Efficient prediction of suitable functional monomers for molecular imprinting via local density of states calculations

Abstract

Synthetic molecular recognition materials, such as molecularly imprinted polymers (MIPs), are of increasing importance in biotechnology and analytical chemistry, as they are able to selectively bind their respective template. However, due to their specificity, each MIP has to be individually designed for the desired target leading to a molecularly tailored synthesis strategy. While trial-and-error remains the common approach for selecting suitable functional monomers (FMs), the study herein introduces a radically new approach towards rationally designing MIPs by rapidly screening suitable functional monomers based on local density of states (LDOS) calculations in a technique known as Electronic Indices Methodology (EIM). An EIM-based method of classification of FMs according to their suitability for imprinting was developed. Starting from a training set of nine different functional monomers, the prediction of suitability of four functional monomers was possible. These predictions were subsequently experimentally confirmed.