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