluated through simulations