Identifying Potential Machine Learning Algorithms for the Simulation of Binding Affinities to Molecularly Imprinted Polymers

Abstract

Molecularly imprinted polymers (MIPs) are synthetic receptors engineered towards the selective binding of a target molecule; however, the manner in which MIPs interact with other molecules is of great importance. Being able to rapidly analyze the binding of potential molecular interferences and determine the selectivity of a MIP can be a long tedious task, being time- and resource-intensive. Identifying computational models capable of reliably predicting and reporting the binding of molecular species is therefore of immense value in both a research and commercial setting. This research therefore sets focus on comparing the use of machine learning algorithms (multitask regressor, graph convolution, weave model, DAG model, and inception) to predict the binding of various molecular species to a MIP designed towards 2-methoxphenidine. To this end, each algorithm was “trained” with an experimental dataset, teaching the algorithms the structures and binding affinities of various molecular species at varying concentrations. A validation experiment was then conducted for each algorithm, comparing experimental values to predicted values and facilitating the assessment of each approach by a direct comparison of the metrics. The research culminates in the construction of binding isotherms for each species, directly comparing experimental vs. predicted values and identifying the approach that best emulates the real-world data.

Rational In Silico Design of Molecularly Imprinted Polymers: Current Challenges and Future Potential

“…Furthermore, ML can enable prediction of characteristics of simulated polymers. Correspondingly, a recent study by Lowdon and coworkers tested ML algorithms for the assessment of binding affinities of MIPs to various molecular species [ 154 ]. Several algorithms were provided and applied to available experimental data to train the algorithms on the structures and binding affinities of various molecular species at varying concentrations.…”

A Molecularly Imprinted Polymer-Based Thermal Sensor for the Selective Detection of Melamine in Milk Samples

“…MIPs are synthetic receptors capable of selectively binding to a target through a “lock and key” mechanism and, as such, are considered synthetic substitutes of natural antibodies [ 21 ]. MIPs have been extensively studied in the last decades, and their high application potential in food safety and clinical diagnostics [ 22 , 23 , 24 , 25 ] is primarily due to the advantages over their natural counterparts in terms of chemical and environmental stability, straightforward preparation, and cost-effectiveness [ 26 , 27 ]. These features make MIPs ideal candidates for the recognition of different molecules and biomarkers in complex matrices [ 28 , 29 , 30 ].…”