Molecular Dynamics in the Study and Development of Molecularly Imprinted Materials – Status Quo, Quo Vadis?

Abstract

The past two decades have witnessed the introduction of and then a steady increase in the use of computational techniques in the study and development of molecularly imprinted polymers (MIPs). Molecular dynamics (MD) based studies have had a significant role in this development as they can provide insights concerning the mechanisms governing the molecular level events underlying MIP synthesis and MIP-ligand interactions and can be used for the identification of preferred monomer compositions and for the prediction of MIP properties. We here review the role that MD has played in the development of molecular imprinting and examine the different types of MD strategies that have been used, including their advantages and challenges. Recent trends in the application of MD to the study of MIPs are presented, along with a perspective on the future importance of MD-based studies for the development of molecular imprinting science and technology.

Design of Molecularly Imprinted Polymers Using Supercritical Carbon Dioxide Technology

“…The computational design of affinity materials, such as MIPs, is mainly focused on their molecular interactions to understand their unique structure–derived properties using approaches such as ab initio methods, force–field techniques, and machine learning (ML), establishing structure–property relationships in the design of innovative materials and enhancing their performance. Most of the computational approaches use the following: (1) quantum mechanics (QMs) or molecular mechanics (MMs) to quantify the interaction between the receptor and the ligand; (2) structure–based virtual screening to evaluate different ligands and select the most promising ones; and (3) molecular dynamics to simulate multicomponent systems, taking time and the dynamic effects into consideration [ 48 , 49 , 50 ]. In recent years, the design of experiments (DOE) has been left aside due to time–consuming laboratory work; however, the use of DOE incorporated into big data analysis allied to ML has been described as the next scientific paradigm in materials design [ 51 ].…”